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model based fmri

Sebastian Schneegans, Paul M Bays
Recent imaging studies have challenged the prevailing view that working memory is mediated by sustained neural activity. Using machine learning methods to reconstruct memory content, these studies found that previously diminished representations can be restored by retrospective cueing or other forms of stimulation. These findings have been interpreted as evidence for an activity-silent working memory state that can be reactivated dependent on task demands. Here, we test the validity of this conclusion by formulating a neural process model of working memory based on sustained activity and using this model to emulate a spatial recall task with retro-cueing...
August 18, 2017: Journal of Cognitive Neuroscience
Yuanning Li, R Mark Richardson, Avniel Singh Ghuman
The lack of multivariate methods for decoding the representational content of interregional neural communication has left it difficult to know what information is represented in distributed brain circuit interactions. Here we present Multi-Connection Pattern Analysis (MCPA), which works by learning mappings between the activity patterns of the populations as a factor of the information being processed. These maps are used to predict the activity from one neural population based on the activity from the other population...
August 13, 2017: NeuroImage
Shilpa Dang, Santanu Chaudhury, Brejesh Lall, Prasun K Roy
OBJECTIVE: Effective connectivity (EC) is the methodology for determining functional-integration among the functionally-active segregated regions of the brain. By definition [1] EC is "the causal influence exerted by one neuronal group on another" which is constrained by anatomical connectivity (AC) (axonal connections). AC is necessary for EC but does not fully determine it, because synaptic communication occurs dynamically in a context-dependent fashion. Although there is a vast emerging evidence of structure-function relationship using multimodal imaging studies, till date only a few studies have done joint modelling of the two modalities: functional MRI (fMRI) and Diffusion Tensor Imaging (DTI)...
August 10, 2017: IEEE Transactions on Bio-medical Engineering
Brea Chouinard, Joanne Volden, Ivor Cribben, Jacqueline Cummine
Because of their difficulties with figurative language in conversation, it is commonly thought that individuals with autism spectrum disorder (ASD) do not understand figurative meaning. However, recent research indicates that individuals with and without ASD are similar in the first two stages of metaphor comprehension, up to and including successful generation of the figurative meaning. In the current study, we used a sentence decision task to evaluate the subsequent stage of metaphor comprehension, the selection stage, which requires suppression/inhibition of the unintended meaning as part of selecting the intended meaning...
August 9, 2017: Neuroscience
Eberhard Munz, Hardy Rolletschek, Steffen Oeltze-Jafra, Johannes Fuchs, André Guendel, Thomas Neuberger, Stefan Ortleb, Peter M Jakob, Ljudmilla Borisjuk
Germination, the process whereby a dry, quiescent seed springs to life, has been a focus of plant biologist for many years, yet the early events following water uptake, during which metabolism of the embryo is restarted, remain enigmatic. Here, the nature of the cues required for this restarting in oilseed rape (Brassica napus) seed has been investigated. A holistic in vivo approach was designed to display the link between the entry and allocation of water, metabolic events and structural changes occurring during germination...
August 11, 2017: New Phytologist
Seungkyu Nam, Dae-Shik Kim
Recent advances in functional magnetic resonance imaging (fMRI) have been used to reconstruct cognitive states based on brain activity evoked by sensory or cognitive stimuli. To date, such decoding paradigms were mostly used for visual modalities. On the other hand, reconstructing functional brain activity in motor areas was primarily achieved through more invasive electrophysiological techniques. Here, we investigated whether non-invasive fMRI responses from human motor cortex can also be used to predict individual arm movements...
2017: Frontiers in Neuroscience
Junhui Gong, Xiaoyan Liu, Tianming Liu, Jiansong Zhou, Gang Sun, Juanxiu Tian
Recently, sparse representation has been successfully used to identify brain networks from task-based fMRI dataset. However, when using the strategy to analyze resting-state fMRI dataset, it is still a challenge to automatically infer the group-wise brain networks under consideration of group commonalities and subject-specific characteristics. In the paper, a novel method based on dual temporal and spatial sparse representation (DTSSR) is proposed to meet this challenge. Firstly, the brain functional networks with subject-specific characteristics are obtained via sparse representation with online dictionary learning for the fMRI time series (temporal domain) of each subject...
August 9, 2017: IEEE Transactions on Bio-medical Engineering
Yury Koush, John Ashburner, Evgeny Prilepin, Ronald Sladky, Peter Zeidman, Sergei Bibikov, Frank Scharnowski, Artem Nikonorov, Dimitri Van De Ville
Here, we briefly describe the real-time fMRI data that is provided for testing the functionality of the open-source Python/Matlab framework for neurofeedback, termed Open NeuroFeedback Training (OpenNFT, Koush et al. [1]). The data set contains real-time fMRI runs from three anonymized participants (i.e., one neurofeedback run per participant), their structural scans and pre-selected ROIs/masks/weights. The data allows for simulating the neurofeedback experiment without an MR scanner, exploring the software functionality, and measuring data processing times on the local hardware...
October 2017: Data in Brief
Ismael Huertas, Marianne Oldehinkel, Erik S B van Oort, David Garcia-Solis, Pablo Mir, Christian F Beckmann, Andre F Marquand
The dominant approach to neuroimaging data analysis employs the voxel as the unit of computation. While convenient, voxels lack biological meaning and their size is arbitrarily determined by the resolution of the image. Here, we propose a multivariate spatial model in which neuroimaging data is characterized as a linearly weighted combination of multiscale basis functions which map onto underlying brain nuclei or networks or nuclei. In this model, the elementary building blocks are derived to reflect the functional anatomy of the brain during the resting state...
August 4, 2017: NeuroImage
Dongha Lee, Sungjae Yun, Changwon Jang, Hae-Jeong Park
This study proposes a method for classifying event-related fMRI responses in a specialized setting of many known but few unknown stimuli presented in a rapid event-related design. Compared to block design fMRI signals, classification of the response to a single or a few stimulus trial(s) is not a trivial problem due to contamination by preceding events as well as the low signal-to-noise ratio. To overcome such problems, we proposed a single trial-based classification method of rapid event-related fMRI signals utilizing sparse multivariate Bayesian decoding of spatio-temporal fMRI responses...
2017: PloS One
Rasmus E Røge, Kristoffer H Madsen, Mikkel N Schmidt, Morten Mørup
Cluster analysis of functional magnetic resonance imaging (fMRI) data is often performed using gaussian mixture models, but when the time series are standardized such that the data reside on a hypersphere, this modeling assumption is questionable. The consequences of ignoring the underlying spherical manifold are rarely analyzed, in part due to the computational challenges imposed by directional statistics. In this letter, we discuss a Bayesian von Mises-Fisher (vMF) mixture model for data on the unit hypersphere and present an efficient inference procedure based on collapsed Markov chain Monte Carlo sampling...
August 4, 2017: Neural Computation
Stefanie Kübel, Katharina Stegmayer, Tim Vanbellingen, Manuela Pastore-Wapp, Manuel Bertschi, Jean-Marc Burgunder, Eugenio Abela, Bruno Weder, Sebastian Walther, Stephan Bohlhalter
Parkinson's disease (PD) patients frequently suffer from dexterous deficits impeding activities of daily living. There is controversy whether impaired fine motor skill may stem from limb kinetic apraxia (LKA) rather than bradykinesia. Based on classical models of limb praxis LKA is thought to result when premotor transmission of time-space information of skilled movements to primary motor representations is interrupted. Therefore, using functional magnetic resonance imaging (fMRI) we tested the hypothesis that dexterous deficits in PD are associated with altered activity and connectivity in left parieto-premotor praxis network...
2017: NeuroImage: Clinical
Sana Suri, Anya Topiwala, Nicola Filippini, Enikő Zsoldos, Abda Mahmood, Claire E Sexton, Archana Singh-Manoux, Mika Kivimäki, Clare E Mackay, Stephen Smith, Klaus P Ebmeier
Episodic and spatial memory are commonly impaired in ageing and Alzheimer's disease. Volumetric and task-based functional magnetic resonance imaging (fMRI) studies suggest a preferential involvement of the medial temporal lobe (MTL), particularly the hippocampus, in episodic and spatial memory processing. The present study examined how these two memory types were related in terms of their associated resting-state functional architecture. 3T multiband resting state fMRI scans from 497 participants (60-82 years old) of the cross-sectional Whitehall II Imaging sub-study were analysed using an unbiased, data-driven network-modelling technique (FSLNets)...
July 26, 2017: NeuroImage
Ethan F Oblak, Jarrod A Lewis-Peacock, James S Sulzer
Direct manipulation of brain activity can be used to investigate causal brain-behavior relationships. Current noninvasive neural stimulation techniques are too coarse to manipulate behaviors that correlate with fine-grained spatial patterns recorded by fMRI. However, these activity patterns can be manipulated by having people learn to self-regulate their own recorded neural activity. This technique, known as fMRI neurofeedback, faces challenges as many participants are unable to self-regulate. The causes of this non-responder effect are not well understood due to the cost and complexity of such investigation in the MRI scanner...
July 2017: PLoS Computational Biology
Xiaoyu Ding, Yihong Yang, Elliot A Stein, Thomas J Ross
Machine learning techniques have been applied to resting-state fMRI data to predict neurological or neuropsychiatric disease states. Existing studies have used either a single type of resting-state feature or a few feature types (<4) in the prediction model. However, resting-state data can be processed in many different ways, yielding different feature types containing complementary and/or novel information, leaving uncertain the most informative features to provide to the classifier. In this study, multiple resting-state features were calculated from two main analytical categories: local measures and network measures...
2017: Frontiers in Human Neuroscience
Rastko Ciric, Jason S Nomi, Lucina Q Uddin, Ajay B Satpute
Investigations of the human brain's connectomic architecture have produced two alternative models: one describes the brain's spatial structure in terms of static localized networks, and the other describes the brain's temporal structure in terms of dynamic whole-brain states. Here, we used tools from connectivity dynamics to develop a synthesis that bridges these models. Using resting fMRI data, we investigated the assumptions undergirding current models of the human connectome. Consistent with state-based models, our results suggest that static localized networks are superordinate approximations of underlying dynamic states...
July 26, 2017: Scientific Reports
Johan D Carlin, Nikolaus Kriegeskorte
The perceptual representation of individual faces is often explained with reference to a norm-based face space. In such spaces, individuals are encoded as vectors where identity is primarily conveyed by direction and distinctiveness by eccentricity. Here we measured human fMRI responses and psychophysical similarity judgments of individual face exemplars, which were generated as realistic 3D animations using a computer-graphics model. We developed and evaluated multiple neurobiologically plausible computational models, each of which predicts a representational distance matrix and a regional-mean activation profile for 24 face stimuli...
July 2017: PLoS Computational Biology
Mahlega S Hassanpour, Adam T Eggebrecht, Jonathan E Peelle, Joseph P Culver
Understanding how cortical networks interact in response to task demands is important both for providing insight into the brain's processing architecture and for managing neurological diseases and mental disorders. High-density diffuse optical tomography (HD-DOT) is a neuroimaging technique that offers the significant advantages of having a naturalistic, acoustically controllable environment and being compatible with metal implants, neither of which is possible with functional magnetic resonance imaging. We used HD-DOT to study the effective connectivity and assess the modulatory effects of speech intelligibility and syntactic complexity on functional connections within the cortical speech network...
October 2017: Neurophotonics
Amir Omidvarnia, Mangor Pedersen, David N Vaughan, Jennifer M Walz, David F Abbott, Andrew Zalesky, Graeme D Jackson
Simultaneous scalp EEG-fMRI recording is a noninvasive neuroimaging technique for combining electrophysiological and hemodynamic aspects of brain function. Despite the time-varying nature of both measurements, their relationship is usually considered as time-invariant. The aim of this study was to detect direct associations between scalp-recorded EEG and regional changes of hemodynamic brain connectivity in focal epilepsy through a time-frequency paradigm. To do so, we developed a voxel-wise framework that analyses wavelet coherence between dynamic regional phase synchrony (DRePS, calculated from fMRI) and band amplitude fluctuation (BAF) of a target EEG electrode with dominant interictal epileptiform discharges (IEDs)...
July 24, 2017: Human Brain Mapping
Lucia Maria Sacheli, Laura Zapparoli, Carlo De Santis, Matteo Preti, Catia Pelosi, Nicola Ursino, Alberto Zerbi, Giuseppe Banfi, Eraldo Paulesu
Gait imagery and gait observation can boost the recovery of locomotion dysfunctions; yet, a neurologically justified rationale for their clinical application is lacking as much as a direct comparison of their neural correlates. Using functional magnetic resonance imaging, we measured the neural correlates of explicit motor imagery of gait during observation of in-motion videos shot in a park with a steady cam (Virtual Walking task). In a 2 × 2 factorial design, we assessed the modulatory effect of gait observation and of foot movement execution on the neural correlates of the Virtual Walking task: in half of the trials, the participants were asked to mentally imitate a human model shown while walking along the same route (mental imitation condition); moreover, for half of all the trials, the participants also performed rhythmic ankle dorsiflexion as a proxy for stepping movements...
July 21, 2017: Human Brain Mapping
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