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https://www.readbyqxmd.com/read/28727850/hybrid-cubature-kalman-filtering-for-identifying-nonlinear-models-from-sampled-recording-estimation-of-neuronal-dynamics
#1
Mahmoud K Madi, Fadi N Karameh
Kalman filtering methods have long been regarded as efficient adaptive Bayesian techniques for estimating hidden states in models of linear dynamical systems under Gaussian uncertainty. Recent advents of the Cubature Kalman filter (CKF) have extended this efficient estimation property to nonlinear systems, and also to hybrid nonlinear problems where by the processes are continuous and the observations are discrete (continuous-discrete CD-CKF). Employing CKF techniques, therefore, carries high promise for modeling many biological phenomena where the underlying processes exhibit inherently nonlinear, continuous, and noisy dynamics and the associated measurements are uncertain and time-sampled...
2017: PloS One
https://www.readbyqxmd.com/read/28723578/convolutional-neural-network-based-encoding-and-decoding-of-visual-object-recognition-in-space-and-time
#2
REVIEW
K Seeliger, M Fritsche, U Güçlü, S Schoenmakers, J-M Schoffelen, S E Bosch, M A J van Gerven
Representations learned by deep convolutional neural networks (CNNs) for object recognition are a widely investigated model of the processing hierarchy in the human visual system. Using functional magnetic resonance imaging, CNN representations of visual stimuli have previously been shown to correspond to processing stages in the ventral and dorsal streams of the visual system. Whether this correspondence between models and brain signals also holds for activity acquired at high temporal resolution has been explored less exhaustively...
July 16, 2017: NeuroImage
https://www.readbyqxmd.com/read/28716961/the-structural-basis-of-large-scale-functional-connectivity-in-the-mouse
#3
Joanes Grandjean, Valerio Zerbi, Joshua Balsters, Nicole Wenderoth, Markus Rudina
Translational neuroimaging requires approaches and techniques that can bridge between multiple different species and disease states. One candidate method, which offers insights into the brain's functional connectivity (FC), is resting state fMRI (rs-fMRI). In both humans and non-human primates, patterns of functional connectivity (often referred to as the functional connectome) have been related to the underlying structural connectivity (structural connectome). Given the recent rise in pre-clinical neuroimaging of mouse models it is an important question whether the mouse functional connectome conforms to the underlying structural connectivity...
July 17, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/28707570/using-event-related-potentials-to-inform-the-neurocognitive-processes-underlying-knowledge-extension-through-memory-integration
#4
Nicole L Varga, Patricia J Bauer
To build a general knowledge base, it is imperative that individuals acquire, integrate, and further extend knowledge across experiences. For instance, in one episode an individual may learn that George Washington was the first president. In a separate episode he or she may then learn that Washington was the commander of the Continental Army. Integration of the information in memory may then support self-derivation of the new knowledge that the leader of the Continental Army was also the first president. Despite a considerable amount of fMRI research aimed at further elucidating the neuroanatomical regions supporting this ability, a consensus has yet to be reached with regards to the precise neurocognitive processes involved...
July 14, 2017: Journal of Cognitive Neuroscience
https://www.readbyqxmd.com/read/28690513/feature-selection-methods-for-zero-shot-learning-of-neural-activity
#5
Carlos A Caceres, Matthew J Roos, Kyle M Rupp, Griffin Milsap, Nathan E Crone, Michael E Wolmetz, Christopher R Ratto
Dimensionality poses a serious challenge when making predictions from human neuroimaging data. Across imaging modalities, large pools of potential neural features (e.g., responses from particular voxels, electrodes, and temporal windows) have to be related to typically limited sets of stimuli and samples. In recent years, zero-shot prediction models have been introduced for mapping between neural signals and semantic attributes, which allows for classification of stimulus classes not explicitly included in the training set...
2017: Frontiers in Neuroinformatics
https://www.readbyqxmd.com/read/28687518/neural-inhibition-can-explain-negative-bold-responses-a-mechanistic-modelling-and-fmri-study
#6
S Sten, K Lundengård, S T Witt, G Cedersund, F Elinder, M Engström
Functional magnetic resonance imaging (fMRI) of hemodynamic changes captured in the blood oxygen level-dependent (BOLD) response contains information of brain activity. The BOLD response is the result of a complex neurovascular coupling and comes in at least two fundamentally different forms: a positive and a negative deflection. Because of the complexity of the signaling, mathematical modelling can provide vital help in the data analysis. For the positive BOLD response, there are plenty of mathematical models, both physiological and phenomenological...
July 4, 2017: NeuroImage
https://www.readbyqxmd.com/read/28682248/fused-estimation-of-sparse-connectivity-patterns-from-rest-fmri-application-to-comparison-of-children-and-adult-brains
#7
Pascal Zille, Vince D Calhoun, Julia M Stephen, Tony W Wilson, Yu-Ping Wang
In this work, we consider the problem of estimating multiple sparse, co-activated brain regions from functional magnetic resonance imaging (fMRI) observations belonging to different classes. More precisely, we propose a method to analyze similarities and differences in functional connectivity between children and young adults. Often, analysis is conducted on each class separately, and differences across classes are identified with an additional postprocessing step using adequate statistical tools. Here, we propose to rely on a generalized fused Lasso penalty, which allows us to make use of the entire dataset in order to estimate connectivity patterns that are either shared across classes, or specific to a given group...
June 29, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28680119/increased-stability-and-breakdown-of-brain-effective-connectivity-during-slow-wave-sleep-mechanistic-insights-from-whole-brain-computational-modelling
#8
Beatrice M Jobst, Rikkert Hindriks, Helmut Laufs, Enzo Tagliazucchi, Gerald Hahn, Adrián Ponce-Alvarez, Angus B A Stevner, Morten L Kringelbach, Gustavo Deco
Recent research has found that the human sleep cycle is characterised by changes in spatiotemporal patterns of brain activity. Yet, we are still missing a mechanistic explanation of the local neuronal dynamics underlying these changes. We used whole-brain computational modelling to study the differences in global brain functional connectivity and synchrony of fMRI activity in healthy humans during wakefulness and slow-wave sleep. We applied a whole-brain model based on the normal form of a supercritical Hopf bifurcation and studied the dynamical changes when adapting the bifurcation parameter for all brain nodes to best match wakefulness and slow-wave sleep...
July 5, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28678984/association-of-neural-and-emotional-impacts-of-reward-prediction-errors-with-major-depression
#9
Robb B Rutledge, Michael Moutoussis, Peter Smittenaar, Peter Zeidman, Tanja Taylor, Louise Hrynkiewicz, Jordan Lam, Nikolina Skandali, Jenifer Z Siegel, Olga T Ousdal, Gita Prabhu, Peter Dayan, Peter Fonagy, Raymond J Dolan
Importance: Major depressive disorder (MDD) is associated with deficits in representing reward prediction errors (RPEs), which are the difference between experienced and predicted reward. Reward prediction errors underlie learning of values in reinforcement learning models, are represented by phasic dopamine release, and are known to affect momentary mood. Objective: To combine functional neuroimaging, computational modeling, and smartphone-based large-scale data collection to test, in the absence of learning-related concerns, the hypothesis that depression attenuates the impact of RPEs...
July 5, 2017: JAMA Psychiatry
https://www.readbyqxmd.com/read/28678703/enforcing-co-expression-within-a-brain-imaging-genomics-regression-framework
#10
Pascal Zille, Vince D Calhoun, Yu-Ping Wang
Among the challenges arising in brain imaging genetic studies, estimating the potential links between neurological and genetic variability within a population is key. In this work, we propose a multivariate, multimodal formulation for variable selection that leverages co-expression patterns across various data modalities. Our approach is based on an intuitive combination of two widely used statistical models: sparse regression and canonical correlation analysis (CCA). While the former seeks multivariate linear relationships between a given phenotype and associated observations, the latter searches to extract co-expression patterns between sets of variables belonging to different modalities...
June 28, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28675490/the-significance-of-negative-correlations-in-brain-connectivity
#11
Liang Zhan, Lisanne M Jenkins, Ouri E Wolfson, Johnson Jonaris GadElkarim, Kevin Nocito, Paul M Thompson, Olusola A Ajilore, Moo K Chung, Alex D Leow
Understanding the modularity of functional magnetic resonance imaging (fMRI)-derived brain networks or "connectomes" can inform the study of brain function organization. However, fMRI connectomes additionally involve negative edges, which may not be optimally accounted for by existing approaches to modularity that variably threshold, binarize, or arbitrarily weight these connections. Consequently, many existing Q maximization-based modularity algorithms yield variable modular structures. Here, we present an alternative complementary approach that exploits how frequent the blood-oxygen-level-dependent (BOLD) signal correlation between two nodes is negative...
July 4, 2017: Journal of Comparative Neurology
https://www.readbyqxmd.com/read/28673442/when-habits-are-dangerous-alcohol-expectancies-and-habitual-decision-making-predict-relapse-in-alcohol-dependence
#12
Miriam Sebold, Stephan Nebe, Maria Garbusow, Matthias Guggenmos, Daniel J Schad, Anne Beck, Soeren Kuitunen-Paul, Christian Sommer, Robin Frank, Peter Neu, Ulrich S Zimmermann, Michael A Rapp, Michael N Smolka, Quentin J M Huys, Florian Schlagenhauf, Andreas Heinz
BACKGROUND: Addiction is supposedly characterized by a shift from goal-directed to habitual decision making, thus facilitating automatic drug intake. The two-step task allows distinguishing between these mechanisms by computationally modeling goal-directed and habitual behavior as model-based and model-free control. In addicted patients, decision making may also strongly depend upon drug-associated expectations. Therefore, we investigated model-based versus model-free decision making and its neural correlates as well as alcohol expectancies in alcohol-dependent patients and healthy controls and assessed treatment outcome in patients...
May 22, 2017: Biological Psychiatry
https://www.readbyqxmd.com/read/28669903/how-to-improve-parameter-estimates-in-glm-based-fmri-data-analysis-cross-validated-bayesian-model-averaging
#13
Joram Soch, Achim Pascal Meyer, John-Dylan Haynes, Carsten Allefeld
In functional magnetic resonance imaging (fMRI), model quality of general linear models (GLMs) for first-level analysis is rarely assessed. In recent work (Soch et al., 2016: "How to avoid mismodelling in GLM-based fMRI data analysis: cross-validated Bayesian model selection", NeuroImage, vol. 141, pp. 469-489; http://dx.doi.org/10.1016/j.neuroimage.2016.07.047), we have introduced cross-validated Bayesian model selection (cvBMS) to infer the best model for a group of subjects and use it to guide second-level analysis...
June 29, 2017: NeuroImage
https://www.readbyqxmd.com/read/28667892/normal-aging-and-parkinson-s-disease-are-associated-with-the-functional-decline-of-distinct-frontal-striatal-circuits
#14
Aleksandra Gruszka, Adam Hampshire, Roger A Barker, Adrian M Owen
Impaired ability to shift attention between stimuli (i.e. shifting attentional 'set') is a well-established part of the dysexecutive syndrome in Parkinson's Disease (PD), nevertheless cognitive and neural bases of this deficit remain unclear. In this study, an fMRI-optimised variant of a classic paradigm for assessing attentional control (Hampshire and Owen 2006) was used to contrast activity in dissociable executive circuits in early-stage PD patients and controls. The results demonstrated that the neural basis of the executive performance impairments in PD is accompanied by hypoactivation within the striatum, anterior cingulate cortex (vACC), and inferior frontal sulcus (IFS) regions...
June 3, 2017: Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
https://www.readbyqxmd.com/read/28666882/a-second-order-orientation-contrast-stimulus-for-population-receptive-field-based-retinotopic-mapping
#15
Funda Yildirim, Joana Carvalho, Frans W Cornelissen
Visual field or retinotopic mapping is one of the most frequently used paradigms in fMRI. It uses activity evoked by position-varying high luminance contrast visual patterns presented throughout the visual field for determining the spatial organization of cortical visual areas. While the advantage of using high luminance contrast is that it tends to drive a wide range of neural populations - thus resulting in high signal-to-noise BOLD responses - this may also be a limitation, especially for approaches that attempt to squeeze more information out of the BOLD response, such as population receptive field (pRF) mapping...
June 27, 2017: NeuroImage
https://www.readbyqxmd.com/read/28662463/meg-language-lateralization-in-partial-epilepsy-using-dspm-of-auditory-event-related-fields
#16
Manoj Raghavan, Zhimin Li, Chad Carlson, Christopher T Anderson, Jeffrey Stout, David S Sabsevitz, Sara J Swanson, Jeffrey R Binder
OBJECTIVE: Methods employed to determine hemispheric language dominance using magnetoencephalography (MEG) have differed significantly across studies in the choice of language-task, the nature of the physiological response studied, recording hardware, and source modeling methods. Our goal was to determine whether an analysis based on distributed source modeling can replicate the results of prior studies that have used dipole-modeling of event-related fields (ERFs) generated by an auditory word-recognition task to determine language dominance in patients with epilepsy...
June 26, 2017: Epilepsy & Behavior: E&B
https://www.readbyqxmd.com/read/28661940/brain-structure-and-response-to-emotional-stimuli-as-related-to-gut-microbial-profiles-in-healthy-women
#17
Kirsten Tillisch, Emeran Mayer, Arpana Gupta, Zafar Gill, Rémi Brazeilles, Boris Le Nevé, Johan E T van Hylckama Vlieg, Denis Guyonnet, Muriel Derrien, Jennifer S Labus
OBJECTIVE: Brain-gut-microbiota interactions may play an important role in human health and behavior. However, while rodent models have demonstrated effects of the gut microbiota on emotional, nociceptive and social behaviors, there is little translational human evidence to date. In this study we identify brain and behavioral characteristics of healthy women clustered by gut microbiota profiles. METHODS: Forty women supplied fecal samples for 16s rRNA profiling...
June 28, 2017: Psychosomatic Medicine
https://www.readbyqxmd.com/read/28657344/value-of-frequency-domain-resting-state-fmri-metrics-alff-falff-in-the-assessment-of-brain-tumor-induced-neurovascular-uncoupling
#18
Shruti Agarwal, Hanzhang Lu, Jay J Pillai
AIM: To explore whether the phenomenon of brain tumor-related neurovascular uncoupling (NVU) in resting state BOLD fMRI (rsfMRI) may also affect the rsfMRI frequency domain metrics ALFF (the amplitude of low-frequency fluctuation) & fALFF (fractional ALFF). METHODS: Twelve de novo brain tumor patients who underwent clinical fMRI exams including task-based fMRI (tbfMRI) and rsfMRI were included in this IRB-approved study. Each patient displayed decreased/absent tbfMRI activation in the primary ipsilesional sensorimotor cortex in the absence of a corresponding motor deficit or suboptimal task performance, consistent with NVU...
June 28, 2017: Brain Connectivity
https://www.readbyqxmd.com/read/28655564/enhanced-limbic-impaired-cortical-loop-connection-onto-the-hippocampus-of-nhe-rats-application-of-resting-state-functional-connectivity-in-a-preclinical-adhd-model
#19
F Zoratto, G M Palombelli, L A Ruocco, E Carboni, G Laviola, A G Sadile, W Adriani, R Canese
Due to a hyperfunctioning mesocorticolimbic system, Naples-High-Excitability (NHE) rats have been proposed to model for the meso-cortical variant of attention deficit/hyperactivity disorder (ADHD). Compared to Naples Random-Bred (NRB) controls, NHE rats show hyperactivity, impaired non-selective attention (Aspide et al., 1998), and impaired selective spatial attention (Ruocco et al., 2009a, 2014). Alteration in limbic functions has been proposed; however, resulting unbalance among forebrain areas has not been assessed yet...
June 26, 2017: Behavioural Brain Research
https://www.readbyqxmd.com/read/28649677/outcome-prediction-for-patient-with-high-grade-gliomas-from-brain-functional-and-structural-networks
#20
Luyan Liu, Han Zhang, Islem Rekik, Xiaobo Chen, Qian Wang, Dinggang Shen
High-grade glioma (HGG) is a lethal cancer, which is characterized by very poor prognosis. To help optimize treatment strategy, accurate preoperative prediction of HGG patient's outcome (i.e., survival time) is of great clinical value. However, there are huge individual variability of HGG, which produces a large variation in survival time, thus making prognostic prediction more challenging. Previous brain imaging-based outcome prediction studies relied only on the imaging intensity inside or slightly around the tumor, while ignoring any information that is located far away from the lesion (i...
October 2016: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
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