Subject fMRI Research Articles

95 - ​Single subject fMRI mapping at 7T during stimulation of the female urogenital region: a pilot study

95 - ​Single subject fMRI mapping at 7T during stimulation of the female urogenital region: a pilot study

A Novel Method to Identify Mild Cognitive Impairment Using Dynamic Spatio-Temporal Graph Neural Network.

Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used in the identification of mild cognitive impairment (MCI) research, MCI patients are relatively at a higher risk of progression to Alzheimer's disease (AD). However, almost machine learning and deep learning methods are rarely analyzed from the perspective of spatial structure and temporal dimension. In order to make full use of rs-fMRI data, this study constructed a dynamic spatiotemporal graph neural network model, which mainly includes three modules: temporal block, spatial block, and graph pooling block. Our proposed model can extract the BOLD signal of the subject's fMRI data and the spatial structure of functional connections between different brain regions, and improve the decision-making results of the model. In the study of AD, MCI and NC, the classification accuracy reached 83.78% outperforming previously reported, which manifested that our model could effectively learn spatiotemporal, and dynamic spatio-temporal method plays an important role in identifying different groups of subjects. In summary, this paper proposed an end-to-end dynamic spatio-temporal graph neural network model, which uses the information of the temporal dimension and spatial structure in rs-fMRI data, and achieves the improvement of the three classification performance among AD, MCI and NC.

FFTPSOGA: Fast Fourier Transform with particle swarm optimization and genetic algorithm approach for pattern identification of brain responses in multi subject fMRI data

FFTPSOGA: Fast Fourier Transform with particle swarm optimization and genetic algorithm approach for pattern identification of brain responses in multi subject fMRI data

The application of the Human Connectome Project in large scale brain network imaging: A potential biomarker for Alzheimer's disease and related dementias

AbstractBackgroundAlzheimer’s disease and related dementias (ADRD) result in progressive dysfunction of large scale brain networks (LSBNs) including the default mode network (DMN). Resting state functional MRI (rs‐fMRI) is an imaging tool offering the potential for tracking LSBN degeneration, but been underutilized as an imaging biomarker. Potential reasons include complexity of imaging protocols and analyzing imaging data. If these steps were simplified and operationalized, further integration of rs‐fMRI into clinical trials as a surrogate endpoint may be feasible. We describe an automated connectome mapping program (Infinitome) that leverages rs‐fMRI scans with standardized protocols, Human Connectome Project (HCP) data, and machine learning to understand longitudinal LSBN degeneration in dementia (figure 1).MethodThe Infinitome program creates a subject specific version of the Human Connectome Project Multimodal Parcellation (HCP‐MMP1) atlas using diffusion tractography (figure 2). Analytics are performed on both diffusion tensor imaging and rs‐fMRI. Outlier detection using a tangent space connectivity matrix is performed by comparing results with a subset of 300 normal HCP subject fMRI samples to determine the range of normal correlations for each regions of interest in a LSBN. Abnormal connectivity is determined as a 3‐sigma outlier for that correlation (figure 3). The imaging protocol for rs‐fMRI is limited to 8 minutes, enabling each study to be readily added on to structural MRI sequences. The program also provides automated image processing and analysis, thus simplifying the process of accessing imaging data and incorporating rs‐fMRI into routine practice.ResultPreliminary work has shown that connectomic analysis using this software in ADRD is feasible and can detect functional anomalies involving regions of interest described by the HCP.ConclusionBy providing support for fMRI processing through a cloud‐based server and incorporating normal control data from the HCP, the Infinitome program addresses the challenges associated with the analysis and interpretation of rs‐fMRI data. Thus, it has the potential to simplify the incorporation of rs‐fMRI into clinical trials as a biomarker for progressive neurodegeneration of vulnerable networks in ADRD. Further studies are planned evaluating LSBNs in AD and dementia with Lewy bodies.

Single subject and group whole-brain fMRI mapping of male genital sensation at 7 Tesla

Processing of genital sensations in the central nervous system of humans is still poorly understood. Current knowledge is mainly based on neuroimaging studies using electroencephalography (EEG), magneto-encephalography (MEG), and 1.5- or 3- Tesla (T) functional magnetic resonance imaging (fMRI), all of which suffer from limited spatial resolution and sensitivity, thereby relying on group analyses to reveal significant data. Here, we studied the impact of passive, yet non-arousing, tactile stimulation of the penile shaft using ultra-high field 7T fMRI. With this approach, penile stimulation evoked significant activations in distinct areas of the primary and secondary somatosensory cortices (S1 & S2), premotor cortex, insula, midcingulate gyrus, prefrontal cortex, thalamus and cerebellum, both at single subject and group level. Passive tactile stimulation of the feet, studied for control, also evoked significant activation in S1, S2, insula, thalamus and cerebellum, but predominantly, yet not exclusively, in areas that could be segregated from those associated with penile stimulation. Evaluation of the whole-brain activation patterns and connectivity analyses indicate that genital sensations following passive stimulation are, unlike those following feet stimulation, processed in both sensorimotor and affective regions.

Impact of sampling rate on statistical significance for single subject fMRI connectivity analysis.

A typical time series in functional magnetic resonance imaging (fMRI) exhibits autocorrelation, that is, the samples of the time series are dependent. In addition, temporal filtering, one of the crucial steps in preprocessing of functional magnetic resonance images, induces its own autocorrelation. While performing connectivity analysis in fMRI, the impact of the autocorrelation is largely ignored. Recently, autocorrelation has been addressed by variance correction approaches, which are sensitive to the sampling rate. In this article, we aim to investigate the impact of the sampling rate on the variance correction approaches. Toward this end, we first derived a generalized expression for the variance of the sample Pearson correlation coefficient (SPCC) in terms of the sampling rate and the filter cutoff frequency, in addition to the autocorrelation and cross‐covariance functions of the time series. Through simulations, we illustrated the importance of the variance correction for a fixed sampling rate. Using the real resting state fMRI data sets, we demonstrated that the data sets with higher sampling rates were more prone to false positives, in agreement with the existing empirical reports. We further demonstrated with single subject results that for the data sets with higher sampling rates, the variance correction strategy restored the integrity of true connectivity.

Predicting Cognitive Scores from Resting fMRI Data and Geometric Features of the Brain.

Anatomical T1 weighted Magnetic Resonance Imaging (MRI) and functional magnetic resonance imaging collected during resting (rfMRI) are promising markers that offer insight into the structure and function of the human brain. The objective of this work is to explore the use of a deep learning neural network to predict cognitive performance scores for a population of normal controls and subjects with Attention Deficit Hyperactivity Disorder (ADHD). Specifically, we predict verbal and performance IQs and ADHD index from features derived from T1 and rfMRI imaging data. First, we processed the rfMRI and MRI data of subjects using the BrainSuite fMRI Processing (BFP) pipeline to perform anatomical and functional preprocessing. This produces for each subject fMRI and geometric (anatomical) features represented in a standardized grayordinate system. The geometric and functional cortical data corresponding to the two hemispheres were then transformed to 128×128 multichannel images and input to a convolutional component of the neural network. Subcortical data were presented in a standard vector form and inputted to a input layer of the network. The neural network was implemented in Python using the Keras library with a TensorFlow backend. Training was performed on 168 images with 90 images used for testing. We observed a high correlation between predicted and actual values of the indices tested: Performance IQ: 0.47; Verbal IQ: 0.41, ADHD: 0.57. Comparing these values to those from network trained on functional-only and structural-only data, we saw that rfMRI is more informative than MRI, but the two modalities are highly complementary in terms of predicting these indices.

A general prediction model for the detection of ADHD and Autism using structural and functional MRI

This work presents a novel method for learning a model that can diagnose Attention Deficit Hyperactivity Disorder (ADHD), as well as Autism, using structural texture and functional connectivity features obtained from 3-dimensional structural magnetic resonance imaging (MRI) and 4-dimensional resting-state functional magnetic resonance imaging (fMRI) scans of subjects. We explore a series of three learners: (1) The LeFMS learner first extracts features from the structural MRI images using the texture-based filters produced by a sparse autoencoder. These filters are then convolved with the original MRI image using an unsupervised convolutional network. The resulting features are used as input to a linear support vector machine (SVM) classifier. (2) The LeFMF learner produces a diagnostic model by first computing spatial non-stationary independent components of the fMRI scans, which it uses to decompose each subject’s fMRI scan into the time courses of these common spatial components. These features can then be used with a learner by themselves or in combination with other features to produce the model. Regardless of which approach is used, the final set of features are input to a linear support vector machine (SVM) classifier. (3) Finally, the overall LeFMSF learner uses the combined features obtained from the two feature extraction processes in (1) and (2) above as input to an SVM classifier, achieving an accuracy of 0.673 on the ADHD-200 holdout data and 0.643 on the ABIDE holdout data. Both of these results, obtained with the same LeFMSF framework, are the best known, over all hold-out accuracies on these datasets when only using imaging data—exceeding previously-published results by 0.012 for ADHD and 0.042 for Autism. Our results show that combining multi-modal features can yield good classification accuracy for diagnosis of ADHD and Autism, which is an important step towards computer-aided diagnosis of these psychiatric diseases and perhaps others as well.

Decoding Multiple Subject fMRI Data using Manifold based Representation of Cognitive State Neural Signatures

Mind reading or thought prediction is a promising application of functional neuroimaging studies. The emergence of functional magnetic resonance imaging (fMRI) has, in the last two decades given a boost to these studies. In order to improve the accuracy, predictability and repeatability of thought prediction, it is important to have a representation that can capture the nuances of fMRI activations with respect to a particular cognitive state. In this paper, the process of creating a geometrical representation of the activations using non-linear manifolds is described. Manifold learning brings out the geometry of the activated voxels in the fMRI image. It is shown that this kind of representation is able to give high accuracy in classification studies as compared to using activation profiles.

Visual and proprioceptive interaction in patients with bilateral vestibular loss.

Following bilateral vestibular loss (BVL) patients gradually adapt to the loss of vestibular input and rely more on other sensory inputs. Here we examine changes in the way proprioceptive and visual inputs interact. We used functional magnetic resonance imaging (fMRI) to investigate visual responses in the context of varying levels of proprioceptive input in 12 BVL subjects and 15 normal controls. A novel metal-free vibrator was developed to allow vibrotactile neck proprioceptive input to be delivered in the MRI system. A high level (100 Hz) and low level (30 Hz) control stimulus was applied over the left splenius capitis; only the high frequency stimulus generates a significant proprioceptive stimulus. The neck stimulus was applied in combination with static and moving (optokinetic) visual stimuli, in a factorial fMRI experimental design. We found that high level neck proprioceptive input had more cortical effect on brain activity in the BVL patients. This included a reduction in visual motion responses during high levels of proprioceptive input and differential activation in the midline cerebellum. In early visual cortical areas, the effect of high proprioceptive input was present for both visual conditions but in lateral visual areas, including V5/MT, the effect was only seen in the context of visual motion stimulation. The finding of a cortical visuo-proprioceptive interaction in BVL patients is consistent with behavioural data indicating that, in BVL patients, neck afferents partly replace vestibular input during the CNS-mediated compensatory process. An fMRI cervico-visual interaction may thus substitute the known visuo-vestibular interaction reported in normal subject fMRI studies. The results provide evidence for a cortical mechanism of adaptation to vestibular failure, in the form of an enhanced proprioceptive influence on visual processing. The results may provide the basis for a cortical mechanism involved in proprioceptive substitution of vestibular function in BVL patients.

Single subject fMRI test–retest reliability metrics and confounding factors

While the fMRI test-retest reliability has been mainly investigated from the point of view of group level studies, here we present analyses and results for single-subject test-retest reliability. One important aspect of group level reliability is that not only does it depend on between-session variance (test-retest), but also on between-subject variance. This has partly led to a debate regarding which reliability metric to use and how different sources of noise contribute to between-session variance. Focusing on single subject reliability allows considering between-session only. In this study, we measured test-retest reliability in four behavioural tasks (motor mapping, covert verb generation, overt word repetition, and a landmark identification task) to ensure generalisation of the results and at three levels of data processing (time-series correlation, t value variance, and overlap of thresholded maps) to understand how each step influences the other and how confounding factors influence reliability at each of these steps. The contributions of confounding factors (scanner noise, subject motion, and coregistration) were investigated using multiple regression and relative importance analyses at each step. Finally, to achieve a fuller picture of what constitutes a reliable task, we introduced a bootstrap technique of within- vs. between-subject variance. Our results show that (i) scanner noise and coregistration errors have little contribution to between-session variance (ii) subject motion (especially correlated with the stimuli) can have detrimental effects on reliability (iii) different tasks lead to different reliability results. This suggests that between-session variance in fMRI is mostly caused by the variability of underlying cognitive processes and motion correlated with the stimuli rather than technical limitations of data processing.

Separating 4D multi-task fMRI data of multiple subjects by independent component analysis with projection

Independent component analysis (ICA) is a widely accepted method to extract brain networks underlying cognitive processes from functional magnetic resonance imaging (fMRI) data. However, the application of ICA to multi-task fMRI data is limited due to the potential non-independency between task-related components. The ICA with projection (ICAp) method proposed by our group (Hum Brain Mapp 2009;30:417–31) is demonstrated to be able to solve the interactions among task-related components for single subject fMRI data. However, it still must be determined if ICAp is capable of processing multi-task fMRI data over a group of subjects. Moreover, it is unclear whether ICAp can be reliably applied to event-related (ER) fMRI data. In this study, we combined the projection method with the temporal concatenation method reported by Calhoun (Hum Brain Mapp 2008;29:828–38), referred to as group ICAp, to perform the group analysis of multi-task fMRI data. Both a human fMRI rest data-based simulation and real fMRI experiments, of block design and ER design, verified the feasibility and reliability of group ICAp, as well as demonstrated that ICAp had the strength to separate 4D multi-task fMRI data into multiple brain networks engaged in each cognitive task and to adequately find the commonalities and differences among multiple tasks.

Does Parametric fMRI Analysis with SPM Yield Valid Results? – An Empirical Study of 1484 Rest Datasets

Does Parametric fMRI Analysis with SPM Yield Valid Results? – An Empirical Study of 1484 Rest Datasets

Adaptive thresholding for reliable topological inference in single subject fMRI analysis

Single subject fMRI has proved to be a useful tool for mapping functional areas in clinical procedures such as tumor resection. Using fMRI data, clinicians assess the risk, plan and execute such procedures based on thresholded statistical maps. However, because current thresholding methods were developed mainly in the context of cognitive neuroscience group studies, most single subject fMRI maps are thresholded manually to satisfy specific criteria related to single subject analyzes. Here, we propose a new adaptive thresholding method which combines Gamma-Gaussian mixture modeling with topological thresholding to improve cluster delineation. In a series of simulations we show that by adapting to the signal and noise properties, the new method performs well in terms of total number of errors but also in terms of the trade-off between false negative and positive cluster error rates. Similarly, simulations show that adaptive thresholding performs better than fixed thresholding in terms of over and underestimation of the true activation border (i.e., higher spatial accuracy). Finally, through simulations and a motor test–retest study on 10 volunteer subjects, we show that adaptive thresholding improves reliability, mainly by accounting for the global signal variance. This in turn increases the likelihood that the true activation pattern can be determined offering an automatic yet flexible way to threshold single subject fMRI maps.

Changes in cortical activation in craniomandibular disorders during splint therapy – A single subject fMRI study

There is some controversial discussion within the therapy of craniomandibular disorders (CMDs) about the mode of action of occlusal splints. Here we present a case report on one CMD-patient measuring cerebral activation changes with functional magnetic resonance imaging (fMRI) before and after therapy with a stabilization splint. Wearing the Michigan splint for 11 nights and partially days resulted in substantial pain relief and changes in occlusal movement performance. Cerebral activation during occlusion was decreased after therapy (PRE-POST) in bilateral sensorimotor regions but also additional areas such as left posterior insula, right superior temporal cortex and bilateral occipital lobe. During the first usage of the splint in the scanner (PRE) increased activation in the left dorsolateral prefrontal lobe (BA 9) was observed. After splint training occlusion with the splint compared to without a splint increasingly involved the left superior parietal lobe (BA 7, POST). Whereas BA 9 might be associated with increasing working memory load due to the manipulation with an unusual object, the BA 7 activation in the POST session might document increased sensorimotor interaction after getting used to the splint. Our findings indicate that wearing an occlusion splint triggers activation in parietal sensorimotor integration areas, also observed after long periods of sensorimotor training. These additional recourses might improve coordination and physiological handling of the masticatory system.

Functionally distinct regions for spatial processing and sensory motor integration in the planum temporale.

There has been much debate recently over the functional role played by the planum temporale (PT) within the context of the dorsal auditory processing stream. Some studies indicate that regions in the PT support spatial hearing and other auditory functions, whereas others demonstrate sensory-motor response properties. This multifunctionality has led to the claim that the PT is performing a common computational pattern matching operation, then routing the signals (spatial, object, sensory-motor) into an appropriate processing stream. An alternative possibility is that the PT is functionally subdivided with separate regions supporting various functions. We assess this possibility using a within subject fMRI block design. DTI data were also collected to examine connectivity. There were four auditory conditions: stationary noise, moving noise, listening to pseudowords, and shadowing pseudowords (covert repetition). Contrasting the shadow and listen conditions should activate regions specific to sensory-motor processes, while contrasting the stationary and moving noise conditions should activate regions involved in spatial hearing. Subjects (N = 16) showed greater activation for shadowing in left posterior PT, area Spt, when the shadow and listen conditions were contrasted. The motion vs. stationary noise contrast revealed greater activation in a more medial and anterior portion of left PT. Seeds from these two contrasts were then used to guide the DTI analysis in an examination of connectivity via streamline tractography, which revealed different patterns of connectivity. Findings support a heterogeneous model of the PT, with functionally distinct regions for sensory-motor integration and processes involved in auditory spatial perception.

Parallel Computation in Medical Imaging Applications

Parallel Computation in Medical Imaging Applications

A non-parametric approach for fMRI analysis: application for single subject data analysis

Event Abstract Back to Event A non-parametric approach for fMRI analysis: application for single subject data analysis Andor V. Gál1, 2*, Adám Bíró1, 2 and Zoltán Vidnyánszky1, 2, 3 1 Pázmány Péter Catholic University, Faculty of Information Technology, Hungary 2 Semmelweis University, MR Research Center, Szentágothai J. Knowledge Center, Hungary 3 Pázmány Péter Catholic University - Semmelweis University, Neurobionics Research Group, Hungarian Academy of Sciences, Hungary The classical analytical method in functional neuroimaging, Statistical Parametric Mapping is prone to violation of a set of assumptions about homogeneity of variance, normality, and independent errors. Prospective bias in the statistical decision escalates as the number of inspected voxels grow (due to multiple comparisons) and in case of fixed effect (single subject) analysis. In clinical applications (typical single subject studies) the appropriate control for potential statistical errors is primary concern. We developed a new, non-parametric method that requires only minimal assumptions for validity and can be used to assess statistical significance in voxelwise analysis accounting for the multiple comparisons problem. The core concept is to create a null-hypothesis sampling distribution of GLM beta-weights for each experimental condition separately. The sampling distribution is formed using randomly generated alternative temporal condition sequences that meet design optimization constraints. Unlike other nonparametric permutation or surrogate data generation based algorithms, this technique does not require multiple experimental conditions and additional assumptions about the structure of the data are minimal. We validated our non-parametric approach on the data obtained from two single subject fMRI experiments and the results were compared to the results obtained using standard parametric approaches on the same data set. It was found that when the appropriate assumptions hold, the nonparametric permutation approach gives more stable results as compared to those obtained from a parametric analysis: i.e. variability in voxel-activations between different studies of the same subject is lower. Conference: IBRO International Workshop 2010, Pécs, Hungary, 21 Jan - 23 Jan, 2010. Presentation Type: Poster Presentation Topic: Abstracts Citation: Gál AV, Bíró A and Vidnyánszky Z (2010). A non-parametric approach for fMRI analysis: application for single subject data analysis. Front. Neurosci. Conference Abstract: IBRO International Workshop 2010. doi: 10.3389/conf.fnins.2010.10.00228 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 04 May 2010; Published Online: 04 May 2010. * Correspondence: Andor V Gál, Pázmány Péter Catholic University, Faculty of Information Technology, Budapest, Hungary, gviktor@digitus.itk.ppke.hu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Andor V Gál Adám Bíró Zoltán Vidnyánszky Google Andor V Gál Adám Bíró Zoltán Vidnyánszky Google Scholar Andor V Gál Adám Bíró Zoltán Vidnyánszky PubMed Andor V Gál Adám Bíró Zoltán Vidnyánszky Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

The Use of Neurofeedback with Real-Time Functional MRI to Suppress Physiological Noise.

ROIs: Sinus=yellow; Ventricles=red. A B The Use of Neurofeedback with Real-Time Functional MRI to Suppress Physiological Noise. J. Bodurka, J. Gonzalez-Castillo, and P. Bandettini Functional MRI Facility, National Institute of Mental Health, NIH, Bethesda, MD, United States, Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States, Section on Functional Imaging Methods, National Institute of Mental Health, NIH, Bethesda, MD, United States INTRODUCTION: Neurofeedback refers to experiments and therapeutic techniques that present subjects with real-time information about their own measured signal, such as brain activity measured with functional magnetic resonance imaging (fMRI). The most extended form of neurofeedback is ECG-Neurofeedback, which has been reported to be successful for clinical applications [1,2]. However, one important limitation of ECG-Neurofeedback is low spatial resolution. Real-time fMRI (rtfMRI) was introduced more than a decade ago [3] but, recent rapid advances in MRI signal reception and detection hardware (multi-channel detectors, better RF coils, faster imaging) as well as computer technologies (increases in image reconstruction speed) resulted in wider implementation of the real-time fMRI [4]. The neurofeedback techniques based on rtfMRI offer excellent spatial resolution and the ability to feedback activity from specific brain structures. Using rtfMRI-Neurofeedback, experimenters have already proven that subjects can self-regulate: pain perception [5], activation on areas such as the hand motor area [6], or the amygdala [7]. In the present study, we employed rtfMRI neurofeedback to investigate whether healthy subjects can learn to self-regulate the variability of the fMRI response in areas affected by high levels of physiological noise [8]. In particular, subjects were asked to attempt to reduce the standard deviation (SDEV) of the fMRI signal in the ventricles and in the posterior sinus. We hypothesized that learning to self-control the variability of the fMRI signal in these extra-cortical areas would be accompanied by increased fMRI time series temporal signal to noise ratio (TSNR=mean voxel time course signal/time course standard deviation [8]) in both white (WM) and gray (GM) matter compartments. MATERIAL and METHODS: MRI Imaging: Six subjects (3 males/3 females) were scanned in a 3T General Electric HDx MRI Scanner equipped with home made rtfMRI system. Sixteen-element receive-only brain array (Nova Medical Inc) and Gradient Echo single shot EPI were used. Physiological noise localizer parameters (EPI, TR/TE=400/27ms, FA=35°, FOV/slice=240/4mm, matrix=64x64, #Slices=7, #Volumes=400). All other functional runs (EPI, TR/TE=2000/30ms, FA=90°, FOV/slice=240/4mm, matrix=64x64, #Slices=20, #Volumes=140, first 4 volumes were not used in data analysis to ensure fMRI signal steady state). Neurofeedback Display: Figure 1A shows ROIs used. Two dynamic bars (Figure 1B) left bar sinus ROI, right ventricles) associated with the SDEV of the subject fMRI signal in the selected two ROIs were presented back to the subject. Bar height was updated with each acquired volume. SDEV was calculated using a running window that expanded back six volumes from the last acquired volume. Experimental Paradigm: No specific instruction how to control their brain signals were given to all subjects except requests to stay calm, avoid movements and breath regular. Scans: 3-Plane Localizer; Physiological Noise Localizer to determine 2 ROIs (sagittal sinus, ventricle); Resting Runs: eyes closed (BEFORE training: REST1,REST2); Training: subjects learned to regulate neurofeedback signal (3 runs); Reduce SDEV + Finger Tapping: keep SDEV as low as possible and perform finger tapping on a block design (2 runs). Reduce SDEV: keep SDEV as low as possible with no additional task (AFTER training, REST1,REST2). Data Analysis: Performed with AFNI. We focus solely on the comparison of beforeand after-training resting runs (no finger tapping). Processing steps: (1) motion correction to 4 volume, (2) tissue segmentation over EPI volumes using method described in [8], (3) calculate TSNR values for white and gray matter compartments. Training Evaluation: (1) Qualitative evaluation: the mean value of the SDEV time series was computed for each subject and each ROI. These mean SDEV values were subsequently used to calculate percent change associated with training; and were also used as input to a paired t-test devoted to evaluate the significance of the observed changes

Extrapolation Scheme For Fast 3D Ising Field Partition Function Estimation. Application to Bayesian Analysis of Within Subject fMRI Data

Extrapolation Scheme For Fast 3D Ising Field Partition Function Estimation. Application to Bayesian Analysis of Within Subject fMRI Data