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Altered Regional Homogeneity in Pediatric Bipolar Disorder during Manic State: A Resting-State fMRI Study

Xiao, Qian; Zhong, Yuan; Lu, Dali; Gao, Weijia; Jiao, Qing; Lu, Guangming; Su, Linyan
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 06/03/2013 Português
Relevância na Pesquisa
26.73%
Pediatric bipolar disorder (PBD) is a severely debilitating illness, which is characterized by episodes of mania and depression separated by periods of remission. Previous fMRI studies investigating PBD were mainly task-related. However, little is known about the abnormalities in PBD, especially during resting state. Resting state brain activity measured by fMRI might help to explore neurobiological biomarkers of the disorder. Methods: Regional homogeneity (ReHo) was examined with resting-state fMRI (RS-fMRI) on 15 patients with PBD in manic state, with 15 age-and sex-matched healthy youth subjects as controls. Results: Compared with the healthy controls, the patients with PBD showed altered ReHo in the cortical and subcortical structures. The ReHo measurement of the PBD group was negatively correlated with the score of Young Mania Rating Scale (YMRS) in the superior frontal gyrus. Positive correlations between the ReHo measurement and the score of YMRS were found in the hippocampus and the anterior cingulate cortex in the PBD group. Conclusions: Altered regional brain activity is present in patients with PBD during manic state. This study presents new evidence for abnormal ventral-affective and dorsal-cognitive circuits in PBD during resting state and may add fresh insights into the pathophysiological mechanisms underlying PBD.

Antipsychotic Drug Effects in Schizophrenia: A Review of Longitudinal fMRI Investigations and Neural Interpretations

Abbott, C.C.; Jaramillo, A.; Wilcox, C.E.; Hamilton, D.A.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em //2013 Português
Relevância na Pesquisa
26.73%
The evidence that antipsychotics improve brain function and reduce symptoms in schizophrenia is unmistakable, but how antipsychotics change brain function is poorly understood, especially within neuronal systems. In this review, we investigated the hypothesized normalization of the functional magnetic resonance imaging (fMRI) blood oxygen level dependent signal in the context of antipsychotic treatment. First, we conducted a systematic PubMed search to identify eight fMRI investigations that met the following inclusion criteria: case-control, longitudinal design; pre- and post-treatment contrasts with a healthy comparison group; and antipsychotic-free or antipsychotic-naïve patients with schizophrenia at the start of the investigation. We hypothesized that aberrant activation patterns or connectivity between patients with schizophrenia and healthy comparisons at the first imaging assessment would no longer be apparent or “normalize” at the second imaging assessment. The included studies differed by analysis method and fMRI task but demonstrated normalization of fMRI activation or connectivity during the treatment interval. Second, we reviewed putative mechanisms from animal studies that support normalization of the BOLD signal in schizophrenia. We provided several neuronal-based interpretations of these changes of the BOLD signal that may be attributable to long-term antipsychotic administration.

Characterizing Functional Connectivity Differences in Aging Adults using Machine Learning on Resting State fMRI Data

Vergun, Svyatoslav; Deshpande, Alok S.; Meier, Timothy B.; Song, Jie; Tudorascu, Dana L.; Nair, Veena A.; Singh, Vikas; Biswal, Bharat B.; Meyerand, M. Elizabeth; Birn, Rasmus M.; Prabhakaran, Vivek
Fonte: Frontiers Media S.A. Publicador: Frontiers Media S.A.
Tipo: Artigo de Revista Científica
Publicado em 25/04/2013 Português
Relevância na Pesquisa
26.73%
The brain at rest consists of spatially distributed but functionally connected regions, called intrinsic connectivity networks (ICNs). Resting state functional magnetic resonance imaging (rs-fMRI) has emerged as a way to characterize brain networks without confounds associated with task fMRI such as task difficulty and performance. Here we applied a Support Vector Machine (SVM) linear classifier as well as a support vector machine regressor to rs-fMRI data in order to compare age-related differences in four of the major functional brain networks: the default, cingulo-opercular, fronto-parietal, and sensorimotor. A linear SVM classifier discriminated between young and old subjects with 84% accuracy (p-value < 1 × 10−7). A linear SVR age predictor performed reasonably well in continuous age prediction (R2 = 0.419, p-value < 1 × 10−8). These findings reveal that differences in intrinsic connectivity as measured with rs-fMRI exist between subjects, and that SVM methods are capable of detecting and utilizing these differences for classification and prediction.

Monitoring Functional Impairment and Recovery after Traumatic Brain Injury in Rats by fMRI

Niskanen, Juha-Pekka; Airaksinen, Antti M.; Sierra, Alejandra; Huttunen, Joanna K.; Nissinen, Jari; Karjalainen, Pasi A.; Pitkänen, Asla; Gröhn, Olli H.
Fonte: Mary Ann Liebert, Inc. Publicador: Mary Ann Liebert, Inc.
Tipo: Artigo de Revista Científica
Publicado em 01/04/2013 Português
Relevância na Pesquisa
26.73%
The present study was designed to test a hypothesis that functional magnetic resonance imaging (fMRI) can be used to monitor functional impairment and recovery after moderate experimental traumatic brain injury (TBI). Moderate TBI was induced by lateral fluid percussion injury in adult rats. The severity of brain damage and functional recovery in the primary somatosensory cortex (S1) was monitored for up to 56 days using fMRI, cerebral blood flow (CBF) by arterial spin labeling, local field potential measurements (LFP), behavioral assessment, and histology. All the rats had reduced blood-oxygen-level-dependent (BOLD) responses during the 1st week after trauma in the ipsilateral S1. Forty percent of these animals showed recovery of the BOLD response during the 56 day follow-up. Unexpectedly, no association was found between the recovery in BOLD response and the volume of the cortical lesion or thalamic neurodegeneration. Instead, the functional recovery occurred in rats with preserved myelinated fibers in layer VI of S1. This is, to our knowledge, the first study demonstrating that fMRI can be used to monitor post-TBI functional impairment and consequent spontaneous recovery. Moreover, the BOLD response was associated with the density of myelinated fibers in the S1...

FMRI analysis of contrast polarity processing in face-selective cortex in humans and monkeys

Yue, Xiaomin; Nasr, Shahin; Devaney, Kathryn J.; Holt, Daphne J.; Tootell, Roger B.H.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.73%
Recognition is strongly impaired when the normal contrast polarity of faces is reversed. For instance, otherwise-familiar faces become very difficult to recognize when viewed as photographic negatives. Here, we used fMRI to demonstrate related properties in visual cortex: 1) fMRI responses in the human Fusiform Face Area (FFA) decreased strongly (26%) to contrast-reversed faces across a wide range of contrast levels (5.3-100% RMS contrast), in all subjects tested. In a whole brain analysis, this contrast polarity bias was largely confined to the Fusiform Face Area (FFA; p < 0.0001), with possible involvement of a left occipital face-selective region. 2) It is known that reversing facial contrast affects three image properties in parallel (absorbance, shading, and specular reflection). Here, comparison of FFA responses to those in V1 suggests that the contrast polarity bias is produced in FFA only when all three component properties were reversed simultaneously, which suggests a prominent non-linearity in FFA processing. 3) Across a wide range (180°) of illumination source angles, 3D face shapes without texture produced response constancy in FFA, without a contrast polarity bias. 4) Consistent with psychophysics, analogous fMRI biases for normal contrast polarity were not produced by non-face objects...

Whole-head rapid fMRI acquisition using echo-shifted magnetic resonance inverse imaging

Chang, Wei-Tang; Nummenmaa, Aapo; Witzel, Thomas; Ahveninen, Jyrki; Huang, Samantha; Tsai, Kevin Wen-Kai; Chu, Ying-Hua; Polimeni, Jonathan R; Belliveau, John W.; Lin, Fa-Hsuan
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.73%
The acquisition time of BOLD contrast functional MRI (fMRI) data with whole-brain coverage typically requires a sampling rate of one volume in 1 3 seconds. Although the volumetric sampling time of a few seconds is adequate for measuring the sluggish hemodynamic response (HDR) to neuronal activation, faster sampling of fMRI might allow for monitoring of rapid physiological fluctuations and detection of subtle neuronal activation timing information embedded in BOLD signals. Previous studies utilizing a highly accelerated volumetric MR inverse imaging (InI) technique have provided a sampling rate of one volume per 100 ms with 5 mm spatial resolution. Here, we propose a novel modification of this technique, the echo-shifted InI, which allows TE to be longer than TR, to measure BOLD fMRI at an even faster sampling rate of one volume per 25 ms with whole-brain coverage. Compared with conventional EPI, echo-shifted InI provided 80-fold speedup with similar spatial resolution and less than 2-fold temporal SNR loss. The capability of echo-shifted InI to detect HDR timing differences was tested empirically. At the group level (n=6), echo-spaced InI was able to detect statistically significant HDR timing differences of as low as 50 ms in visual stimulus presentation. At the level of individual subjects...

Order Selection of the Linear Mixing Model for Complex-valued FMRI Data

Xiong, Wei; Li, Yi-Ou; Correa, Nicolle; Adalı, Tülay; Calhoun, Vince D.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.73%
Functional magnetic resonance imaging (fMRI) data are originally acquired as complex-valued images, which motivates the use of complex-valued data analysis methods. Due to the high dimension and high noise level of fMRI data, order selection and dimension reduction are important procedures for multivariate analysis methods such as independent component analysis (ICA). In this work, we develop a complex-valued order selection method to estimate the dimension of signal subspace using information-theoretic criteria. To correct the effect of sample dependence to information-theoretic criteria, we develop a general entropy rate measure for complex Gaussian random process to calibrate the independent and identically distributed (i.i.d.) sampling scheme in the complex domain. We show the effectiveness of the approach for order selection on both simulated and actual fMRI data. A comparison between the results of order selection and ICA on real-valued and complex-valued fMRI data demonstrates that a fully complex analysis extracts more information about brain activation.

Spatial vs. Temporal Features in ICA of Resting-State fMRI – A Quantitative and Qualitative Investigation in the Context of Response Inhibition

Tian, Lixia; Kong, Yazhuo; Ren, Juejing; Varoquaux, Gaël; Zang, Yufeng; Smith, Stephen M.
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 18/06/2013 Português
Relevância na Pesquisa
26.73%
Independent component analysis (ICA) can identify covarying functional networks in the resting brain. Despite its relatively widespread use, the potential of the temporal information (unlike spatial information) obtained by ICA from resting state fMRI (RS-fMRI) data is not always fully utilized. In this study, we systematically investigated which features in ICA of resting-state fMRI relate to behaviour, with stop signal reaction time (SSRT) in a stop-signal task taken as a test case. We did this by correlating SSRT with the following three kinds of measure obtained from RS-fMRI data: (1) the amplitude of each resting state network (RSN) (evaluated by the standard deviation of the RSN timeseries), (2) the temporal correlation between every pair of RSN timeseries, and (3) the spatial map of each RSN. For multiple networks, we found significant correlations not only between SSRT and spatial maps, but also between SSRT and network activity amplitude. Most of these correlations are of functional interpretability. The temporal correlations between RSN pairs were of functional significance, but these correlations did not appear to be very sensitive to finding SSRT correlations. In addition, we also investigated the effects of the decomposition dimension...

Advances in Functional Neuroanatomy: A Review of Combined DTI and fMRI Studies in Healthy Younger and Older Adults

Bennett, Ilana J.; Rypma, Bart
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.73%
Structural connections between brain regions are thought to influence neural processing within those regions. It follows that alterations to the quality of structural connections should influence the magnitude of neural activity. The quality of structural connections may also be expected to differentially influence activity in directly versus indirectly connected brain regions. To test these predictions, we reviewed studies that combined diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) in younger and older adults. By surveying studies that examined relationships between DTI measures of white matter integrity and fMRI measures of neural activity, we identified variables that accounted for variability in these relationships. Results revealed that relationships between white matter integrity and neural activity varied with (1) aging (i.e., positive and negative DTI-fMRI relationships in younger and older adults, respectively) and (2) spatial proximity of the neural measures (i.e., positive and negative DTI-fMRI relationships when neural measures were extracted from adjacent and non-adjacent brain regions, respectively). Together, the studies reviewed here provided support for both of our predictions.

Real-time fMRI links subjective experience with brain activity during focused attention

Garrison, Kathleen A.; Scheinost, Dustin; Worhunsky, Patrick D.; Elwafi, Hani M.; Thornhill, Thomas A.; Thompson, Evan; Saron, Clifford; Desbordes, Gaëlle; Kober, Hedy; Hampson, Michelle; Gray, Jeremy R.; Constable, R. Todd; Papademetris, Xenophon; Brewe
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.73%
Recent advances in brain imaging have improved the measure of neural processes related to perceptual, cognitive and affective functions, yet the relation between brain activity and subjective experience remains poorly characterized. In part, it is a challenge to obtain reliable accounts of participant’s experience in such studies. Here we addressed this limitation by utilizing experienced meditators who are expert in introspection. We tested a novel method to link objective and subjective data, using real-time fMRI (rt-fMRI) to provide participants with feedback of their own brain activity during an ongoing task. We provided real-time feedback during a focused attention task from the posterior cingulate cortex, a hub of the default mode network shown to be activated during mind-wandering and deactivated during meditation. In a first experiment, both meditators and non-meditators reported significant correspondence between the feedback graph and their subjective experience of focused attention and mind-wandering. When instructed to volitionally decrease the feedback graph, meditators, but not non-meditators, showed significant deactivation of the posterior cingulate cortex. We were able to replicate these results in a separate group of meditators using a novel step-wise rt-fMRI discovery protocol in which participants were not provided with prior knowledge of the expected relationship between their experience and the feedback graph (i.e....

Independent Component Analysis (ICA) of Generalized Spike Wave Discharges in fMRI: Comparison with General Linear Model-Based EEG-fMRI

Moeller, Friederike; LeVan, Pierre; Gotman, Jean
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /02/2011 Português
Relevância na Pesquisa
26.73%
Most EEG-fMRI studies in epileptic patients are analyzed using the general linear model (GLM), which assumes a known hemodynamic response function (HRF) to epileptic spikes. In contrast, independent component analysis (ICA) can extract blood-oxygenation level dependent (BOLD) responses without imposing constraints on the HRF. ICA might therefore detect responses that vary in time and shape, and that are not detected in the GLM analysis. In this study, we compared the findings of ICA and GLM analyses in 12 patients with idiopathic generalized epilepsy. Spatial ICA was used to extract independent components from the functional magnetic resonance imaging (fMRI) data. A deconvolution method identified component time courses significantly related to the generalized EEG discharges, without constraining the shape of the HRF. The results from the ICA analysis were compared to those from the GLM analysis. GLM maps and ICA maps showed significant correlation and revealed BOLD responses in the thalamus, caudate nucleus, and default mode areas. In patients with a low rate of discharges per minute, the GLM analysis detected BOLD signal changes within the thalamus and the caudate nucleus that were not revealed by the ICA. In conclusion, ICA is a viable alternative technique to GLM analyses in EEG-fMRI studies related to generalized discharges. This study demonstrated that the BOLD response largely resembles the standard HRF and that GLM analysis is adequate. However...

Separating neural and vascular effects of caffeine using simultaneous EEG–FMRI: Differential effects of caffeine on cognitive and sensorimotor brain responses

Diukova, Ana; Ware, Jennifer; Smith, Jessica E.; Evans, C. John; Murphy, Kevin; Rogers, Peter J.; Wise, Richard G.
Fonte: Academic Press Publicador: Academic Press
Tipo: Artigo de Revista Científica
Publicado em 01/08/2012 Português
Relevância na Pesquisa
26.73%
The effects of caffeine are mediated through its non-selective antagonistic effects on adenosine A1 and A2A adenosine receptors resulting in increased neuronal activity but also vasoconstriction in the brain. Caffeine, therefore, can modify BOLD FMRI signal responses through both its neural and its vascular effects depending on receptor distributions in different brain regions. In this study we aim to distinguish neural and vascular influences of a single dose of caffeine in measurements of task-related brain activity using simultaneous EEG–FMRI. We chose to compare low-level visual and motor (paced finger tapping) tasks with a cognitive (auditory oddball) task, with the expectation that caffeine would differentially affect brain responses in relation to these tasks. To avoid the influence of chronic caffeine intake, we examined the effect of 250 mg of oral caffeine on 14 non and infrequent caffeine consumers in a double-blind placebo-controlled cross-over study. Our results show that the task-related BOLD signal change in visual and primary motor cortex was significantly reduced by caffeine, while the amplitude and latency of visual evoked potentials over occipital cortex remained unaltered. However, during the auditory oddball task (target versus non-target stimuli) caffeine significantly increased the BOLD signal in frontal cortex. Correspondingly...

Combining Spatial Priors and Anatomical Information for fMRI Detection

Ou, Wanmei; Wells, William M.; Golland, Polina
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.73%
In this paper, we analyze Markov Random Field (MRF) as a spatial regularizer in fMRI detection. The low signal-to-noise ratio (SNR) in fMRI images presents a serious challenge for detection algorithms, making regularization necessary to achieve good detection accuracy. Gaussian smoothing, traditionally employed to boost SNR, often produces over-smoothed activation maps. Recently, the use of MRF priors has been suggested as an alternative regularization approach. However, solving for an optimal configuration of the MRF is NP-hard in general. In this work, we investigate fast inference algorithms based on the Mean Field approximation in application to MRF priors for fMRI detection. Furthermore, we propose a novel way to incorporate anatomical information into the MRF-based detection framework and into the traditional smoothing methods. Intuitively speaking, the anatomical evidence increases the likelihood of activation in the gray matter and improves spatial coherency of the resulting activation maps within each tissue type. Validation using the receiver operating characteristic (ROC) analysis and the confusion matrix analysis on simulated data illustrates substantial improvement in detection accuracy using the anatomically guided MRF spatial regularizer. We further demonstrate the potential benefits of the proposed method in real fMRI signals of reduced length. The anatomically guided MRF regularizer enables significant reduction of the scan length while maintaining the quality of the resulting activation maps.

Principal Feature Analysis: A Multivariate Feature Selection Method for fMRI Data

Wang, Lijun; Lei, Yu; Zeng, Ying; Tong, Li; Yan, Bin
Fonte: Hindawi Publishing Corporation Publicador: Hindawi Publishing Corporation
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.73%
Brain decoding with functional magnetic resonance imaging (fMRI) requires analysis of complex, multivariate data. Multivoxel pattern analysis (MVPA) has been widely used in recent years. MVPA treats the activation of multiple voxels from fMRI data as a pattern and decodes brain states using pattern classification methods. Feature selection is a critical procedure of MVPA because it decides which features will be included in the classification analysis of fMRI data, thereby improving the performance of the classifier. Features can be selected by limiting the analysis to specific anatomical regions or by computing univariate (voxel-wise) or multivariate statistics. However, these methods either discard some informative features or select features with redundant information. This paper introduces the principal feature analysis as a novel multivariate feature selection method for fMRI data processing. This multivariate approach aims to remove features with redundant information, thereby selecting fewer features, while retaining the most information.

Simultaneous EEG and fMRI: towards the characterization of structure and dynamics of brain networks

Mulert, Christoph
Fonte: Les Laboratoires Servier Publicador: Les Laboratoires Servier
Tipo: Artigo de Revista Científica
Publicado em /09/2013 Português
Relevância na Pesquisa
26.73%
Progress in the understanding of normal and disturbed brain function is critically dependent on the methodological approach that is applied. Both electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are extremely efficient methods for the assessment of human brain function. The specific appeal of the combination is related to the fact that both methods are complementary in terms of basic aspects: EEG is a direct measurement of neural mass activity and provides high temporal resolution. FMRI is an indirect measurement of neural activity and based on hemodynamic changes, and offers high spatial resolution. Both methods are very sensitive to changes of synaptic activity, suggesting that with simultaneous EEG and fMRI the same neural events can be characterized with both high temporal and spatial resolution. Since neural oscillations that can be assessed with EEG are a key mechanism for multi-site communication in the brain, EEG-fMRI can offer new insights into the connectivity mechanisms of brain networks.

ICA Extracts Epileptic Sources from fMRI in EEG-Negative Patients: A Retrospective Validation Study

Hunyadi, Borbála; Tousseyn, Simon; Mijović, Bogdan; Dupont, Patrick; Van Huffel, Sabine; Van Paesschen, Wim; De Vos, Maarten
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 12/11/2013 Português
Relevância na Pesquisa
26.73%
Simultaneous EEG-fMRI has proven to be useful in localizing interictal epileptic activity. However, the applicability of traditional GLM-based analysis is limited as interictal spikes are often not seen on the EEG inside the scanner. Therefore, we aim at extracting epileptic activity purely from the fMRI time series using independent component analysis (ICA). To our knowledge, we show for the first time that ICA can find sources related to epileptic activity in patients where no interictal spikes were recorded in the EEG. The epileptic components were identified retrospectively based on the known localization of the ictal onset zone (IOZ). We demonstrate that the selected components truly correspond to epileptic activity, as sources extracted from patients resemble significantly better the IOZ than sources found in healthy controls. Furthermore, we show that the epileptic components in patients with and without spikes recorded inside the scanner resemble the IOZ in the same degree. We conclude that ICA of fMRI has the potential to extend the applicability of EEG-fMRI for presurgical evaluation in epilepsy.

Real-Time fMRI Pattern Decoding and Neurofeedback Using FRIEND: An FSL-Integrated BCI Toolbox

Sato, João R.; Basilio, Rodrigo; Paiva, Fernando F.; Garrido, Griselda J.; Bramati, Ivanei E.; Bado, Patricia; Tovar-Moll, Fernanda; Zahn, Roland; Moll, Jorge
Fonte: Public Library of Science Publicador: Public Library of Science
Tipo: Artigo de Revista Científica
Publicado em 02/12/2013 Português
Relevância na Pesquisa
26.73%
The demonstration that humans can learn to modulate their own brain activity based on feedback of neurophysiological signals opened up exciting opportunities for fundamental and applied neuroscience. Although EEG-based neurofeedback has been long employed both in experimental and clinical investigation, functional MRI (fMRI)-based neurofeedback emerged as a promising method, given its superior spatial resolution and ability to gauge deep cortical and subcortical brain regions. In combination with improved computational approaches, such as pattern recognition analysis (e.g., Support Vector Machines, SVM), fMRI neurofeedback and brain decoding represent key innovations in the field of neuromodulation and functional plasticity. Expansion in this field and its applications critically depend on the existence of freely available, integrated and user-friendly tools for the neuroimaging research community. Here, we introduce FRIEND, a graphic-oriented user-friendly interface package for fMRI neurofeedback and real-time multivoxel pattern decoding. The package integrates routines for image preprocessing in real-time, ROI-based feedback (single-ROI BOLD level and functional connectivity) and brain decoding-based feedback using SVM. FRIEND delivers an intuitive graphic interface with flexible processing pipelines involving optimized procedures embedding widely validated packages...

In vivo evaluation of the effect of stimulus distribution on FIR statistical efficiency in event-related fMRI

Jansma, J Martijn; de Zwart, Jacco A; van Gelderen, Peter; Duyn, Jeff H; Drevets, Wayne C; Furey, Maura L
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.73%
Technical developments in MRI have improved signal to noise, allowing use of analysis methods such as Finite impulse response (FIR) of rapid event related functional MRI (er-fMRI). FIR is one of the most informative analysis methods as it determines onset and full shape of the hemodynamic response function (HRF) without any a-priori assumptions. FIR is however vulnerable to multicollinearity, which is directly related to the distribution of stimuli over time. Efficiency can be optimized by simplifying a design, and restricting stimuli distribution to specific sequences, while more design flexibility necessarily reduces efficiency. However, the actual effect of efficiency on fMRI results has never been tested in vivo. Thus, it is currently difficult to make an informed choice between protocol flexibility and statistical efficiency. The main goal of this study was to assign concrete fMRI signal to noise values to the abstract scale of FIR statistical efficiency. Ten subjects repeated a perception task with five random and m-sequence based protocol, with varying but, according to literature, acceptable levels of multicollinearity. Results indicated substantial differences in signal standard deviation, while the level was a function of multicollinearity. Experiment protocols varied up to 55.4% in standard deviation. Results confirm that quality of fMRI in an FIR analysis can significantly and substantially vary with statistical efficiency. Our in vivo measurements can be used to aid in making an informed decision between freedom in protocol design and statistical efficiency.

Quasi-periodic patterns (QPP): large-scale dynamics in resting state fMRI that correlate with local infraslow electrical activity

Thompson, Garth John; Pan, Wen-Ju; Magnuson, Matthew Evan; Jaeger, Dieter; Keilholz, Shella Dawn
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.73%
Functional connectivity measurements from resting state blood-oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) are proving a powerful tool to probe both normal brain function and neuropsychiatric disorders. However, the neural mechanisms that coordinate these large networks are poorly understood, particularly in the context of the growing interest in network dynamics. Recent work in anesthetized rats has shown that the spontaneous BOLD fluctuations are tightly linked to infraslow local field potentials (LFPs) that are seldom recorded but comparable in frequency to the slow BOLD fluctuations. These findings support the hypothesis that long-range coordination involves low frequency neural oscillations and establishes infraslow LFPs as an excellent candidate for probing the neural underpinnings of the BOLD spatiotemporal patterns observed in both rats and humans. To further examine the link between large-scale network dynamics and infraslow LFPs, simultaneous fMRI and microelectrode recording were performed in anesthetized rats. Using an optimized filter to isolate shared components of the signals, we found that time-lagged correlation between infraslow LFPs and BOLD is comparable in spatial extent and timing to a quasi-periodic pattern (QPP) found from BOLD alone...

IDENTIFYING PATTERNS IN TEMPORAL VARIATION OF FUNCTIONAL CONNECTIVITY USING RESTING STATE FMRI

Eavani, Harini; Satterthwaite, Theodore D.; Gur, Raquel E.; Gur, Ruben C.; Davatzikos, Christos
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em 31/12/2013 Português
Relevância na Pesquisa
26.73%
Estimating functional brain networks from fMRI data has been the focus of much research in recent years. Low sample sizes (time-points) and high dimensionality of fMRI has restricted estimation to a temporally averaged connectivity matrix per subject, due to which the dynamics of functional connectivity is largely unknown. In this paper, we propose a novel method based on constrained matrix factorization that addresses two major issues. Firstly, it finds a set of basis networks that are the semantic parts of the time-varying whole-brain functional networks. The whole-brain network at any point in time, for any subject, is a non-negative combination of these basis networks. Secondly, significant dimensionality reduction is achieved by projecting the data onto this basis, facilitating subsequent analysis of temporal dynamics. Results on simulated fMRI data show that our method can effectively recover underlying basis networks. We apply this method on a normative dataset of resting state fMRI scans. Results indicate that the functional connectivity of a subject at any point during the scan is composed of combinations of overlapping task-positive/negative pairs of sub-networks.