Página 8 dos resultados de 8518 itens digitais encontrados em 0.008 segundos

"Novos metodos em processamento de sinais cerebrais: aplicações em eletroencefalografia e ressonância magnética funcional".

Tedeschi, Walfred
Fonte: Biblioteca Digitais de Teses e Dissertações da USP Publicador: Biblioteca Digitais de Teses e Dissertações da USP
Tipo: Tese de Doutorado Formato: application/pdf
Publicado em 26/03/2004 Português
Relevância na Pesquisa
26.84%
Muito embora a eletroencefalografia continue sendo amplamente empregada no estudo e diagnóstico da epilepsia, as imagens funcionais de ressonância magnética tornaram-se uma das principais ferramentas de acesso não invasivo às funções normais do cérebro. Atualmente, é uma realidade clínica a aplicação dessas técnicas para o mapeamento pré-cirúrgico e também nos estudos básicos em neurociência. Entretanto, em muitos casos é necessário um estudo combinado dessas duas técnicas. De um modo geral os sinais obtidos em experimentos de ressonância magnética funcional (fMRI) devem ser processados a fim de revelar o mapa de ativação, relativo ao estímulo aplicado. Entretanto até a presente data não há um método consensual para a análise dos sinais de fMRI. Nesse sentido, apresentamos nesse trabalho dois novos métodos para a análise de sinais de fMRI baseados em conceitos de teoria de informação utilizando a entropia de Tsallis. O primeiro método consiste em uma alternativa para análise de fMRI obtida através de paradigmas evento-relacionados, sem que a forma da resposta ao estímulo seja levada em conta. Utilizando a teoria de informação, consideramos a evolução temporal da entropia do sinal sem realizar nenhuma hipótese sobre a forma da função de resposta. O método se mostrou capaz de discriminar regiões ativas e não ativas em paradigmas motores e visuais. Através de simulações...

BOLD fMRI and Somatosensory Evoked Potentials Are Well Correlated Over a Broad Range of Frequency Content of Somatosensory Stimulation of the Rat Forepaw

Goloshevsky, Artem G.; Silva, Afonso C.; Dodd, Stephen J.; Koretsky, Alan P.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.84%
Electrical stimulation of the rat paw is commonly used to study the hemodynamic, metabolic, and neuronal mechanisms of functional MRI (fMRI) responses in somatosensory cortex. Several groups have reported good correlation between the Blood Oxygenation Level Dependent (BOLD) fMRI signal and somatosensory evoked potentials (SEPs) using short, typically 300 μs, square stimulation pulses. The spectral power of these short pulses is evenly distributed over a wide range of frequencies and thus the effects of the frequency content of the stimulation pulse on fMRI responses have not been previously described. Here, the effects that different stimulation pulse waveforms with a range of frequency content have on neuronal activity, as measured by SEPs, and on the amplitude of the BOLD fMRI signal in rat somatosensory cortex are investigated. The peak-to-peak SEP amplitudes increased as the power in the high frequency harmonics of the different pulse waveforms increased, using either triangular or sinusoidal stimuli waveforms from 9 Hz to 180 Hz. Similarly, BOLD fMRI response increased with increased high frequency content of the stimulation pulse. There was a linear correlation between SEPs and BOLD fMRI over the full range of frequency content in the stimulations.

Neuropsychological Predictors of BOLD Response During a Spatial Working Memory Task in Adolescents: What Can Performance Tell Us About fMRI Response Patterns?

NAGEL, BONNIE J.; BARLETT, VALERIE C.; SCHWEINSBURG, ALECIA D.; TAPERT, SUSAN F.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /10/2005 Português
Relevância na Pesquisa
26.84%
The relationship between standardized neuropsychological test performance and functional magnetic resonance imaging (fMRI) response during cognitive tasks is largely unknown. This exploratory investigation examined the relationship between neuropsychological test performance and fMRI response to a spatial working memory (SWM) task among 49 typically developing adolescents. Participants were administered a variety of neuropsychological tests in the domains of working memory, visuospatial skills, executive functioning, attention, learning and memory, visuomotor skills and processing speed, and language functioning. Neuropsychological domain scores were used to predict fMRI response during a SWM task. Results suggest that in many brain regions, neuropsychological performance negatively predicts fMRI response, suggesting that those teens with better neuropsychological abilities required fewer neural resources to adequately perform the task. This study provides further understanding of how neuropsychological abilities relate to neural activity during fMRI tasks, and provides an important link between neuropsychological and fMRI research.

Performance of Blind Source Separation Algorithms for FMRI Analysis using a Group ICA Method

Correa, Nicolle; Adali, Tülay; Calhoun, Vince D.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.84%
Independent component analysis (ICA) is a popular blind source separation (BSS) technique that has proven to be promising for the analysis of functional magnetic resonance imaging (fMRI) data. A number of ICA approaches have been used for fMRI data analysis, and even more ICA algorithms exist, however the impact of using different algorithms on the results is largely unexplored. In this paper, we study the performance of four major classes of algorithms for spatial ICA, namely information maximization, maximization of non-gaussianity, joint diagonalization of cross-cumulant matrices, and second-order correlation based methods when they are applied to fMRI data from subjects performing a visuo-motor task. We use a group ICA method to study the variability among different ICA algorithms and propose several analysis techniques to evaluate their performance. We compare how different ICA algorithms estimate activations in expected neuronal areas. The results demonstrate that the ICA algorithms using higher-order statistical information prove to be quite consistent for fMRI data analysis. Infomax, FastICA, and JADE all yield reliable results; each having their strengths in specific areas. EVD, an algorithm using second-order statistics, does not perform reliably for fMRI data. Additionally...

Brain Correlates of Autonomic Modulation: Combining Heart Rate Variability with fMRI

Napadow, Vitaly; Dhond, Rupali; Conti, Giulia; Makris, Nikos; Brown, Emery N.; Barbieri, Riccardo
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.84%
The central autonomic network (CAN) has been described in animal models but has been difficult to elucidate in humans. Potential confounds include physiological noise artifacts affecting brainstem neuroimaging data, and difficulty in deriving non-invasive continuous assessments of autonomic modulation. We have developed and implemented a new method which relates cardiac-gated fMRI timeseries with continuous-time heart rate variability (HRV) to estimate central autonomic processing. As many autonomic structures of interest are in brain regions strongly affected by cardiogenic pulsatility, we chose to cardiac-gate our fMRI acquisition to increase sensitivity. Cardiac-gating introduces T1-variability, which was corrected by transforming fMRI data to a fixed TR using a previously published method (Guimaraes et al. 1998). The electrocardiogram was analyzed with a novel point process adaptive-filter algorithm for computation of the high-frequency (HF) index, reflecting the time-varying dynamics of efferent cardiovagal modulation. Central command of cardiovagal outflow was inferred by using the HF timeseries resampled at as a regressor to the fMRI data. A grip task was used to perturb the autonomic nervous system. Our combined HRV-fMRI approach demonstrated HF correlation with fMRI activity in the hypothalamus...

Ipsilateral cortical fMRI responses after peripheral nerve damage in rats reflect increased interneuron activity

Pelled, Galit; Bergstrom, Debra A.; Tierney, Patrick L.; Conroy, Richard S.; Chuang, Kai-Hsiang; Yu, David; Leopold, David A.; Walters, Judith R.; Koretsky, Alan P.
Fonte: National Academy of Sciences Publicador: National Academy of Sciences
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.84%
In the weeks following unilateral peripheral nerve injury, the deprived primary somatosensory cortex (SI) responds to stimulation of the ipsilateral intact limb as demonstrated by functional magnetic resonance imaging (fMRI) responses. The neuronal basis of these responses was studied by using high-resolution fMRI, in vivo electrophysiological recordings, and juxtacellular neuronal labeling in rats that underwent an excision of the forepaw radial, median, and ulnar nerves. These nerves were exposed but not severed in control rats. Significant bilateral increases of fMRI responses in SI were observed in denervated rats. In the healthy SI of the denervated rats, increases in fMRI responses were concordant with increases in local field potential (LFP) amplitude and an increased incidence of single units responding compared with control rats. In contrast, in the deprived SI, increases in fMRI responses were associated with a minimal change in LFP amplitude but with increased incidence of single units responding. Based on action potential duration, juxtacellular labeling, and immunostaining results, neurons responding to intact forepaw stimulation in the deprived cortex were identified as interneurons. These results suggest that the increases in fMRI responses in the deprived cortex reflect increased interneuron activity.

Hemodynamic Nonlinearities Affect BOLD fMRI Response Timing and Amplitude

de Zwart, Jacco A; van Gelderen, Peter; Jansma, J Martijn; Fukunaga, Masaki; Bianciardi, Marta; Duyn, Jeff H
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.84%
The interpretation of functional Magnetic Resonance Imaging (fMRI) studies based on Blood Oxygen-Level Dependent (BOLD) contrast generally relies on the assumption of a linear relationship between evoked neuronal activity and fMRI response. While nonlinearities in this relationship have been suggested by a number of studies, it remains unclear to what extent they relate to the neurovascular response and are therefore inherent to BOLD-fMRI. Full characterization of potential vascular nonlinearities is required for accurate inferences about the neuronal system under study. To investigate the extent of vascular nonlinearities, evoked activity was studied in humans with BOLD-fMRI (n=28) and Magnetoencephalography (MEG) (n=5). Brief (600-800 ms) rapidly repeated (1 Hz) visual stimuli were delivered using a stimulation paradigm that minimized neuronal nonlinearities. Nevertheless, BOLD-fMRI experiments showed substantial remaining nonlinearities. The smallest stimulus separation (200-400 ms) resulted in significant response broadening (15-20% amplitude decrease; 10-12% latency increase; 6-14% duration increase) with respect to a linear prediction. The substantial slowing and widening of the response in the presence of preceding stimuli suggests a vascular rather than neuronal origin to the observed non-linearity. This was confirmed by the MEG data...

Multimodal functional imaging using fMRI-informed regional EEG/MEG source estimation

Ou, Wanmei; Nummenmaa, Aapo; Ahveninen, Jyrki; Belliveau, John W.; Hämäläinen, Matti S.; Golland, Polina
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.84%
We propose a novel method, fMRI-Informed Regional Estimation (FIRE), which utilizes information from fMRI in E/MEG source reconstruction. FIRE takes advantage of the spatial alignment between the neural and the vascular activities, while allowing for substantial differences in their dynamics. Furthermore, with a region-based approach, FIRE estimates the model parameters for each region independently. Hence, it can be efficiently applied on a dense grid of source locations. The optimization procedure at the core of FIRE is related to the re-weighted minimum-norm algorithms. The weights in the proposed approach are computed from both the current source estimates and fMRI data, leading to robust estimates in the presence of silent sources in either fMRI or E/MEG measurements. We employ a Monte Carlo evaluation procedure to compare the proposed method to several other joint E/MEG-fMRI algorithms. Our results show that FIRE provides the best trade-off in estimation accuracy between the spatial and the temporal accuracy. Analysis using human E/MEG-fMRI data reveals that FIRE significantly reduces the ambiguities in source localization present in the minimum-norm estimates, and that it accurately captures activation timing in adjacent functional regions.

Semiblind Spatial ICA of fMRI Using Spatial Constraints

Lin, Qiu-Hua; Liu, Jingyu; Zheng, Yong-Rui; Liang, Hualou; Calhoun, Vince D.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em /07/2010 Português
Relevância na Pesquisa
26.84%
Independent component analysis (ICA) utilizing prior information, also called semiblind ICA, has demonstrated considerable promise in the analysis of functional magnetic resonance imaging (fMRI). So far, temporal information about fMRI has been used in temporal ICA or spatial ICA as additional constraints to improve estimation of task-related components. Considering that prior information about spatial patterns is also available, a semiblind spatial ICA algorithm utilizing the spatial information was proposed within the framework of constrained ICA with fixed-point learning. The proposed approach was first tested with synthetic fMRI-like data, and then was applied to real fMRI data from 11 subjects performing a visuomotor task. Three components of interest including two task-related components and the “default mode” component were automatically extracted, and atlas-defined masks were used as the spatial constraints. The default mode network, a set of regions that appear correlated in particular in the absence of tasks or external stimuli and is of increasing interest in fMRI studies, was found to be greatly improved when incorporating spatial prior information. Results from simulation and real fMRI data demonstrate that the proposed algorithm can improve ICA performance compared to a different semiblind ICA algorithm and a standard blind ICA algorithm.

Are functional deficits in concussed individuals consistent with white matter structural alterations: combined FMRI & DTI study

Zhang, K.; Johnson, B.; Pennell, D.; Ray, W.; Sebastianelli, W.; Slobounov, S.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.84%
There is still controversy in the literature whether a single episode of mild traumatic brain injury (MTBI) results in short-term functional and/or structural deficits as well as any induced long-term residual effects. With the inability of traditional structural brain imaging techniques to accurately diagnosis MTBI, there is hope that more advanced applications like functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) will be more specific in diagnosing MTBI. In this study, 15 subjects who have recently suffered from sport-related MTBI and 15 age-matched normal controls underwent both fMRI and DTI to investigate the possibility of traumatic axonal injury associated with functional deficits in recently concussed but asymptomatic individuals. There are several findings of interest. First, MTBI subjects had a more disperse brain activation pattern with additional increases in activity outside of the shared regions of interest (ROIs) as revealed by FMRI blood oxygen level–dependent (BOLD) signals. The MTBI group had additional activation in the left dorsal-lateral prefrontal cortex during encoding phase of spatial navigation working memory task that was not observed in normal controls. Second, neither whole-brain analysis nor ROI analysis showed significant alteration of white matter (WM) integrity in MTBI subjects as evidenced by fractional anisotropy FA (DTI) data. It should be noted...

Quantitative basal CBF and CBF fMRI of rhesus monkeys using three-coil continuous arterial spin labeling

Zhang, Xiaodong; Nagaoka, Tsukasa; Auerbach, Edward J.; Champion, Robbie; Zhou, Lei; Hu, Xiaoping; Duong, Timothy Q.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.84%
A three-coil continuous arterial-spin-labeling technique with a separate neck labeling coil was implemented on a Siemens 3T Trio for quantitative cerebral blood flow (CBF) and CBF fMRI measurements in non-human primates (rhesus monkeys). The optimal labeling power was 2 W, labeling efficiency was 92±2%, and optimal post-labeling delay was 0.8 s. Gray matter (GM) and white matter (WM) were segmented based on T1 maps. Quantitative CBF were obtained in 3 min with 1.5-mm isotropic resolution. Whole-brain average ΔS/S was 1.0–1.5%. GM CBF was 104±3 ml/100 g/min (n=6, SD) and WM CBF was 45±6 ml/100 g/min in isoflurane-anesthetized rhesus monkeys, with the CBF GM/WM ratio of 2.3±0.2. Combined CBF and BOLD (blood-oxygenation-level-dependent) fMRI associated with hypercapnia and hyperoxia were made with 8-s temporal resolution. CBF fMRI responses to 5% CO2 were 59±10% (GM) and 37±4% (WM); BOLD fMRI responses were 2.0±0.4% (GM) and 1.2±0.4% (WM). CBF fMRI responses to 100% O2 were −9.4±2% (GM) and −3.9±2.6% (WM); BOLD responses were 2.4±0.7% (GM) and 0.8±0.2% (WM). The use of a separate neck coil for spin labeling significantly increased CBF signal-to-noise ratio and the use of small receive-only surface coil significantly increased signal-to-noise ratio and spatial resolution. This study sets the stage for quantitative perfusion imaging and CBF fMRI for neurological diseases in anesthetized and awake monkeys.

A Hybrid Machine Learning Method for Fusing fMRI and Genetic Data: Combining both Improves Classification of Schizophrenia

Yang, Honghui; Liu, Jingyu; Sui, Jing; Pearlson, Godfrey; Calhoun, Vince D.
Fonte: Frontiers Research Foundation Publicador: Frontiers Research Foundation
Tipo: Artigo de Revista Científica
Publicado em 25/10/2010 Português
Relevância na Pesquisa
26.84%
We demonstrate a hybrid machine learning method to classify schizophrenia patients and healthy controls, using functional magnetic resonance imaging (fMRI) and single nucleotide polymorphism (SNP) data. The method consists of four stages: (1) SNPs with the most discriminating information between the healthy controls and schizophrenia patients are selected to construct a support vector machine ensemble (SNP-SVME). (2) Voxels in the fMRI map contributing to classification are selected to build another SVME (Voxel-SVME). (3) Components of fMRI activation obtained with independent component analysis (ICA) are used to construct a single SVM classifier (ICA-SVMC). (4) The above three models are combined into a single module using a majority voting approach to make a final decision (Combined SNP-fMRI). The method was evaluated by a fully validated leave-one-out method using 40 subjects (20 patients and 20 controls). The classification accuracy was: 0.74 for SNP-SVME, 0.82 for Voxel-SVME, 0.83 for ICA-SVMC, and 0.87 for Combined SNP-fMRI. Experimental results show that better classification accuracy was achieved by combining genetic and fMRI data than using either alone, indicating that genetic and brain function representing different, but partially complementary aspects...

Effect of Hemodynamic Variability on Granger Causality Analysis of fMRI

Deshpande, Gopikrishna; Sathian, K.; Hu, Xiaoping
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.84%
In this work, we investigated the effect of the regional variability of the hemodynamic response on the sensitivity of Granger causality (GC) analysis of functional magnetic resonance imaging (fMRI) data to neuronal causal influences. We simulated fMRI data by convolving a standard canonical hemodynamic response function (HRF) with local field potentials (LFPs) acquired from the macaque cortex and manipulated the causal influence and neuronal delays between the LFPs, the hemodynamic delays between the HRFs, the signal to noise ratio (SNR) and the sampling period (TR) in order to assess the effect of each of these factors on the detectability of the neuronal delays from GC analysis of fMRI. In our first bivariate implementation, we assumed the worst case scenario of the hemodynamic delay being at the empirical upper limit of its normal physiological range and opposing the direction of neuronal delay. We found that, in the absence of HRF confounds, even tens of milliseconds of neuronal delays can be inferred from fMRI. However, in the presence of HRF delays which opposed neuronal delays, the minimum detectable neuronal delay was hundreds of milliseconds. In our second multivariate simulation, we mimicked the real situation more closely by using a multivariate network of four time series and assumed the hemodynamic and neuronal delays to be unknown and drawn from a uniform random distribution. The resulting accuracy of detecting the correct multivariate network from fMRI was well above chance and was up to 90% with faster sampling. Generically...

Relationship between fMRI-identified regions and neuronal category-selectivity

Bell, Andrew H.; Malecek, Nicholas J.; Morin, Elyse L.; Hadj-Bouziane, Fadila; Tootell, Roger B.H.; Ungerleider, Leslie G.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em 24/08/2011 Português
Relevância na Pesquisa
26.84%
Functional magnetic resonance imaging (fMRI) has been used extensively to identify regions in the inferior temporal (IT) cortex that are selective for categories of visual stimuli. However, comparatively little is known about the neuronal responses relative to these fMRI-defined regions. Here, we compared in non-human primates the distribution and response properties of IT neurons recorded within vs. outside fMRI regions selective for four different visual categories: faces, body-parts, objects, and places. Although individual neurons that preferred each of the four categories were found throughout the sampled regions, they were most concentrated within the corresponding fMRI region, decreasing significantly within 1–4 mm from the edge of these regions. Further, the correspondence between fMRI and neuronal distributions was specific to neurons that increased their firing rates in response to the visual stimuli, but not to neurons suppressed by visual stimuli, suggesting that the processes associated with inhibiting neuronal activity did not contribute strongly to the fMRI signal in this experiment.

Global and local fMRI signals driven by neurons defined optogenetically by type and wiring

Lee, Jin Hyung; Durand, Remy; Gradinaru, Viviana; Zhang, Feng; Goshen, Inbal; Kim, Dae-Shik; Fenno, Lief E.; Ramakrishnan, Charu; Deisseroth, Karl
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em 10/06/2010 Português
Relevância na Pesquisa
26.84%
Despite a rapidly-growing scientific and clinical brain imaging literature based on functional MRI using blood oxygenation level dependent (BOLD)1 signals, it remains controversial if BOLD signals in a particular region can be caused by activation of local excitatory neurons2. This difficult question is central to the interpretation and utility of BOLD, with major significance for fMRI studies in basic research and clinical applications3. Using a novel integrated technology unifying optogenetic4–13 control of inputs with high-field fMRI signal readouts, we show here that specific stimulation of local CaMKIIα-expressing excitatory neurons, either in neocortex or thalamus, elicits positive BOLD signals at the stimulus location with classical kinetics. We also show that optogenetic fMRI (ofMRI) allows visualization of the causal effects of specific cell types defined not only by genetic identity and cell body location, but also by axonal projection target. Finally, we show that ofMRI within the living and intact mammalian brain reveals BOLD signals in downstream targets distant from the stimulus, indicating that this approach can be used to map the global effects of controlling a local cell population. In this respect, unlike both conventional fMRI studies based on correlations14 and fMRI with electrical stimulation which will also directly drive afferent and nearby axons...

Scale-Free Properties of the fMRI Signal during Rest and Task

He, Biyu J.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Publicado em 28/09/2011 Português
Relevância na Pesquisa
26.84%
It has recently been shown that a significant portion of brain electrical field potentials consists of scale-free dynamics. These scale-free brain dynamics contain complex spatiotemporal structures and are modulated by task performance. Here we show that the fMRI signal recorded from the human brain is also scale-free; its power-law exponent differentiates between brain networks, and correlates with fMRI signal variance and brain glucose metabolism. Importantly, in parallel to brain electrical field potentials, the variance and power-law exponent of the fMRI signal decrease during task activation, suggesting that the signal contains more long-range memory during rest and conversely is more efficient at online information processing during task. Remarkably, similar changes also occurred in task-deactivated brain regions, revealing the presence of an optimal dynamic range in the fMRI signal. The scale-free properties of the fMRI signal and brain electrical field potentials bespeak their respective stationarity and nonstationarity. This suggests that neurovascular coupling mechanism is likely to contain a transformation from nonstationarity to stationarity. In summary, our results demonstrate the functional relevance of scale-free properties of the fMRI signal and impose constraints on future models of neurovascular coupling.

Error-related processing following severe traumatic brain injury: An event-related functional magnetic resonance imaging (fMRI) study

Sozda, Christopher N.; Larson, Michael J.; Kaufman, David A.S.; Schmalfuss, Ilona M.; Perlstein, William M.
Fonte: PubMed Publicador: PubMed
Tipo: Artigo de Revista Científica
Português
Relevância na Pesquisa
26.84%
Continuous monitoring of one’s performance is invaluable for guiding behavior towards successful goal attainment by identifying deficits and strategically adjusting responses when performance is inadequate. In the present study, we exploited the advantages of event-related functional magnetic resonance imaging (fMRI) to examine brain activity associated with error-related processing after severe traumatic brain injury (sTBI). fMRI and behavioral data were acquired while 10 sTBI participants and 12 neurologically-healthy controls performed a task-switching cued-Stroop task. fMRI data were analyzed using a random-effects whole-brain voxel-wise general linear model and planned linear contrasts. Behaviorally, sTBI patients showed greater error-rate interference than neurologically-normal controls. fMRI data revealed that, compared to controls, sTBI patients showed greater magnitude error-related activation in the anterior cingulate cortex (ACC) and an increase in the overall spatial extent of error-related activation across cortical and subcortical regions. Implications for future research and potential limitations in conducting fMRI research in neurologically-impaired populations are discussed, as well as some potential benefits of employing multimodal imaging (e.g....

ICA-fNORM: Spatial Normalization of fMRI Data Using Intrinsic Group-ICA Networks

Khullar, Siddharth; Michael, Andrew M.; Cahill, Nathan D.; Kiehl, Kent A.; Pearlson, Godfrey; Baum, Stefi A.; Calhoun, Vince D.
Fonte: Frontiers Research Foundation Publicador: Frontiers Research Foundation
Tipo: Artigo de Revista Científica
Publicado em 17/11/2011 Português
Relevância na Pesquisa
26.84%
A common pre-processing challenge associated with group level fMRI analysis is spatial registration of multiple subjects to a standard space. Spatial normalization, using a reference image such as the Montreal Neurological Institute brain template, is the most common technique currently in use to achieve spatial congruence across multiple subjects. This method corrects for global shape differences preserving regional asymmetries, but does not account for functional differences. We propose a novel approach to co-register task-based fMRI data using resting state group-ICA networks. We posit that these intrinsic networks (INs) can provide to the spatial normalization process with important information about how each individual’s brain is organized functionally. The algorithm is initiated by the extraction of single subject representations of INs using group level independent component analysis (ICA) on resting state fMRI data. In this proof-of-concept work two of the robust, commonly identified, networks are chosen as functional templates. As an estimation step, the relevant INs are utilized to derive a set of normalization parameters for each subject. Finally, the normalization parameters are applied individually to a different set of fMRI data acquired while the subjects performed an auditory oddball task. These normalization parameters...

Single-Unit Recordings in the Macaque Face Patch System Reveal Limitations of fMRI MVPA

Dubois, Julien; Otto de Berker, Archy; Tsao, Doris Ying
Fonte: Society for Neuroscience Publicador: Society for Neuroscience
Tipo: Article; PeerReviewed Formato: application/pdf
Publicado em 11/02/2015 Português
Relevância na Pesquisa
26.87%
Multivariate pattern analysis (MVPA) of fMRI data has become an important technique for cognitive neuroscientists in recent years; however, the relationship between fMRI MVPA and the underlying neural population activity remains unexamined. Here, we performed MVPA of fMRI data and single-unit data in the same species, the macaque monkey. Facial recognition in the macaque is subserved by a well characterized system of cortical patches, which provided the test bed for our comparison. We showed that neural population information about face viewpoint was readily accessible with fMRI MVPA from all face patches, in agreement with single-unit data. Information about face identity, although it was very strongly represented in the populations of units of the anterior face patches, could not be retrieved from the same data. The discrepancy was especially striking in patch AL, where neurons encode both the identity and viewpoint of human faces. From an analysis of the characteristics of the neural representations for viewpoint and identity, we conclude that fMRI MVPA cannot decode information contained in the weakly clustered neuronal responses responsible for coding the identity of human faces in the macaque brain. Although further studies are needed to elucidate the relationship between information decodable from fMRI multivoxel patterns versus single-unit populations for other variables in other brain regions...

Imaging schizophrenia: data fusion approaches to characterize and classify

Michael, Andrew M.
Fonte: Rochester Instituto de Tecnologia Publicador: Rochester Instituto de Tecnologia
Tipo: Dissertação
Português
Relevância na Pesquisa
26.87%
Schizophrenia is a complex, chronic and disabling mental disorder that affects about one percent of the adult population. The etiology of schizophrenia remains elusive and to date there are no image based tools to diagnose it. Advancements in magnetic resonance imaging (MRI) have enabled researchers to develop less invasive and in vivo techniques, such as structural MRI (sMRI), functional MRI (fMRI) and diffusion tensor imaging (DTI), to construct theories about the neural underpinnings of schizophrenia. With sMRI, fMRI and DTI the distribution of tissues, the functional activity and the brain network are imaged respectively. Subjects with schizophrenia (SZ) and healthy controls (HC) are scanned with different modalities to identify differences, but the analysis of each modality has traditionally been carried out separately. Data fusion of multimodal data and an analysis of the joint information may hold the key to reveal hidden traces of this subtle disorder. In this work we develop techniques to correlate sMRI with fMRI, fMRI with other fMRI and DTI with symptom scores. The brain is a highly interconnected organ and local morphology can influence functional activity at distant regions. Through our methods it is possible to perform a cross correlation analysis between modalities incorporating all brain voxels. By reducing the large cross correlation matrix to useful statistics new aspects of schizophrenia are revealed. The methods introduced are simple...