1,502 research outputs found
Detecting the subtle shape differences in hemodynamic responses at the group level
The nature of the hemodynamic response (HDR) is still not fully understood due to the multifaceted processes involved. Aside from the overall amplitude, the response may vary across cognitive states, tasks, brain regions, and subjects with respect to characteristics such as rise and fall speed, peak duration, undershoot shape, and overall duration. Here we demonstrate that the fixed-shape or adjusted-shape methods may fail to detect some shape subtleties. In contrast, the estimated-shape method (ESM) through multiple basis functions can provide the opportunity to identify some subtle shape differences and achieve higher statistical power at both individual and group levels. Previously, some dimension reduction approaches focused on the peak magnitude, or made inferences based on the area under the curve or interaction, which can lead to potential misidentifications. By adopting a generic framework of multivariate modeling (MVM), we showcase a hybrid approach that is validated by simulations and real data. Unlike the few analyses that were limited to main effect, two- or three-way interactions, we extend the approach to an inclusive platform that is more adaptable than the conventional GLM, achieving a practical equipoise among representation, false positive control, statistical power, and modeling flexibility
Group-level impacts of within- and between-subject hemodynamic variability in fMRI
International audienceInter-subject fMRI analyses have specific issues regarding the reliability of the results concerning both the detection of brain activation patterns and the estimation of the underlying dynamics. Among these issues lies the variability of the hemodynamic response function (HRF), that is usually accounted for using functional basis sets in the general linear model context. Here, we use the joint detection-estimation approach (JDE) (Makni et al., 2008; Vincent et al., 2010) which combines regional nonparametric HRF inference with spatially adaptive regularization of activation clusters to avoid global smoothing of fMRI images. We show that the JDE-based inference brings a significant improvement in statistical sensitivity for detecting evoked activity in parietal regions. In contrast, the canonical HRF associated with spatially adaptive regularization is more sensitive in other regions, such as motor cortex. This different regional behavior is shown to reflect a larger discrepancy of HRF with the canonical model. By varying parallel imaging acceleration factor, SNR-specific region-based hemodynamic parameters (activation delay and duration) were extracted from the JDE inference. Complementary analyses highlighted their significant departure from the canonical parameters and the strongest between-subject variability that occurs in the parietal region, irrespective of the SNR value. Finally, statistical evidence that the fluctuation of the HRF shape is responsible for the significant change in activation detection performance is demonstrated using paired t-tests between hemodynamic parameters inferred by GLM and JDE
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Estimating Neural Signal Dynamics in the Human Brain
Although brain imaging methods are highly effective for localizing the effects of neural activation throughout the human brain in terms of the blood oxygenation level dependent (BOLD) response, there is currently no way to estimate the underlying neural signal dynamics in generating the BOLD response in each local activation region (except for processes slower than the BOLD time course). Knowledge of the neural signal is critical if spatial mapping is to progress to the analysis of dynamic information flow through the cortical networks as the brain performs its tasks. We introduce an analytic approach that provides a new level of conceptualization and specificity in the study of brain processing by non-invasive methods. This technique allows us to use brain imaging methods to determine the dynamics of local neural population responses to their native temporal resolution throughout the human brain, with relatively narrow confidence intervals on many response properties. The ability to characterize local neural dynamics in the human brain represents a significant enhancement of brain imaging capabilities, with potential applications ranging from general cognitive studies to assessment of neuropathologies
Vector-Based Approach for the Detection of Initial Dips Using Functional Near-Infrared Spectroscopy
Functional near-infrared spectroscopy (fNIRS) is a non-invasive method for the detection of local brain activity using changes in the local levels of oxyhemoglobin (oxyHb) and deoxyhemoglobin (deoxyHb). Simultaneous measurement of the levels of oxyHb and deoxyHb is an advantage of fNIRS over other modalities. This review provides a historical description of the physiological problems involved in the accurate identification of local brain activity using fNIRS. The need for improved spatial and temporal identification of local brain activity is described in terms of the physiological challenges of task selection and placement of probes. In addition, this review discusses challenges with data analysis based on a single index, advantages of the simultaneous analysis of multiple indicators, and recently established composite indicators. The vector-based approach provides quantitative imaging of the phase and intensity contrast for oxygen exchange responses in a time series and may detect initial dips related to neuronal activity in the skull. The vector plane model consists of orthogonal vectors of oxyHb and deoxyHb. Initial dips are hemodynamic reactions of oxyHb and deoxyHb induced by increased oxygen consumption in the early tasks of approximately 2–3 seconds. The new analytical concept of fNIRS, able to effectively detect initial dips, may extend further the clinical and social applications of fNIRS
THE RELATIONSHIP BETWEEN P300 EVOKED POTENTIALS AND PREFRONTAL CORTEX OXYGEN USE: A COMBINED EEG AND NIRS STUDY
The P300 subcomponent, P3b, is an event related potential detected at the scalp surface when a working memory comparison results in differences between the contents of working memory and incoming stimulus information. Previous research has indicated that as infrequent targets become more difficult to detect (morphologically similar to a frequent non-target stimulus) the P300 becomes attenuated. fMRI research has also indicated increased prefrontal cortex (PFC) activity during P300 generation. To examine the relationship between P3b amplitude and PFC activity participants performed an easy and difficult target detection task in both EEG and NIRS called the oddball. The EEG and behavioral results confirmed prior reports that difficult to detect targets result in attenuated P3b amplitude, as well as increased misses and reaction time, in comparison to easy to detect targets. NIRS results indicated that detection of targets generally lead to greater increases in oxygenated hemoglobin and decreases in deoxygenated hemoglobin in lateral compared to medial optodes. Additionally, oxygenated hemoglobin increased in the right medial PFC in easy compared to difficult conditions. Taken together, the results of this study and theories behind P3b attenuation suggest that the right medial PFC is involved in attention to salient stimulus features (bottom-up attention) and the lateral PFC is involved in sustained attention to the task (top-down attention). Thus, P3b attenuation is reflective of delimiting attention to salient features and allowing task driven attention to initiate the working memory comparison
Deconvolution of the Functional Ultrasound Response in the Mouse Visual Pathway Using Block-Term Decomposition
Functional ultrasound (fUS) indirectly measures brain activity by recording
changes in cerebral blood volume and flow in response to neural activation.
Conventional approaches model such functional neuroimaging data as the
convolution between an impulse response, known as the hemodynamic response
function (HRF), and a binarized representation of the input (i.e., source)
signal based on the stimulus onsets, the so-called experimental paradigm (EP).
However, the EP may not be enough to characterize the whole complexity of the
underlying source signals that evoke the hemodynamic changes, such as in the
case of spontaneous resting state activity. Furthermore, the HRF varies across
brain areas and stimuli. To achieve an adaptable framework that can capture
such dynamics and unknowns of the brain function, we propose a deconvolution
method for multivariate fUS time-series that reveals both the region-specific
HRFs, and the source signals that induce the hemodynamic responses in the
studied regions. We start by modeling the fUS time-series as convolutive
mixtures and use a tensor-based approach for deconvolution based on two
assumptions: (1) HRFs are parametrizable, and (2) source signals are
uncorrelated. We test our approach on fUS data acquired during a visual
experiment on a mouse subject, focusing on three regions within the mouse
brain's colliculo-cortical, image-forming pathway: the lateral geniculate
nucleus, superior colliculus and visual cortex. The estimated HRFs in each
region are in agreement with prior works, whereas the estimated source signal
is observed to closely follow the EP. Yet, we note a few deviations from the EP
in the estimated source signal that most likely arise due to the trial-by-trial
variability of the neural response across different repetitions of the stimulus
observed in the selected regions
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