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The variability of human
... Because of the known variability of the HRF across subjects and sessions [50] and even brain regions [51], the EEG features were also convolved with HRF functions peaking at different latencies (3, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 A c c e p t e d M a n u s c r i p t = ⊗ HRF (8) thus resulting in the final model: ...
... Motivated by the known variability of the HRF [50,51], Sato and colleagues [80] followed an approach similar to the "delays" approach used in the EFP model to estimate the transfer function between the EEG and the BOLD signal measured at the visual cortex. Despite their promising results, only two pilot subjects were considered, which limits the generalization of the results. ...
... Nonetheless, the morphology of the estimated transfer functions varied significantly across participants and experiments, thus supporting the 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 A c c e p t e d M a n u s c r i p t convolution of the proposed EEG features with multiple HRF functions peaking at different latencies. For the same number of predictors, this approach outperformed the "delays" and convolution with the canonical HRF approaches, possibly because it not only incorporates the hemodynamic response behavior in the predictor time course, but also addresses the variability of the HRF function, which has already been observed across subjects, sessions and brain regions [50,51]. ...
Objective:
fMRI-based neurofeedback (NF) interventions represent the method of choice for the neuromodulation of localized brain areas. Although we have already validated an fMRI-NF protocol targeting the facial expressions processing network (FEPN), its dissemination is hampered by the economical and logistical constraints of fMRI-NF interventions, which may be however surpassed by transferring it to EEG setups, due to their low cost and portability. One of the major challenges of this procedure is then to reconstruct the BOLD-fMRI signal measured at the FEPN using only EEG signals. Because these types of approaches have been poorly explored so far, here we systematically investigated the extent at which the BOLD-fMRI signal recorded from the FEPN during a fMRI-NF protocol could be reconstructed from the simultaneously recorded EEG signal.
Approach:
Several features from both scalp and source spaces (the latter estimated using continuous EEG source imaging) were extracted and used as predictors in a regression problem using random forests. Furthermore, three different approaches to deal with the hemodynamic delay of the BOLD signal where tested. The resulting models where compared with the only approach already proposed in the literature that uses spectral features and considers different time delays.
Main results:
Our results show that the combination of different kinds of features extracted from the scalp, and convolving them with multiple HRF functions peaking at different latencies, increases significantly the reconstruction accuracy (defined as the correlation between the measured and reconstructed BOLD signal) from 20% (the current literature) to 52%.
Significance:
With this pipeline, a more accurate reconstruction of the BOLD signal can be obtained, which will positively impact the transfer of fMRI-based neurofeedback interventions to EEG setups, and more importantly, their dissemination and efficacy in modulating the activity of the desired brain areas.
... 1 Most studies assume a standard whole-brain canonical HRF during analysis (typically made of 2 gamma functions), although previous works show HRF variability for different brain regions and across subjects. [2][3][4][5] The variability of non-neural components of HRF across the brain as well as across individuals 2,3 is problematic. Since only neural activity is of interest in most fMRI studies, interpretation of fMRI findings is often clouded because of the aforementioned non-neural sources of variability in fMRI. ...
... 5,[22][23][24][25][26][27][28][29] For the sake of argument, however, even if we were to say that our estimated HRFs post deconvolution were largely inaccurate, it is still undeniable that, theoretically, the ground-truth HRF varies considerably across the brain, across individuals, and across disease groups. 2,3 Our findings, in the worst case, at least illustrate how HRF variability can result in widespread confounds in FC estimates. To illustrate this point, we picked 25% of all the connections that exhibited the least difference in the HRF parameters between the corresponding regions and F IGUR E 6 Individual connectivity estimates of a connectivity path (the pseudo-positive connection with highest T-value taken as an example here). ...
... 40 These neurochemical processes are known to vary across the brain, with larger differences being more likely between distinct and distant regions. 2,3 Vasculature is also inconsistent across the brain, hence the HRF would be different between brain regions neighboring larger blood vessels compared to smaller ones. 3 However, it is to be noted that this is a simplistic explanation of much complex underlying neurochemical and neurovascular phenomena. ...
Purpose:
fMRI is the convolution of the hemodynamic response function (HRF) and unmeasured neural activity. HRF variability (HRFv) across the brain could, in principle, alter functional connectivity (FC) estimates from resting‐state fMRI (rs‐fMRI). Given that HRFv is driven by both neural and non‐neural factors, it is problematic when it confounds FC. However, this aspect has remained largely unexplored even though FC studies have grown exponentially. We hypothesized that HRFv confounds FC estimates in the brain's default‐mode‐network.
Methods:
We tested this hypothesis using both simulations (where the ground truth is known and modulated) as well as rs‐fMRI data obtained in a 7T MRI scanner (N = 47, healthy). FC was obtained using 2 pipelines: data with hemodynamic deconvolution (DC) to estimate the HRF and minimize HRFv, and data with no deconvolution (NDC, HRFv‐ignored). DC and NDC FC networks were compared, along with regional HRF differences, revealing potential false connectivities that resulted from HRFv.
Results:
We found evidence supporting our hypothesis using both simulations and experimental data. With simulations, we found that HRFv could cause a change of up to 50% in FC. With rs‐fMRI, several potential false connectivities attributable to HRFv, with majority connections being between different lobes, were identified. We found a double exponential relationship between the magnitude of HRFv and its impact on FC, with a mean/median error of 30.5/11.5% caused in FC by HRF confounds.
Conclusion:
HRFv, if ignored, could cause identification of false FC. FC findings from HRFv‐ignored data should be interpreted cautiously. We suggest deconvolution to minimize HRFv.
... To enable linear analysis, the HRF is often assumed to be sufficiently stereotypical that a standard form can be used as a neurovascular impulse response. Principal component analysis (PCA) has demonstrated a moderately unimodal character to the HRF ( Aguirre et al., 1998;Friman et al., 2003;Steffener et al., 2010;Woolrich et al., 2001). To match the temporal profile of the HRF, other works have suggested a heuristic, double gamma model for the HRF ( Friston et al., 1998;Glover, 1999;Handwerker et al., 2004) that has been commonly used for fMRI analysis. ...
... To enable linear analysis, the HRF is often assumed to be suffi- ciently stereotypical that a standard form can be used as a neurovascu- lar impulse response. Principal component analysis (PCA) has demon- strated a moderately unimodal character to the HRF ( Aguirre et al., 1998;Friman et al., 2003;Steffener et al., 2010;Woolrich et al., 2001). To match the temporal profile of the HRF, other works have suggested a heuristic, double gamma model for the HRF ( Friston et al., 1998;Glover, 1999;Handwerker et al., 2004) that has been commonly used for fMRI analysis. ...
... Some experiments have shown noticeable variation of the HRF across subjects and sessions (Aguirre et al., 1998;Fransson et al., 1999;Handwerker et al., 2004;Miezin et al., 2000). They found significant variation in HRF magnitude and shape across specific regions of interest and even greater variation between subjects. ...
A brief (<4 s) period of neural activation evokes a stereotypical sequence of vascular and metabolic events to create the hemodynamic response function (HRF) measured using functional magnetic resonance imaging (fMRI). Linear analysis of fMRI data requires that the HRF be treated as an impulse response, so the character and temporal stability of the HRF are critical issues. Here, a simple audiovisual stimulus combined with a fast-paced task was used to evoke a strong HRF across a majority, ∼77%, of cortex during a single scanning session. High spatiotemporal resolution (2-mm voxels, 1.25-s acquisition time) was used to focus HRF measurements specifically on the gray matter for whole brain. The majority of activated cortex responds with positive HRFs, while ∼27% responds with negative (inverted) HRFs. Spatial patterns of the HRF response amplitudes were found to be similar across subjects. Timing of the initial positive lobe of the HRF was relatively stable across the cortical surface with a mean of 6.1 ± 0.6 s across subjects, yet small but significant timing variations were also evident in specific regions of cortex. The results provide guidance for linear analysis of fMRI data. More importantly, this method provides a means to quantify neurovascular function across most of the brain, with potential clinical utility for the diagnosis of brain pathologies such as traumatic brain injury.
... The variability of BOLD signal is directly related to the limit of detecting the correlation between hemodynamic and behavioral measurements (Aguirre et al., 1998; D'Esposito et al., 1999; Handwerker et al., 2004; Huettel and McCarthy, 2001). This variability also poses the limit of detecting information flows across brain regions using inter-regional hemodynamic measures. ...
... Across subjects and across locations at the human visual cortex, the variability of the time-to-peak (TTP) of the BOLD signal was found around 0.52 s (standard deviation) at human visual cortex; the intra-subject TTP variability was slightly larger (0.79 s) (de Zwart et al., 2005). However, the intra-subject variability of the BOLD signal was found smaller than the inter-subject variability at the human visual cortex in another study (Leontiev and Buxton, 2007) and at the human motor cortex (Aguirre et al., 1998). By further separating the intra-subject variability into the contribution across days and across sessions on the same day, it was found that the latter was more stable with a measured variability of a fraction of a second (Aguirre et al., 1998). ...
... However, the intra-subject variability of the BOLD signal was found smaller than the inter-subject variability at the human visual cortex in another study (Leontiev and Buxton, 2007) and at the human motor cortex (Aguirre et al., 1998). By further separating the intra-subject variability into the contribution across days and across sessions on the same day, it was found that the latter was more stable with a measured variability of a fraction of a second (Aguirre et al., 1998). While the BOLD signal is mostly stable within-area, within-subject, and within-session, it still varies significantly across trials (Duann et al., 2002). ...
The blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) signal is a robust surrogate for local neuronal activity. However, it has been shown to vary substantially across subjects, brain regions, and repetitive measurements. This variability represents a limit to the precision of the BOLD response and the ability to reliably discriminate brain hemodynamic responses elicited by external stimuli or behavior that are nearby in time. While the temporal variability of the BOLD signal at human visual cortex has been found in the range of a few hundreds of milliseconds, the spatial distributions of the average and standard deviation of this temporal variability have not been quantitatively characterized. Here we use fMRI measurements with a high sampling rate (10 Hz) to map the latency, intra- and inter-subject variability of the evoked BOLD signal in human primary (V1) visual cortices using an event-related fMRI paradigm. The latency relative to the average BOLD signal evoked by 30 stimuli was estimated to be 0.03 s +/− 0.20 s. Within V1, the absolute value of the relative BOLD latency was found correlated to intra- and inter-subject temporal variability. After comparing these measures to retinotopic maps, we found that locations with V1 areas sensitive to smaller eccentricity have later responses and smaller inter-subject variabilities. These correlations were found from data with either short inter-stimulus interval (ISI; average 4 s) or long ISI (average 30 s). Maps of the relative latency as well as inter-/intra-subject variability were found visually asymmetric between hemispheres. Our results suggest that the latency and variability of regional BOLD signal measured with high spatiotemporal resolution may be used to detect regional differences in hemodynamics to inform fMRI studies. However, the physiological origins of timing index distributions and their hemispheric asymmetry remain to be investigated.
... If the stimulus events are repeated identically and separated in time by a sufficient amount, the experimenter can assume that the hemodynamic response will be the same each time for a particular cortical region. Experiments have shown that the hemodynamic response function (HRF) for primary sensory stimuli is consistent in a given region over different trials for the same subject, implying time-invariance (Kim et al 1997;Aguirre et al. 1998;Miezin et al. 2000). However, if a stimulus presentation parameter changes, the experimenter cannot predict the changes that will occur in the HRF. ...
... Several potential sources of error exist for this analysis. It was assumed that all of the voxels in a particular region would produce similar BOLD responses, as reported by others (Aguirre et al. 1998;Miezin et al. 2000). However, it is possible that this is not true, as some researchers have reported a variation of nonlinearity on a voxelwise basis (Birn et al. 2001;Pfeuffer et al. 2003). ...
... The shape of the BOLD response has been found to be consistent across trials for single subjects but can vary across different subjects (Kim et al 1997;Aguirre et al. 1998;Miezin et al. 2000). Therefore, a single model of the hemodynamic curve cannot be used to accurately predict the BOLD response for all subjects. ...
... This has lead to the general characterization of restingstate signals by low-frequency oscillations, typically in the frequency range of 0.01-0.1 Hz (Kalcher et al., 2012;Margulies et al., 2010;Thomas Yeo et al., 2011). However, it is unclear whether oscillations may be present also in higher signal frequencies (i.e., above 0.1 Hz) (Boubela et al., 2013;Lee et al., 2013a;Niazy et al., 2011), or whether high-frequency oscillations consist of noise only (Aguirre et al., 1998;Cunnington et al., 2002). To increase the information content of highand low-frequency oscillations (Feinberg et al., 2010;Smith et al., 2012), a high temporal resolution is necessary. ...
... Together, the data suggest that high-and low-frequency hemodynamic oscillations contribute differently, possibly even independently, to the magnitude and phase angle of coherence. Our data therefore support previous studies that characterized resting-state by low-frequency oscillations (Kalcher et al., 2012;Margulies et al., 2010;Thomas Yeo et al., 2011), but do not support a contribution of high signal frequencies (Aguirre et al., 1998;Boubela et al., 2013;Cunnington et al., 2002;Lee et al., 2013a;Niazy et al., 2011). A last aspect should be mentioned. ...
... As for the HRF type factor, several strategies have been proposed (14,32,35) that try to improve analysis sensitivity; it is also important to determine which of these results are most reliable. Previous studies have shown that measured HRFs vary among subjects and among regions within the same subject (36,37). This indicates that special considerations are needed for modeling the HRF. ...
... Previous studies (36,45) on healthy subjects have reported that there is HRF variability among subjects, with variations larger than variations between brain regions (37). Such findings were confirmed by our previous study (14) with MREG. ...
Purpose:
Recent studies have applied the new magnetic resonance encephalography (MREG) sequence to the study of interictal epileptic discharges (IEDs) in the electroencephalogram (EEG) of epileptic patients. However, there are no criteria to quantitatively evaluate different processing methods, to properly use the new sequence.
Methods:
We evaluated different processing steps of this new sequence under the common generalized linear model (GLM) framework by assessing the reliability of results. A bootstrap sampling technique was first used to generate multiple replicated data sets; a GLM with different processing steps was then applied to obtain activation maps, and the reliability of these maps was assessed.
Results:
We applied our analysis in an event-related GLM related to IEDs. A higher reliability was achieved by using a GLM with head motion confound regressor with 24 components rather than the usual 6, with an autoregressive model of order 5 and with a canonical hemodynamic response function (HRF) rather than variable latency or patient-specific HRFs. Comparison of activation with IED field also favored the canonical HRF, consistent with the reliability analysis.
Conclusion:
The reliability analysis helps to optimize the processing methods for this fast fMRI sequence, in a context in which we do not know the ground truth of activation areas. Magn Reson Med, 2016. © 2016 International Society for Magnetic Resonance in Medicine.
... The temporal profile of this delay is known as the hemodynamic response function (HRF). In adults, if a stimulus evokes a short burst of neural activity there is a peak in the HRF around 5 seconds later [12]. In infants the HRF has a different size and shape (Fig 1A), although there has not yet emerged a consensus across studies using fMRI [13,14,15], near-infrared spectroscopy (NIRS) [16,17,18], and optical imaging [19]. ...
... The exponent x has been taken to be 0.67 [26] and 1 [12,25,27]. We calculated the power spectrum for each grey matter voxel, and then averaged this across voxels. ...
The development of brain function in young infants is poorly understood. The core challenge is that infants have a limited behavioral repertoire through which brain function can be expressed. Neuroimaging with fMRI has great potential as a way of characterizing typical development, and detecting abnormal development early. But, a number of methodological challenges must first be tackled to improve the robustness and sensitivity of neonatal fMRI. A critical one of these, addressed here, is that the hemodynamic response function (HRF) in pre-term and term neonates differs from that in adults, which has a number of implications for fMRI. We created a realistic model of noise in fMRI data, using resting-state fMRI data from infants and adults, and then conducted simulations to assess the effect of HRF of the power of different stimulation protocols and analysis assumptions (HRF modeling). We found that neonatal fMRI is most powerful if block-durations are kept at the lower range of those typically used in adults (full on/off cycle duration 25-30s). Furthermore, we show that it is important to use the age-appropriate HRF during analysis, as mismatches can lead to reduced power or even inverted signal. Where the appropriate HRF is not known (for example due to potential developmental delay), a flexible basis set performs well, and allows accurate post-hoc estimation of the HRF.
... We modeled HIRF as a difference of two gamma functions with six free parameters (Friston et al., 1998;Glover, 1999) by fitting the predicted fMRI time series to the observed time series using a least-square procedure (MATLAB's fminsearch.m). Here, because there is HIRF variability across subjects (Aguirre et al., 1998;Handwerker et al., 2004) and the pRF estimates (especially spatial extents of pRF) depend strongly on the HIRFs (Lage-Castellanos et al., 2020; Lerma-Usabiaga et al., 2020), we derived subject specific HIRF fits. The percent variance explained is 97.68 ± 0.01% (mean ± s.d. ...
An object occupies an enclosed region in the visual field, which defines its spatial extent. Humans display exquisite finesse in spatial extent perception. Recent series of human neuroimaging and monkey single-cell studies suggest the spatial representation encoded in the early visual cortex (EVC) as the neural substrate of spatial extent estimation. Guided by this “EVC hypothesis” on spatial extent estimation, we predicted that human estimation of spatial extents would reflect the topographic biases known to exist in EVC’s spatial representation, the co-axial and radial biases. To test this prediction, we concurrently assessed those two spatial biases in both EVC’s and perceptual spatial representations by probing the anisotropy of EVC’s population receptive fields, on the one hand, and that of humans’ spatial extent estimation, on the other hand. To our surprise, we found a marked topographic mismatch between EVC’s and perceptual representations of oriented visual patterns, the radial bias in the former and the co-axial bias in the latter. Amid this topographic mismatch, the extent to which the anisotropy of spatial extents is modulated by stimulus orientation is correlated across individuals between EVC and perception. Our findings seem to require a revision of the current understanding of EVC’s functional architecture and contribution to visual perception: EVC’s spatial representation (i) is governed by the radial bias but only weakly modulated by the co-axial bias, and (ii) do contribute to spatial extent perception, but in a limited way where additional neural mechanisms are called in to counteract the radial bias in EVC.
Significant statement
Previous anatomical and functional studies suggest both radial and co-axial biases as topographic factors governing the spatial representation of the early visual cortex (EVC). On the other hand, EVC’s fine-grained spatial representation has been considered the most plausible neural substrate for exquisite human perception of spatial extents. Based on these suggestions, we reasoned that these two topographic biases are likely to be shared between EVC’s and perceptual representations of spatial extents. However, our neuroimaging and psychophysics experiments implicate a need for revising those two suggestions. Firstly, the co-axial bias seems to exert only a modulatory influence on EVC’s functional architecture. Secondly, human spatial extent perception requires further contribution from neural mechanisms that correct EVC’s spatial representation for its radial bias.
... If changing context influences the hemodynamic response function (HRF), then this would in turn influence activity estimates, producing apparent context-sensitivity even if the underlying neural representation is fully invariant. Factors known to influence the HRF include stimulus duration [51], separate scans [52], inter-trial interval [53], stress level [54], and levels of some neurotransmitters [55][56][57]. Exploring which changes in the HRF could influence the results of tests against invariance is beyond the scope of this work, but researchers should design their studies so that factors known to influence the HRF do not co-vary with changes in context. ...
Many research questions in sensory neuroscience involve determining whether the neural representation of a stimulus property is invariant or specific to a particular stimulus context (e.g., Is object representation invariant to translation? Is the representation of a face feature specific to the context of other face features?). Between these two extremes, representations may also be context-tolerant or context-sensitive. Most neuroimaging studies have used operational tests in which a target property is inferred from a significant test against the null hypothesis of the opposite property. For example, the popular cross-classification test concludes that representations are invariant or tolerant when the null hypothesis of specificity is rejected. A recently developed neurocomputational theory suggests two insights regarding such tests. First, tests against the null of context-specificity, and for the alternative of context-invariance, are prone to false positives due to the way in which the underlying neural representations are transformed into indirect measurements in neuroimaging studies. Second, jointly performing tests against the nulls of invariance and specificity allows one to reach more precise and valid conclusions about the underlying representations, particularly when the null of invariance is tested using the fine-grained information from classifier decision variables rather than only accuracies (i.e., using the decoding separability test). Here, we provide empirical and computational evidence supporting both of these theoretical insights. In our empirical study, we use encoding of orientation and spatial position in primary visual cortex as a case study, as previous research has established that these properties are encoded in a context-sensitive way. Using fMRI decoding, we show that the cross-classification test produces false-positive conclusions of invariance, but that more valid conclusions can be reached by jointly performing tests against the null of invariance. The results of two simulations further support both of these conclusions. We conclude that more valid inferences about invariance or specificity of neural representations can be reached by jointly testing against both hypotheses, and using neurocomputational theory to guide the interpretation of results.
... Using customized fmristat software (Aguirre et al., 1998), a twolevel voxel-wise linear mixed-effects model was utilized to examine the effect sizes of the key Group/Condition contrasts in an ANCOVA setting. First, a voxel-wise multiple linear regression model was employed at the individual subject level. ...
Background:
The bed nucleus of the stria terminalis (BNST) plays an important role in rodent posttraumatic stress disorder (PTSD), but evidence to support its relevance to human PTSD is limited. We sought to understand the role of the BNST in human PTSD via fMRI, behavioral, and physiological measurements.
Methods:
29 patients with PTSD (childhood sexual abuse) and 23 healthy controls (HC) underwent BOLD imaging with an emotional word paradigm. Symptom severity was assessed using the Clinician-Administered PTSD Scale and HPA-axis dysfunction was assessed by measuring the diurnal cortisol amplitude index (DCAI). A data-driven multivariate analysis was used to determine BNST task-based functional co-occurrence (tbFC) across individuals.
Results:
In the trauma-versus-neutral word contrast, patients showed increased activation compared to HC in the BNST, medial prefrontal cortex (mPFC), posterior cingulate gyrus (PCG), caudate heads, and midbrain, and decreased activation in dorsolateral prefrontal cortex (DLPFC). Symptom severity positively correlated with activity in the BNST, caudate head, amygdala, hippocampus, dorsal anterior cingulate gyrus (dACG), and PCG, and negatively with activity in the medial orbiotofrontal cortex (mOFC) and DLPFC. Patients and HC showed marked differences in the relationship between the DCAI and BOLD activity in the BNST, septal nuclei, dACG, and PCG. Patients showed stronger tbFC between the BNST and closely linked limbic and subcortical regions, and a loss of negative tbFC between the BNST and DLPFC.
Conclusions:
Based upon novel data, we present a new model of dysexecutive emotion processing and HPA-axis dysfunction in human PTSD that incorporates the role of the BNST and functionally linked neurocircuitry.
... Finally, a transient change referred to as the undershot can be observed. Maximal variance is observed between subjects and minimal variance between scans of the same subject ( Aguirre et al., 1998). However, within subject variance increases when comparing several areas -i.e. the shape of the hemodynamic response is influenced by the local vasculature which differs from one area to the other. ...
Functional magnetic resonance imaging (fMRI) allows addressing the functional organization of the human brain with minimal invasiveness and in healthy individuals. The implementation of that technique in non-human primates represents an important achievement in systems neuroscience. On the one hand, monkey fMRI contributes to the reduction and refinement of invasive approaches in non-human primates, by revealing the regions of interest in which focal electrophysiological and/or anatomical investigations should be carried out. On the other hand, the knowledge acquired with such invasive approaches can be more safely transposed to humans, once inter-species homologies and differences have been identified through the use of similar fMRI protocols in human and non-human primates. The first part of this thesis reviews the most common approaches that have been used to study brain functions, either in humans or in non-human primates. It is shown that despite progresses in the human approaches, invasive studies in monkeys remain necessary for understanding the neuronal mechanisms underlying cognitive functions. Then follows a description of the evolution of the monkey fMRI techniques and some of its achievements in bridging the gap between non-invasive human studies and invasive animal studies, notably for deciphering the neural mechanisms supporting visually-guided grasping. The end of this first part is purely methodological. It undertakes the description of the monkey facilities and the MR platform in Toulouse, and details the necessary milestones for conducting fMRI research in macaque monkeys. The second part of the thesis presents the 4 studies we have conducted with monkey fMRI. The first study is a preparatory experiment for characterizing the monkey hemodynamic response function, which is a prerequisite for proper analysis of subsequent monkey fMRI data. The second study addresses the visuotopic organization of the primate dorsal visual cortex with a novel technique of wide-field (80°) phase-encoded visual stimulation, coupled with a state of the art surface-based analysis of population receptive fields. The results obtained in 2 animals uncover a new cluster of visuotopic areas in the posterior parietal cortex of the macaque monkey, bringing a fresh view to the functional organization of this piece of cortex and opening a promising avenue for inter-species comparisons. The third study unveils the cortical network involved in optic flow processing in non-human primates and it compares this network to that recently described in humans. To that end, we replicated in macaque monkeys an experiment previously conducted in human subjects with optic flow stimuli that are either consistent or inconsistent with egomotion. Besides confirming the involvement of areas previously identified through electrophysiological recordings, our results reveal new cortical areas involved in the processing of optic flow, drawing the picture of a network sharing many similarities, but also striking differences, with that documented in the human brain. In summary, the ambition of this thesis is two-fold: (1) providing guidelines for setting-up monkey fMRI techniques, drawn from our own experience and (2) exposing a set of studies we have conducted with this approach, dealing with the visuotopic organization of the dorsal visual cortex and its involvement in the processing of visual motion. Besides bringing a fresh view to the functional organization of the dorsal visual pathway in non-human primates, these studies illustrate how monkey fMRI bridges the gap between electrophysiological studies in non-human primates and functional imaging studies in humans.
... To model the inter-and intra-subject variability of real hemodynamic responses, synthetic evoked HRFs had variable size and shape across subjects and channels. While each HRF had the mathematical form of a canonical HRF [6], their peak amplitudes ranged from 0.01 to 0.1 µM [18], while, based on experience and existing literature on the variability of the hemodynamic response [19], the onset-to-peak times ranged from 2 to 8 s and the onset-to-undershoot times ranged from 14 to 18 s. ...
Objective:
The statistical analysis of Functional Near Infrared Spectroscopy (fNIRS) data based on the General Linear Model (GLM) is often made difficult by serial correlations, high inter-subject variability of the hemodynamic response, and the presence of motion artifacts. In this work we propose to extract information on the pattern of hemodynamic activations without using any a priori model for the data, by classifying the channels as "active" or "not active" with a multivariate classifier based on Linear Discriminant Analysis (LDA).
Approach:
This work is developed in two steps. First we compared the performance of the two analyses, using a synthetic approach in which simulated hemodynamic activations were combined with either simulated or real resting-state fNIRS data. This procedure allowed for exact quantification of the classification accuracies of GLM and LDA. In the case of real resting-state data, the correlations between classification accuracy and demographic characteristics were investigated by means of a Linear Mixed Model. In the second step, to further characterize the reliability of the newly proposed analysis method, we conducted an experiment in which participants had to perform a simple motor task and data were analyzed with the LDA-based classifier as well as with the standard GLM analysis.
Main results:
The results of the simulation study show that the LDA-based method achieves higher classification accuracies than the GLM analysis, and that the LDA results are more uniform across different subjects and, in contrast to the accuracies achieved by the GLM analysis, have no significant correlations with any of the demographic characteristics. Findings from the real-data experiment are consistent with the results of the real-plus-simulation study, in that the GLM-analysis results show greater inter-subject variability than do the corresponding LDA results.
Significance:
The results obtained suggest that the outcome of GLM analysis is highly vulnerable to violations of theoretical assumptions, and that therefore a data-driven approach such as that provided by the proposed LDA-based method is to be favored.
... Nevertheless, the aforementioned approach suffers from several limitations. The most prominent among them are: a) It assumes that the HRF function is known; although the HRF varies significantly across different persons as well as across different areas of the same brain [9], in practice, an assumption need to be made. b) The hypothesis testing on whether a voxel is active due to the experimental task or not, requires the proper tuning of a threshold, which ensures statistical significance. ...
In this paper, the task-related fMRI problem is treated in its matrix factorization form, focusing on the Dictionary Learning (DL) approach. The proposed method allows the incorporation of a priori knowledge that is associated with both the experimental design and available brain atlases. Moreover, it can cope efficiently with uncertainties in the modeling of the hemodynamic response function. In addition, the method bypasses one of the major drawbacks of the DL methods; namely, the selection of the sparsity-related regularization parameters. Under the proposed formulation, the associated regularization parameters bear a direct relation to the number of the activated voxels for each one of the sources’ spatial maps. This natural interpretation facilitates fine-tuning of the related parameters and allows for exploiting external information from brain atlases. The proposed method is evaluated against several other popular techniques, including the classical General Linear Model (GLM). The obtained performance gains are quantitatively demonstrated via a novel realistic synthetic fMRI dataset as well as real data from a challenging experimental design.
... s after the end of the stimulus presentation and lasted 2.5 s. This time jitter in the onset of the signal was introduced to account for the inter-subject variability of the BOLD hemodynamic response ( Aguirre et al., 1998) and to introduce variation in the timing of stimulus presentation. Participants performed a 3 AFC task, where they had to identify the target tone by pressing either the first, second or third button on a response box, according to the target's position (i.e., first, second or third tone presented). ...
Musicians are highly trained to discriminate fine pitch changes but the neural bases of this ability are poorly understood. It is unclear whether such training-dependent differences in pitch processing arise already in the subcortical auditory system or are linked to more central stages. To address this question, we combined psychoacoustic testing with functional MRI to measure cortical and subcortical responses in musicians and non-musicians during a pitch-discrimination task. First, we estimated behavioral pitch-discrimination thresholds for complex tones with harmonic components that were either resolved or unresolved in the auditory system. Musicians outperformed non-musicians, showing lower pitch-discrimination thresholds in both conditions. The same participants underwent task-related functional MRI, while they performed a similar pitch-discrimination task. To account for the between-group differences in pitch-discrimination, task difficulty was adjusted to each individual's pitch-discrimination ability. Relative to non-musicians, musicians showed increased neural responses to complex tones with either resolved or unresolved harmonics especially in right-hemispheric areas, comprising the right superior temporal gyrus, Heschl's gyrus, insular cortex, inferior frontal gyrus, and in the inferior colliculus. Both subcortical and cortical neural responses predicted the individual pitch-discrimination performance. However, functional activity in the inferior colliculus correlated with differences in pitch discrimination across all participants, but not within the musicians group alone. Only neural activity in the right auditory cortex scaled with the fine pitch-discrimination thresholds within the musicians. These findings suggest two levels of neuroplasticity in musicians, whereby training-dependent changes in pitch processing arise at the collicular level and are preserved and further enhanced in the right auditory cortex.
... Finally, we provide a few cautionary notes for interpreting the results presented in this report. First, given the confounding effect of the variability of the hemodynamic response [33], [34] on Granger causal estimates obtained from BOLD fMRI [35], [36], it is noteworthy that hemodynamic variability was probably not a factor influencing the results of both the Katwal et al.'s study [3] as well as the current study since left and right visual cortices are likely to have the same hemodynamics as they are fed by a common hemodynamic source. Also the monotonic increase in GC measures with the experimentally-controlled delays denotes a task-related effect and rules out hemodynamics as the cause of our results. ...
Decoding the sequential flow of events in the human brain non-invasively is critical for gaining a mechanistic understanding of brain function. In this study, we propose a method based on dynamic Granger causality analysis to measure timing differences in brain responses from fMRI. We experimentally validate this method by detecting sub-100 ms timing differences in fMRI responses obtained from bilateral visual cortex using fast sampling, ultra-high field and an event-related visual hemifield paradigm with known timing difference between the hemifields. Classical Granger causality was previously shown to be able to detect sub-100 ms timing differences in the visual cortex. Since classical Granger causality does not differentiate between spontaneous and stimulus-evoked responses, dynamic Granger causality has been proposed as an alternative, thereby necessitating its experimental validation. In addition to detecting timing differences as low as 28 ms using dynamic Granger causality, the significance of the inference from our method increased with increasing delay both in simulations and experimental data. Therefore, it provides a methodology for understanding mental chronometry from fMRI in a data-driven way.
... [21][22][23][24][25][26] However, the findings by Passow et al. 20 could reflect regional differences in the very slow pharmacokinetics associated with FDG uptake in the brain (> 10 min) rather than the relatively faster fluctuations in glucose phosphorylation associated with the energy cost of information processing as captured by rfMRI. Furthermore, since t-MC requires global signal normalization, a procedure that could introduce anti-correlations among regions, 27,28 we expected t-MC patterns to be predominantly driven by regional-differences in the pharmacokinetics of FDG rather than by functional interactions between remote brain regions. We studied temporal functional connectivity (t-FC) and t-MC at rest in 53 healthy participants to test these hypotheses. ...
It remains unclear whether resting state functional magnetic resonance imaging (rfMRI) networks are associated with underlying synchrony in energy demand, as measured by dynamic 2-deoxy-2-[ 18 F]fluoroglucose (FDG) positron emission tomography (PET). We measured absolute glucose metabolism, temporal metabolic connectivity (t-MC) and rfMRI patterns in 53 healthy participants at rest. Twenty-two rfMRI networks emerged from group independent component analysis (gICA). In contrast, only two anti-correlated t-MC emerged from FDG-PET time series using gICA or seed-voxel correlations; one included frontal, parietal and temporal cortices, the other included the cerebellum and medial temporal regions. Whereas cerebellum, thalamus, globus pallidus and calcarine cortex arose as the strongest t-MC hubs, the precuneus and visual cortex arose as the strongest rfMRI hubs. The strength of the t-MC linearly increased with the metabolic rate of glucose suggesting that t-MC measures are strongly associated with the energy demand of the brain tissue, and could reflect regional differences in glucose metabolism, counterbalanced metabolic network demand, and/or differential time-varying delivery of FDG. The mismatch between metabolic and functional connectivity patterns computed as a function of time could reflect differences in the temporal characteristics of glucose metabolism as measured with PET-FDG and brain activation as measured with rfMRI.
... Data analysis. For each participant, we identified V1 using standard retinotopic mapping techniques ( Sereno et al., 1995;DeYoe et al., 1996;Engel et al., 1997;Aguirre et al., 1998). Briefly, participants observed four runs of a smoothly rotating checkerboard wedge stimulus and two runs of an expanding/contracting ring stimulus (for full stimulus details, see Mannion et al., 2013), which was analyzed through phase-encoding methods ( Engel, 2012) to establish preferences in the visual field over the cortical surface. ...
Vision can be considered as a process of probabilistic inference. In a Bayesian framework, perceptual estimates from sensory information are combined with prior knowledge, with a stronger influence of the prior when the sensory evidence is less certain. Here, we explored the behavioral and neural consequences of manipulating stimulus certainty in the context of orientation processing. First, we asked participants to judge whether a stimulus was oriented closer to vertical or the clockwise primary oblique (45°) for two stimulus types (spatially filtered noise textures and sinusoidal gratings) and three manipulations of certainty (orientation bandwidth, contrast, and duration).We found that participants consistently had a bias toward reporting orientation as closer to 45° during conditions of high certainty and that this bias was reduced when sensory evidence was less certain. Second, we measured event-related fMRI BOLD responses in human primary visual cortex (V1) and manipulated certainty via stimulus contrast (100% vs 3%).Wethen trained a multivariate classifier on the pattern of responses in V1 to cardinal and primary oblique orientations. We found that the classifier showed a bias toward classifying orientation as oblique at high contrast but categorized a wider range of orientations as cardinal for low-contrast stimuli. Orientation classification based on data from V1 thus paralleled the perceptual biases revealed through the behavioral experiments. This pattern of bias cannot be explained simply by a prior for cardinal orientations.
... We would like to emphasize that the current implementation of rDCM only represents a starting point of development and is subject to three major limitations when compared to the original DCM framework. First, due to the replacement of the hemodynamic forward model with a fixed hemodynamic response function, rDCM does not presently capture the variability in the BOLD signal across brain regions and individuals (Aguirre et al., 1998; Handwerker et al., 2004). Critically, accounting for the inter-regional variability is crucial to avoid confounds when inferring effective connectivity from fMRI data (David et al., 2008; Valdes-Sosa et al., 2011). ...
The development of large-scale network models that infer the effective (directed) connectivity among neuronal populations from neuroimaging data represents a key challenge for computational neuroscience. Dynamic causal models (DCMs) of neuroimaging and electrophysiological data are frequently used for inferring effective connectivity but are presently restricted to small graphs (typically up to 10 regions) in order to keep model inversion computationally feasible. Here, we present a novel variant of DCM for functional magnetic resonance imaging (fMRI) data that is suited to assess effective connectivity in large (whole-brain) networks. The approach rests on translating a linear DCM into the frequency domain and reformulating it as a special case of Bayesian linear regression. This paper derives regression DCM (rDCM) in detail and presents a variational Bayesian inversion method that enables extremely fast inference and accelerates model inversion by several orders of magnitude compared to classical DCM. Using both simulated and empirical data, we demonstrate the face validity of rDCM under different settings of signal-to-noise ratio (SNR) and repetition time (TR) of fMRI data. In particular, we assess the potential utility of rDCM as a tool for whole-brain connectomics by challenging it to infer effective connection strengths in a simulated whole-brain network comprising 66 regions and 300 free parameters. Our results indicate that rDCM represents a computationally highly efficient approach with promising potential for inferring whole-brain connectivity from individual fMRI data.
... Finally, development of better neurovascular coupling hemodynamic response models is likely to play an important role in UHF brainstem fMRI. The assumption of a spatially homogeneous hemodynamic response function (HRF) has been repeatedly challenged in the literature (Handwerker et al., 2004;Aguirre et al., 1998), and more complex basis sets have been introduced in order to account for such variability (see for exampleLindquist et al., 2009). However, the performance of the most widely adopted methods, such as derivative and related models, has been shown to be biased for temporal shifts greater than a few seconds from the canonical response (Lindquist et al., 2009). ...
The human brainstem plays a central role in connecting the cerebrum, the cerebellum and the spinal cord to one another, hosting relay nuclei for afferent and efferent signaling, and providing source nuclei for several neuromodulatory systems that impact central nervous system function. While the investigation of the brainstem with functional or structural magnetic resonance imaging has been hampered for years due to this brain structure's physiological and anatomical characteristics, the field has seen significant advances in recent years thanks to the broader adoption of ultrahigh-field (UHF) MRI scanning. In the present review, we focus on the advantages offered by UHF in the context of brainstem imaging, as well as the challenges posed by the investigation of this complex brain structure in terms of data acquisition and analysis. We also illustrate how UHF MRI can shed new light on the neuroanatomy and neurophysiology underlying different brainstem-based circuitries, such as the central autonomic network and neurotransmitter/neuromodulator systems, discuss existing and foreseeable clinical applications to better understand diseases such as chronic pain and Parkinson's disease, and explore promising future directions for further improvements in brainstem imaging using UHF MRI techniques.
... Given a stimulation protocol, prior knowledge makes it possible to define the expected BOLD response as a parametric hemodynamics response function (HRF), used in a general linear model (GLM) framework [76], resulting in a univariate analysis of the correlation between the real signal and the estimated HRF. These methods require, therefore, an accurate definition of the expected HRF shape, although it may vary significantly in different populations between subjects and between different brain areas [77]. Recent sophisticated HRF models attempt better to capture the complex structure of the BOLD response [78]. ...
The current state of modeling artificial neurons and networks posits a significant problem of incorporating a concept of time into the machine learning infrastructure. At present the concept of time is encoded by transforming other values, such as space, color and depth. However, this approach does not seem to reflect correctly the actual functioning of biological neurons and neural networks and, moreover this leads to the exponential growth of the computing algorithm running time and of an artificially construed neural network. The aim of my doctoral thesis is to explore the concept of liquid state machines (LSM) (a neural network consisting of a large collection of units with recurrent connections) with regard to the above problem. More specifically, our aim was first to explore the correctness of the model and to identify its limitations as a model of biological neural activity; second, to improve the LSM model with regard to its identified limitations and to find another possible solution for time encoding and third, further explore the LSM concept with regard to its practical applications that until now have been very limited. By a series of experiments we were able to prove that LSM as normally defined cannot serve as models for natural neural function and that their parts are very vulnerable to failures. Our research shows that contrary to prior belief LSM are in fact not as robust (able to keep to prescribed functioning) as is necessary for their practical application. Further, our research focused on the improvement of LSM and encoding a time variable. We were able to prove that specifying certain kinds of topological constraints claimed to be reasonably plausible biologically, can restore robustness of the LSM. By adding topological constraints to the random connectivity of the network it was possible to make the network tolerant to internal damage and noise. Additionally we were able to greatly improve generalization capability of LSM by adding a sliding threshold i.e. the ability to accommodate to current input based on the history of previous input to a specific kind of neurons and learning mechanism related to spike-timing-dependent plasticity. Finally, our research focused on practical applications of LSM. Among other things, we were able to use LSM to create a model-free method of analyzing fMRI data in order to predict response of the BOLD (Blood Oxygenation Level Dependent) and to differentiate the between relevant data and noise during prediction. We were also able to use an adapted version of LSM, receiving direct real valued input, for recognition of phoneme signals in a reliable way. We learned that more reliable results are conditioned on normalizing real values of the phonemes, adding a history dependent sliding threshold to all integrate and fir neurons (LIF) in the liquid, and to topological constraints on the network connectivity.
... These imaging parameters were determined by simulations as well as data from a pilot study [34]. Previous studies also demonstrated that an acquisition window of 16 s was sufficient for the ER-fMRI signal to return to the baseline [35,36]. ...
Passband balanced steady state free precession (b-SSFP) fMRI employs the flat portion of the SSFP off-resonance response to obtain microscopic susceptibility changes elicited by changes in blood oxygenation following enhancement in neuronal activity. This technique can reduce geometric distortion and signal dropout while maintaining rapid acquisition and high signal-to-noise ratio (SNR) compared with traditional fMRI techniques. In the study, we developed a novel multi-phase passband b-SSFP fMRI technique that can achieve a spatial resolution of a few mm(3) and a high temporal sampling rate of 50ms per slice at 7 Tesla. This technique was further applied for an event-related (ER) fMRI paradigm. As a comparison, gradient-echo echo-planar imaging (GE-EPI) with similar spatial resolution and temporal sampling rate was carried out for the same ER-fMRI experiment. Experiments with visual cortex stimulation were carried out at 7 Tesla to demonstrate whether the multi-phase b-SSFP technique and GE-EPI are able to differentiate temporal delays in hemodynamic response function (HRF) separated by 100ms in stimulus onset. Consistent with ERP results, the upslope of the HRF of both techniques can differentiate 100ms delay in stimulus onset, with the former showing a lower level of intersubject variability. The present study demonstrated that the multi-phase passband b-SSFP fMRI technique can be applied for resolving neuronal events on the order of 100ms at ultrahigh magnetic fields.
... This study was focused on BOLD changes in the immediate vicinity of the most active icEEG contacts, i.e., BOLD changes within a small volume of brain tissue not expected to exhibit different haemodynamic responses. Furthermore, reports of HRF shape variability are not limited to studies of epileptic activity; it has also been observed in relation to location in the healthy brain, using a relatively constrained basis set (Aguirre et al., 1998), and in relation to various normal stimuli (Grouiller et al., 2010;Handwerker et al., 2004). Analyses of exceptionally high SNR fMRI data of normal brain activation, using a totally unconstrainedhemodynamic kernel basis set, have revealed a wide range of HRF shapes covering almost the entire brain, but with unknown biological meaning (neuronal vs vascular effects) for the deviant ones (Gonzalez-Castillo et al., 2012). ...
In current fMRI studies designed to map BOLD changes related to interictal epileptiform discharges (IED), which are recorded on simultaneous EEG, the information contained in the morphology and field extent of the EEG events is exclusively used for their classification. Usually, a BOLD predictor based on IED onset times alone is constructed, effectively treating all events as identical. We used intracranial EEG (icEEG)-fMRI data simultaneously recorded in humans to investigate the effect of including any of the features: amplitude, width (duration), slope of the rising phase, energy (area under the curve), or spatial field extent (number of contacts over which the sharp wave was observed) of the fast wave of the IED (the sharp wave), into the BOLD model, to better understand the neurophysiological origin of sharp wave-related BOLD changes, in the immediate vicinity of the recording contacts. Among the features considered, the width was the only one found to explain a significant amount of additional variance, suggesting that the amplitude of the BOLD signal depends more on the duration of the underlying field potential (reflected in the sharp wave width) than on the degree of neuronal activity synchrony (reflected in the sharp wave amplitude), and, consequently, that including inter-event variations of the sharp wave width in the BOLD signal model may increase the sensitivity of forthcoming EEG-fMRI studies of epileptic activity.
... This finding highlights the importance of the delay chosen for analysis of breath-hold data, as a standard HRF delay used for subjects of varying ages may inadvertently favor younger subjects when determining active voxels. This may represent a larger problem in cognitive neuroscience research, in which a standard model based on young healthy subjects is regularly used for all subjects (Aguirre et al., 1998; D'Esposito et al., 2003; Handwerker et al., 2004). Such modeling may overlook neural activity in non-typical subjects that induces a measureable but less predictable vascular response. ...
Functional MRI (fMRI) is well-established for the study of brain function in healthy populations, though its clinical application has proven more challenging. Specifically, cerebrovascular reactivity (CVR), which allows the assessment of the vascular response that serves as the basis for fMRI, has been shown to be reduced in healthy aging as well as in a range of diseases, including chronic stroke. However, the timing of when this occurs relative to the stroke event is unclear. We used a breath-hold fMRI task to evaluate CVR across gray matter in a group of acute stroke patients (< 10days from stroke; N=22) to address this question. These estimates were compared with those from both age-matched (N=22) and younger (N=22) healthy controls. As expected, young controls had the greatest mean CVR, as indicated by magnitude and extent of fMRI activation; however, stroke patients did not differ from age-matched controls. Moreover, the ipsilesional and contralesional hemispheres of stroke patients did not differ with respect to any of these measures. These findings suggest that fMRI remains a valid tool within the first few days of a stroke, particularly for group fMRI studies in which findings are compared with healthy subjects of similar age. However, given the relatively high variability in CVR observed in our stroke sample, caution is warranted when interpreting fMRI data from individual patients or a small cohort. We conclude that a breath-hold task can be a useful addition to functional imaging protocols for stroke patients.
... The peak responses from the resulting deconvolved BOLD time series were used as estimates of response strengths to the different stimulus types. The point in time of these peak responses varied from participant to participant (4.5-6 s), most likely due to individual differences in the hemodynamic responses (Aguirre et al., 1998). ...
... Funkční MR navíc nezatěžuje vyšetřované subjekty ionizujícím zářením. Z tohoto důvodu je vhodnější pro longitudinální sledování s opakovanými vyšetřeními u jednoho subjektu [24]. Funkční MR mapuje tytéž lokální procesy jako perfuzní PET, totiž nárůst lokálního metabolizmu a lokální tkáňové perfuze při zvýšení synaptické aktivity v šedé hmotě. ...
Stroke is the leading cause of disability worldwide. Even the adult brain is capable of structural and functional reorganization following stroke, the resulting neural plasticity is assumed to underlie most of the recovery of neurological deficit. Other neuroplastic changes, however, may worsen neurological functions. Development of post-stroke spasticity can be considered an example of such maladaptive plasticity. It is estimated that 20-40% of stroke survivors develop spasticity. Post-stroke spasticity affects functional status and quality of life of patients and represents a significant socioeconomic burden. Therapy of post-stroke spasticity requires team collaboration, treatment strategies consist of physiotherapy and botulinum toxin application. Botulinum toxin type A is currently considered first-line therapy for post-stroke spasticity. In addition to peripheral effects of botulinum toxin on the neuromuscular junction, there is growing evidence of distant effects on the CNS. The results of recent studies using functional magnetic resonance imaging in the chronic stroke patients suggest that botulinum toxin injected into the spastic muscle modulates the abnormal cortical reorganization (maladaptive plasticity).
... It still remains open which HRF to use for EEG-fMRI analysis. There is evidence that HRFs not always follow the same time series and that differences exist between patients, age or brain areas [47,48]. HRFs following focal epileptic spikes may differ from the standard HRF, a canonical HRF which follows short auditory stimuli [49]. ...
Objective:
The present study aims to investigate whether a newly developed fast fMRI called MREG (magnetic resonance encephalography) measures metabolic changes related to interictal epileptic discharges (IED). For this purpose BOLD changes are correlated with the IED distribution and variability.
Methods:
Patients with focal epilepsy underwent EEG-MREG using a 64 channel cap. IED voltage maps were generated using 32 and 64 channels and compared regarding their correspondence to the BOLD response. The extents of IEDs (defined as number of channels with >50% of maximum IED negativity) were correlated with the extents of positive and negative BOLD responses. Differences in inter-spike variability were investigated between interictal epileptic discharges (IED) sets with and without concordant positive or negative BOLD responses.
Results:
17 patients showed 32 separate IED types. In 50% of IED types the BOLD changes could be confirmed by another independent imaging method. The IED extent significantly correlated with the positive BOLD extent (p = 0.04). In 6 patients the 64-channel EEG voltage maps better reflected the positive or negative BOLD response than the 32-channel EEG; in all others no difference was seen. Inter-spike variability was significantly lower in IED sets with than without concordant positive or negative BOLD responses (with p = 0.04).
Significance:
Higher density EEG and fast fMRI seem to improve the value of EEG-fMRI in epilepsy. The correlation of positive BOLD and IED extent could suggest that widespread BOLD responses reflect the IED network. Inter-spike variability influences the likelihood to find IED concordant positive or negative BOLD responses, which is why single IED analysis may be promising.
... One such proxy is behavioral response time, the use of which is predicated on the inference that a noisier brain would have greater behavioral variability and slower information processing (Welford, 1981;Salthouse and Lichty, 1985). Functional magnetic resonance imaging studies define noise as signal variability (Aguirre et al., 1998;D'Esposito et al., 1999;Huettel et al., 2001) (or related information theoretic measures ). ...
Aging is associated with performance decrements across multiple cognitive domains. The neural noise hypothesis, a dominant view of the basis of this decline, posits that aging is accompanied by an increase in spontaneous, noisy baseline neural activity. Here we analyze data from two different groups of human subjects: intracranial electrocorticography from 15 participants over a 38 year age range (15–53 years) and scalp EEG data from healthy younger (20–30 years) and older (60–70 years) adults to test the neural noise hypothesis from a 1/f noise perspective. Many natural phenomena, including electrophysiology, are characterized by 1/f noise. The defining characteristic of 1/f is that the power of the signal frequency content decreases rapidly as a function of the frequency (f) itself. The slope of this decay, the noise exponent (χ), is often <−1 for electrophysiological data and has been shown to approach white noise (defined as χ = 0) with increasing task difficulty. We observed, in both electrophysiological datasets, that aging is associated with a flatter (more noisy) 1/f power spectral density, even at rest, and that visual cortical 1/f noise statistically mediates age-related impairments in visual working memory. These results provide electrophysiological support for the neural noise hypothesis of aging.
... CSs were presented in both the conditioning context and the extinction context in a counterbalanced order across subjects (Fig. 1). This design permitted a within-subjects comparison of contextual effects on fear expression on Day 2, which is maximally efficient for fMRI designs due to the large variance in blood-oxygenated-level-dependent (BOLD) signal across individuals (Aguirre et al., 1998). A jittered inter-stimulus interval (ISI) of 12 ± 2 s was used during all experimental phases. ...
... fASL is more directly coupled with the neuronal activation Variations in CBF as imaged by fASL are more directly coupled with neuronal activation than the BOLD effect [Buxton 2004]. Furthermore, while the variability of the haemodynamic response function observed in BOLD is a clear shortcoming [Aguirre 1998], the fASL response might be less variable [Aguirre 2002]. ...
This thesis deals with the analysis of brain function in Magnetic Resonance Imaging (MRI) using two sequences: BOLD functional MRI (fMRI) and Arterial Spin Labelling (ASL). In this context, group statistical analyses are of great importance in order to understand the general mechanisms underlying a pathology, but there is also an increasing interest towards patient-specific analyses that draw conclusions at the patient level. Both group and patient-specific analyses are studied in this thesis. We first introduce a group analysis in BOLD fMRI for the study of specific language impairment, a pathology that was very little investigated in neuroimaging. We outline atypical patterns of functional activity and lateralisation in language regions. Then, we move forward to patient-specific analysis. We propose the use of robust estimators to compute cerebral blood flow maps in ASL. Then, we analyse the validity of the assumptions underlying standard statistical analyses in the context of ASL. Finally, we propose a new locally multivariate statistical method based on an a contrario approach and apply it to the detection of atypical patterns of perfusion in ASL and to activation detection in BOLD functional MRI.
... 5A and D). These responses serve as a quality control check and verify that our localized responses are consistent with evoked hemodynamic activity (Aguirre et al., 1998). Finally, in order to ascertain how reliably we were able to detect hierarchical responses to speech comprehension in individual subjects and individual runs of data, we created single-subject and single run renderings of the main contrasts (sentences N noise and complex N easy). ...
... The most commonly employed shape is the so-called 'canonical' HRF derived from, and widely used in cognitive fMRI studies (36): it comprises two gamma functions, one accounting for the peak and the other for undershoot. However, significant variation in the shape and onset of the hemodynamic responses has been demonstrated across subjects (37,38) and brain regions (39) and may be influenced by top down and bottom up processing. In epilepsy (particularly generalised discharges) there is a suggestion that the shape of the HRF can deviate from the canonical shape, resulting in significantly decreased sensitivity (40). ...
Brain activity data in general and more specifically in epilepsy can be represented as a matrix that includes measures of electrophysiology, anatomy and behaviour. Each of these sub-matrices has a complex interaction depending upon the brain state i.e., rest, cognition, seizures and interictal periods. This interaction presents significant challenges for interpretation but also potential for developing further insights into individual event types. Successful treatments in epilepsy hinge on unravelling these complexities, and also on the sensitivity and specificity of methods that characterize the nature and localization of underlying physiological and pathological networks. Limitations of pharmacological and surgical treatments call for refinement and elaboration of methods to improve our capability to localise the generators of seizure activity and our understanding of the neurobiology of epilepsy. Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI), by potentially circumventing some of the limitations of EEG in terms of sensitivity, can allow the mapping of haemodynamic networks over the entire brain related to specific spontaneous and triggered epileptic events in humans, and thereby provide new localising information. In this work we review the published literature, and discuss the methods and utility of EEG-fMRI in localising the generators of epileptic activity. We draw on our experience and that of other groups, to summarise the spectrum of information provided by an increasing number of EEG-fMRI case-series, case studies and group studies in patients with epilepsy, for its potential role to elucidate epileptic generators and networks. We conclude that EEG-fMRI provides a multidimensional view that contributes valuable clinical information to localize the epileptic focus with potential important implications for the surgical treatment of some patients with drug-resistant epilepsy, and insights into the resting state and cognitive network dynamics.
... The shape of the evolving hemodynamic response is a function of stimulus intensity, stimulus duration, inter-stimulus interval duration, and incompletely understood interrelationships among neural activity, cerebral metabolic rate of oxygen consumption (CMRO 2 ), cerebral blood flow (CBF), and cerebral blood volume (CBV) (Buxton et al., 1998). If the stimulus trials are repeated identically and separated sufficiently in time, the experimenter can assume that the hemodynamic response will be the same each time in activated cortical regions (Aguirre et al., 1998; Miezin et al., 2000). However, if a stimulus presentation parameter changes, the experimenter cannot make this assumption, but must know the relationship between the stimulus parameter and the hemodynamic response to accurately interpret the functional data. ...
Hemodynamic responses to auditory and visual stimuli and motor tasks were assessed for the nonlinearity of response in each of the respective primary cortices. Five stimulus or task durations were used (1, 2, 4, 8, and 16 s), and five male subjects (aged 19 ± 1.9 years) were imaged. Two tests of linearity were conducted. The first test consisted of using BOLD responses to short stimuli to predict responses to longer stimuli. The second test consisted of fitting ideal impulse response functions to the observed responses for each event duration. Both methods show that the extent of the nonlinearity varies across cortices. Results for the second method indicate that the hemodynamic response is nonlinear for stimuli less than 10 s in the primary auditory cortex, nonlinear for tasks less than 7 s in the primary motor cortex, and nonlinear for stimuli less than 3 s in the primary visual cortex. In addition, neural adaptation functions were characterized that could model the observed nonlinearities.
... In order to mitigate this problem, a solution based on matrix regularization has been proposed. 7,18,20 The sFIR model (2) consists on the addition in (1) of a smoothing or regularization term that introduces priors about a standard HRF response, thus biasing the beta coefficients, deteriorating the estimation in case of the existence of an altered BOLD and breaking the blind principle. Please, confront (7) for the definition of the variables of the regularization term. ...
Finite impulse response (FIR) filters are considered the least constrained option for the blind estimation of the hemodynamic response function (HRF). However, they have a tendency to yield unstable solutions in the case of short-events sequences. There are solutions based on regularization, e.g. smooth FIR (sFIR), but at the cost of a regularization penalty and prior knowledge, thus breaking the blind principle. In this study, we show that spreading codes (scFIR) outperforms FIR and sFIR in short-events sequences, thus enabling the blind and dynamic estimation of the HRF without numerical instabilities and the regularization penalty. The scFIR approach was applied in short-events sequences of simulated and experimental functional magnetic resonance imaging (fMRI) data. In general terms, scFIR performed the best with both simulated and experimental data. While FIR was unable to compute the blind estimation of two simulated target HRFs for the shortest sequences (15 and 31 events) and sFIR yielded shapes barely correlated with the targets, scFIR achieved a normalized correlation coefficient above 0.9. Furthermore, scFIR was able to estimate in a responsive way dynamic changes of the amplitude of a simulated target HRF more accurately than FIR and sFIR. With experimental fMRI data, the ability of scFIR to estimate the real HRF obtained from a training data set was superior in terms of correlation and mean-square error. The use of short-events sequences for the blind estimation of the HRF could benefit patients in terms of scanning time or intensity of magnetic field in clinical tests. Furthermore, short-events sequences could be used, for instance, on the online detection of rapid shifts of visual attention that, according to literature, entails rapid changes in the amplitude of the HRF.
... The resulting trained model extrapolates from the training data to make testable predictions of the brain activity associated with novel text passages with may vary arbitrary in their content. In training this generative model we make minimal prior assumptions about the form of the hemodynamic response that relates neural activity to observed fMRI activity, instead allowing the training procedure to estimate the hemodynamic response separately for each distinct story feature at each distinct voxel; it has been shown that the hemodynamic response varies across different regions of the brain [13]. We also employ a novel approach for combining fMRI data from multiple human subjects, which is robust to small local anatomical variabilities among their brains. ...
Story understanding involves many perceptual and cognitive subprocesses, from perceiving individual words, to parsing sentences, to understanding the relationships among the story characters. We present an integrated computational model of reading that incorporates these and additional subprocesses, simultaneously discovering their fMRI signatures. Our model predicts the fMRI activity associated with reading arbitrary text passages, well enough to distinguish which of two story segments is being read with 74% accuracy. This approach is the first to simultaneously track diverse reading subprocesses during complex story processing and predict the detailed neural representation of diverse story features, ranging from visual word properties to the mention of different story characters and different actions they perform. We construct brain representation maps that replicate many results from a wide range of classical studies that focus each on one aspect of language processing and offer new insights on which type of information is processed by different areas involved in language processing. Additionally, this approach is promising for studying individual differences: it can be used to create single subject maps that may potentially be used to measure reading comprehension and diagnose reading disorders.
... However, the SFIR has several complications when analyzing fMRI data from multi-stimulus designs: (1) selection for the optimal regularization parameter involves computationally intensive algorithms, (2) the resulting estimate has a large bias and can be sensitive to the parameter choice when data are noisy, and (3) the SFIR regularization incurs unequal biases towards estimating HRFs under distinct stimuli , so they may not be suitable for comparing brain responses to multiple stimuli. A third common class of approaches are based on representing the HRFs by a set of basis functions (e.g., Aguirre et al., 1998; Woolrich et al., 2004; Zarahn 2002 ). The main challenge of such methods lies in finding appropriate functional bases to represent the HRF parsimoniously, especially for data with weak signals. ...
Many existing methods for estimating the hemodynamic response functions (HRF) break down when analyzing multi-subject fMRI data from multi-stimulus designs due to the large and inhomogeneous variances across subjects. Within the context of the General Linear Model (GLM), we propose two new nonparametric estimators of the HRF for fMRI data arising from such designs. We first introduce a kernel-smoothed nonparamet-ric estimator, based on which we conduct hypothesis tests to identify task relevant neural activity. To cope with the inherent large data variance, we further impose a Tikhonov reg-ularization to the kernel-smoothed HRF estimator, on top of which a bias-correction pro-cedure is introduced by utilizing the multi-subject averaged HRF. A fast algorithm with linear computational time is also developed to select optimal regularization and smooth-ing parameters. We apply these methods to the fMRI data collected under a psychology design employing the Monetary Incentive Delay (MID) task. The proposed method suc-cessfully identified regions of neural activation related to affective responses to monetary incentives and penalties that were not identified using the traditional GLM approach. Moreover, the HRF estimates reveal that the magnitude of the penalty-related neural ac-tivity is significantly correlated with an individual state anxiety measure.
... We then averaged the two images obtained in the time window of 2-6 sec after the end of each trial. These images were deemed most likely to include the signals corresponding to the finger movement events because the Blood Oxygen Level-Dependent (BOLD) signal normally has a delay and peaks at approximately 6 s after the occurrence of neuronal activity (Aguirre et al., 1998). Finally, we extracted the values of each voxel belonging to the brain (segmentation). ...
Background:
Recent functional magnetic resonance imaging (fMRI) decoding techniques allow us to predict the contents of sensory and motor events or participants' mental states from multi-voxel patterns of fMRI signals. Sparse logistic regression (SLR) is a useful pattern classification algorithm that has the advantage of being able to automatically select voxels to avoid over-fitting. However, SLR suffers from over-pruning, in which many voxels that are potentially useful for prediction are discarded.
New method:
We propose an ensemble solution for over-pruning, called "Iterative Recycling" (iRec), in which sparse classifiers are trained iteratively by recycling over-pruned voxels.
Results:
Our simulation demonstrates that iRec can effectively rectify over-pruning in SLR and improve its classification accuracy. We also conduct an fMRI experiment in which eight healthy volunteers perform a finger-tapping task with their index or middle fingers. The results indicate that SLR with iRec (iSLR) can predict the finger used more accurately than SLR. Further, iSLR is able to identify a voxel cluster representing the finger movements in the biologically plausible contralateral primary sensory-motor cortices in each participant. We also successfully dissociated the regularly arranged representation for each finger in the cluster.
Conclusion and comparison with other methods:
To the best of our knowledge, ours is the first study to propose a solution for over-pruning with ensemble-learning that is applicable to any sparse algorithm. In addition, from the viewpoint of machine learning, we provide the novel idea of using the sparse classification algorithm to generate accurate divergent base classifiers.
... For example, the mapping of decision processes to brain areas is accomplished by engaging subjects with specific decision tasks and observing their corresponding brain activations when making decisions. A basic assumption of the neuroscience literature is that there is similarity across subjects in the anatomy and functionality of the human brain (e.g., Aguirre et al. 1998), and localized activation in a certain brain area is likely to generalize to the broader population of healthy (right-handed) adults. 6 For example, certain brain areas are usually associated with certain mental processes, as demonstrated in meta-analyses of how processes are mapped on the brain (e.g., Krain et al. 2006; Phan et al. 2002; Phelps 2006). ...
This research essay outlines a set of guidelines for conducting functional Magnetic Resonance Imaging (fMRI) studies in social science research in general and also, accordingly, in Information Systems research. Given the increased interest in using neuroimaging tools across the social sciences, this study aims at specifying the key steps needed to conduct an fMRI study while ensuring that enough detail is provided to evaluate the methods and results. The outline of an fMRI study consists of four key steps: (1) formulating the research question, (2) designing the fMRI protocol, (3) analyzing fMRI data, and (4) interpreting and reporting fMRI results. These steps are described with an illustrative example of a published fMRI study on trust and distrust in this journal (Dimoka 2010). The paper contributes to the methodological literature by (1) providing a set of guidelines for designing and conducting fMRI studies, (2) specifying methodological details that should be included in fMRI studies in academic venues, and (3) illustrating these practices with an exemplar fMRI study. Future directions for conducting high-quality fMRI studies in the social sciences are discussed.
... Model (1) without the term of the 2nd-order Volterra kernel is the GLM (Friston et al., 1995 ). There is a vast literature on the estimation of the HRF h i,k (t), including parametric methods (e.g., Friston et al., 1998a; Glover, 1999; Henson et al., 2002; Lindquist and Wager, 2007; Lindquist et al., 2009; Riera et al., 2004; Worsley and Friston, 1995) and nonparametric methods (e.g., Aguirre et al., 1998; Bai et al., 2009; Dale, 1999; Lange et al., 1999; Vakorin et al., 2007; Wang et al., 2011; Woolrich et al., 2004; Zarahn, 2002). Estimation of V i;k 1 k 2 t 1 ; t 2 ð Þis more challenging than that of the HRF, because the Volterra kernel function, defined on the two-dimensional space, involves many more parameters, while the number of observations, T, for each subject is usually limited. ...
Increasing the temporal resolution of the blood‑oxygen-dependent (BOLD) response is usually accompanied by a decrease in repetition time and therefore also a reduction of the magnetic resonance (MR) signal due to incomplete T1 relaxation and thus a loss of signal-to-noise ratio (SNR). A previous data reordering method can achieve higher temporal sampling rate without the loss of SNR but at the cost of increased scan time. In this proof-of-principle work, we show that combining HiHi reshuffling with multiband acceleration allows us to measure the in vivo BOLD response with a 75-ms sampling rate that is decoupled from the acquisition repetition time (here 1.5 s and hence higher SNR) while covering the entire forebrain with 60 2-mm slices in a ~ 35-min scan. We provide single-voxel time-courses of the BOLD responses in the primary visual and motor cortices in three fMRI experiments on a 7 T scanner - 1 male (scanned twice on different days for test-retest reproducibility) and 1 female participant.
Functional magnetic resonance imaging (fMRI) using blood-oxygenation-level-dependent (BOLD) contrast is a common method for studying human brain function non-invasively. Gradient-echo (GRE) BOLD is highly sensitive to the blood oxygenation change in blood vessels; however, the signal specificity can be degraded due to signal leakage from the activated lower layers to the superficial layers in depth-dependent (also called laminar or layer-specific) fMRI. Alternatively, physiological variables such as cerebral blood volume using VAscular-Space-Occupancy (VASO) measurements have shown higher spatial specificity compared to BOLD. To better understand the physiological mechanisms (e.g., blood volume and oxygenation change) and to interpret the measured depth-dependent responses we need models that reflect vascular properties at this scale. For this purpose, we adapted a cortical vascular model previously developed to predict the layer-specific BOLD signal change in human primary visual cortex to also predict layer-specific VASO response. To evaluate the model, we compared the predictions with experimental results of simultaneous VASO and BOLD measurements in a group of healthy participants. Fitting the model to our experimental findings provided an estimate of CBV change in different vascular compartments upon neural activity. We found that stimulus-evoked CBV changes mainly occur in intracortical arteries as well as small arterioles and capillaries and that the contribution from venules is small for a long stimulus (~30 sec). Our results confirm the notion that VASO contrast is less susceptible to large vessel effects compared to BOLD.
Since the invention of functional magnetic resonance imaging (fMRI), thousands of studies in healthy and clinical samples have enlightened our understanding of the organization of cognition in the human brain and neuroplastic changes following brain disease and injury. Increasingly, studies involve analyses rooted in complex systems theory and analysis applied to clinical samples. Given the complexity in available approaches, concise descriptions of the theoretical motivation of network techniques and their relationship to traditional approaches and theory are necessary. To this end, this review concerns the use of fMRI to understand basic cognitive function and dysfunction in the human brain scaling from emphasis on basic units (or “nodes”) in the brain to interactions within and between brain networks. First, major themes and theoretical issues in the scientific study of the injured brain are introduced to contextualize these analyses, particularly concerning functional “brain reorganization.” Then, analytic approaches ranging from the voxel level to the systems level using graph theory and related approaches are reviewed as complementary approaches to examine neurocognitive processes following TBI. Next, some major findings relevant to functional reorganization hypotheses are discussed. Finally, major open issues in functional network analyses in neurotrauma are discussed in theoretical, analytic, and translational terms.
Signed languages are natural human languages using the visual-motor modality. Previous neuroimaging studies based on univariate activation analysis show that a widely overlapped cortical network is recruited regardless whether the sign language is comprehended (for signers) or not (for non-signers). Here we move beyond previous studies by examining whether the functional connectivity profiles and the underlying organizational structure of the overlapped neural network may differ between signers and non-signers when watching sign language. Using graph theoretical analysis (GTA) and fMRI, we compared the large-scale functional network organization in hearing signers with non-signers during the observation of sentences in Chinese Sign Language. We found that signed sentences elicited highly similar cortical activations in the two groups of participants, with slightly larger responses within the left frontal and left temporal gyrus in signers than in non-signers. Crucially, further GTA revealed substantial group differences in the topologies of this activation network. Globally, the network engaged by signers showed higher local efficiency (t(24) = 2.379, p = 0.026), small-worldness (t(24) = 2.6042, p = 0.016) and modularity (t(24) = 3.513, p = 0.002), and exhibited different modular structures, compared to the network engaged by non-signers. Locally, the left ventral pars opercularis served as a network hub in the signer group but not in the non-signer group. These findings suggest that, despite overlap in cortical activation, the neural substrates underlying sign language comprehension are distinguishable at the network level from those for the processing of gestural action.
In the last few decades, structural and functional imaging methods have revolutionized cognitive neuroscience, providing a window into the functional organization and behavior of the human brain (review: Raichle, 2009). Neuroscientists no longer need rely on postmortem analysis of cortical lesions and their correlation to in vivo deficits (see Clarke and Morosan, Chapter 2) or perform invasive electrocorticographic recordings to study neural correlates of cognition (see Howard, Nourski, and Brugge, Chapter 3). Rather, detailed anatomical images, often acquired via magnetic resonance imaging (MRI), may be combined with functional information from electroencephalography (EEG) (see Alain and Winkler, Chapter 4), magnetoencephalography (MEG) (see Nagarajan, Gabriel, and Herman, Chapter 5), positron emission tomography (PET), or functional MRI (fMRI) to obtain dynamic pictures of brain function, in disordered and normal states, with reasonable spatial and temporal resolution.
A core task in the analysis of functional magnetic resonance imaging (fMRI) data is to detect groups of voxels that exhibit synchronous activity while the subject is performing a certain task. Synchronous activity is typically interpreted as functional connectivity between brain regions. We compare classical approaches like statistical parametric mapping (SPM) and some new approaches that are loosely based on frequent pattern mining principles, but restricted to the local neighborhood of a voxel. In particular, we examine how a soft notion of activity (rather than a binary one) can be modeled and exploited in the analysis process. In addition, we explore a fault-tolerant notion of synchronous activity of groups of voxels in both the binary and the soft/fuzzy activity setting. We apply the methods to fMRI data from a visual stimulus experiment to demonstrate their usefulness.
This chapter proposes a review of the most prominent issues in analysing brain functionalMagnetic Resonance data. It introduces the domain for readers with no or little knowledge in the field. The introduction places the context and orients the reader in the many questions put to the data, and summarizes the currently most commonly applied approach. The second section deals with intra subject data analysis, emphasizing hemodynamic response estimation issues. The third section describes current approaches and advances in analysing group data in a standard coordinate system. The last section proposes new spatial models for group analyses.Overall, the chapter gives a brief overview of the field and details some specific advances that are important for application studies in cognitive neurosciences.
Various statistical models have been proposed to analyze fMRI data. The usual goal is to make inferences about the effects that are related to an external stimulus. The primary focus of this paper is on those statisti-cal methods that enable one to detect 'significantly activated' regions of the brain due to event-related stimuli. Most of these methods share a common property, requiring estimation of the hemodynamic response function (HRF) as part of the deterministic component of the statistical model. We propose and investigate a new approach that does not require HRF fits to detect 'activated' voxels. We argue that the method not only avoids fitting a specific HRF, but still takes into account that the unknown response is delayed and smeared in time. This method also adapts to differential re-sponses of the BOLD response across different brain regions and experimen-tal sessions. The maximum cross-correlation between the kernel-smoothed stimulus sequence and shifted (lagged) values of the observed response is the proposed test statistic. Using our recommended approach we show through realistic simulations and with real data that we obtain better sensitivity than simple correlation methods using default values of SPM2. The simulation experiment incor-porates different HRFs empirically determined from real data. The noise models are also different AR(3) fits and fractional Gaussians estimated from real data. We conclude that our proposed method is more powerful than simple correlation procedures, because of its robustness to variation in the HRF.
PurposeYang et al. proposed an MRI technique for the simultaneous acquisition of cerebral blood volume (CBV), cerebral blood flow (CBF), and blood oxygenation level-dependent (BOLD)-weighted MRI signals (9). The purpose of this study was to develop modified version of the Yang sequence, which utilizes the advantages of 7 Tesla, leading to a robust and reliable MRI sequence.Methods
The inversion recovery-based MR pulse sequence introduced here involves slice-saturation slab-inversion vascular space occupancy (SI-SS-VASO) MRI, double echo planar imaging readouts for arterial spin labeling, and VASO in order to correct for BOLD contamination, and a separate BOLD acquisition to minimize inversion effects on the BOLD signal. A standard visual stimulus block design was used to evaluate the spatial and temporal characteristics of CBV-, CBF-, and BOLD-weighted images.ResultsThe high signal-to-noise ratio and spatial resolution of this method leads to robust activation maps. This technique enables the investigation of the differential spatial specificity and temporal characteristics of the different modalities.Conclusion
The pulse sequence could be a powerful tool for studies of neurovascular coupling, hemodynamic response, or calibrated BOLD. Magn Reson Med, 2014. © 2014 Wiley Periodicals, Inc.
Developing translational biomarkers is a priority for psychiatry research. Task-independent functional brain imaging is a relatively novel technique that allows examination of the brain's intrinsic networks, defined as functionally and (often) structurally connected populations of neurons whose properties reflect fundamental neurobiological organizational principles of the central nervous system. The ability to study the activity and organization of these networks has opened a promising new avenue for translational investigation, because they can be analogously examined across species and disease states. Interestingly, imaging studies have revealed shared spatial and functional characteristics of the intrinsic network architecture of the brain across species, including mice, rats, non-human primates, and humans. Using schizophrenia as an example, we show how intrinsic networks may show similar abnormalities in human diseases and animal models of these diseases, supporting their use as biomarkers in drug development.
Surface segregation provides an efficient way to parse the visual scene for perceptual analysis. Here, we investigated the segregation of a bivectorial motion display into transparent surfaces through a psychophysical task and fMRI. We found that perceptual transparency correlated with neural activity in the early areas of the visual cortex, suggesting these areas may be involved in the segregation of motion-defined surfaces. Two oppositely rotating, uniquely colored random dot kinematograms (RDKs) were presented either sequentially or in a spatially interleaved manner, displayed at varying alternation frequencies. Participants reported the color and rotation direction pairing of the RDKs in the psychophysical task. The spatially interleaved display generated the percept of motion transparency across the range of frequencies tested, yielding ceiling task performance. At high alternation frequencies, performance on the sequential display also approached ceiling, indicative of perceived transparency. However, transparency broke down in lower alternation frequency sequential displays, producing performance close to chance. A corresponding pattern mirroring the psychophysical data was also evident in univariate and multivariate analyses of the fMRI BOLD activity in visual cortical areas V1, V2, V3, V3AB, hV4, and V5/MT+. Using gray RDKs, we found significant presentation by frequency interactions in most areas; differences in BOLD signal between presentation types were significant only at the lower alternation frequency. Multivariate pattern classification was similarly unable to discriminate between presentation types at the higher frequency. This study provides evidence that early visual cortex may code for motion-defined surface segregation, which in turn may enable perceptual transparency.
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