Efficient Design of Event-Related fMRI Experiments Using M-Sequences

Buračas, Giedrius T.; Boynton, Geoffrey M.

doi:10.1006/nimg.2002.1116

Cited by 264 publications

(222 citation statements)

References 16 publications

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“…To this end, we recorded brain responses to negative, positive, chimeric and eye images, using functional magnetic resonance imaging (fMRI). Eight subjects were shown 25 instances each of these 4 classes of face images in M-sequences (29) and their brain activations recorded using a rapid event related design. For any one subject, a particular face appeared in only 1 of the 4 conditions.…”

Section: Resultsmentioning

confidence: 99%

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Role of ordinal contrast relationships in face encoding

Gilad

Meng²,

Sinha³

2009

Proc. Natl. Acad. Sci. U.S.A.

116

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What aspects of facial information do we use to recognize individuals? One way to address this fundamental question is to study image transformations that compromise facial recognizability. The goal would be to identify factors that underlie the recognition decrement and, by extension, are likely constituents of facial encoding. To this end, we focus here on the contrast negation transformation. Contrast negated faces are remarkably difficult to recognize for reasons that are currently unclear. The dominant proposals so far are based either on negative faces' seemingly unusual pigmentation, or incorrectly computed 3D shape. Both of these explanations have been challenged by recent results. Here, we propose an alternative account based on 2D ordinal relationships, which encode local contrast polarity between a few regions of the face. Using a novel set of facial stimuli that incorporate both positive and negative contrast, we demonstrate that ordinal relationships around the eyes are major determinants of facial recognizability. Our behavioral studies suggest that destruction of these relationships in negatives likely underlies the observed recognition impairments, and our neuro-imaging data show that these relationships strongly modulate brain responses to facial images. Besides offering a potential explanation for why negative faces are hard to recognize, these results have implications for the representational vocabulary the visual system uses to encode faces.contrast negation ͉ face perception ͉ fMRI ͉ neural representation ͉ object recognition I n principle, a contrast negated image is exactly as informative as its positive counterpart; negation perfectly preserves an image's 2D geometric and spectral structure. However, as anyone who has had to search through a roll of negatives for a snapshot of a particular person knows, this simple operation has dramatically adverse consequences on our ability to identify faces, as illustrated in Fig. 1 (1-6). Exploring the causes of this phenomenon is important for understanding the broader issue of the nature of information the visual system uses for face identification.Several researchers have hypothesized that negated face-images are hard to recognize because of the unnatural shading cues in negatives, which compromise shape from shading processes (7-11). The resulting problems in recovering veridical 3D facial shape are believed to impair recognition performance. Although plausible, it is unclear whether this explanation is a sufficient one, especially in light of experimental results showing preserved recognition performance in the absence of shading gradients (12), and theories of face recognition that are based on the use of 2D intensity patterns rather than recovered 3D shapes (13,14). Another prominent hypothesis is that negation causes faces to have unusual pigmentation (15, 16). However, the adequacy of this ''pigmentation hypothesis'' has been challenged by data showing that hue negation, which also results in unnatural pigmentation (making the entire face l...

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Section: Resultsmentioning

confidence: 99%

“…Experimental stimuli consisted of 100 face images in negative, positive, chimeric and eyes-only conditions. In each experimental run, these 100 faces mixed with 24 fixation-only trials were presented in a different M-sequence (29). Each trial was 2 s long.…”

Section: Methodsmentioning

confidence: 99%

Role of ordinal contrast relationships in face encoding

Gilad

Meng²,

Sinha³

2009

Proc. Natl. Acad. Sci. U.S.A.

116

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“…The display was terminated by the observer's response or after a maximum duration of 1.5 s. A white fixation cross was displayed during the inter-trial interval, which lasted, variably, 0, 500, or 1000 ms. Each of the four experimental conditions was presented with a probability of 0.2, the remaining trials were null events. The order of events, including null events, was determined using maximumlength shift register sequences (m-sequences) described in detail by Buracas and Boynton (2002). M-sequences counterbalance subsequences of a given length in order to ensure that trials from each condition were preceded equally often by trials from each of the other conditions.…”

Section: Participantsmentioning

confidence: 99%

Selective and interactive neural correlates of visual dimension changes and response changes

Pollmann

Weidner

Müller³

et al. 2006

NeuroImage

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In an event-related fMRI study, we investigated the neural correlates of visual dimension and response changes. We used a compound task, which required target selection by a singleton feature, a unique color or motion direction, before the appropriate motor response, which was determined by target orientation, could be selected. Both types of change elicited distinct patterns of activation, with dimension-changerelated activation primarily in posterior visual areas and responserelated activation primarily in motor-related areas of the parietal and frontal cortices. Response-change-related activation was delayed by about 1 s relative to dimension-change-related activation, suggesting that the latter is elicited by perceptual processes, whereas the former reflects response-related or post-response processes. Although dimension changes and response changes rely on different processes, they are not independent: response facilitation was observed for combined dimension and response repetitions, this facilitation, however, was disrupted by dimension changes. D

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“…A number of studies have addressed the problem of systematically assessing the quality of fMRI experimental designs, both in terms of the ability to detect stimulus/task-related BOLD activation (detection power) and the ability to estimate the HRF model (estimation efficiency) in a given amount of imaging time (Dale, 1999;Liu et al, 2001). Different methodologies have been proposed to determine the optimal design of fMRI experiments for maximal estimation efficiency (Buracas and Boynton, 2002;Wager and Nichols, 2003;Maus et al, 2012), and a few studies have compared different HRF models and the associated estimation efficiency, focusing on specific parameters of interest such as the response latency and duration (Lindquist and Wager, 2007;Lindquist et al, 2009). Importantly, the authors were concerned with the physiological plausibility of the estimated HRF parameters and with their independence, such that differences in one parameter are not confounded with differences in another parameter.…”

Section: Introductionmentioning

confidence: 99%

On the distinguishability of HRF models in fMRI

Silvestre¹,

Figueiredo²,

Rosa³

2010

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology

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Modeling the Hemodynamic Response Function (HRF) is a critical step in fMRI studies of brain activity, and it is often desirable to estimate HRF parameters with physiological interpretability. A biophysically informed model of the HRF can be described by a non-linear time-invariant dynamic system. However, the identification of this dynamic system may leave much uncertainty on the exact values of the parameters. Moreover, the high noise levels in the data may hinder the model estimation task. In this context, the estimation of the HRF may be seen as a problem of model falsification or invalidation, where we are interested in distinguishing among a set of eligible models of dynamic systems. Here, we propose a systematic tool to determine the distinguishability among a set of physiologically plausible HRF models. The concept of absolutely input-distinguishable systems is introduced and applied to a biophysically informed HRF model, by exploiting the structure of the underlying non-linear dynamic system. A strategy to model uncertainty in the input time-delay and magnitude is developed and its impact on the distinguishability of two physiologically plausible HRF models is assessed, in terms of the maximum noise amplitude above which it is not possible to guarantee the falsification of one model in relation to another. Finally, a methodology is proposed for the choice of the input sequence, or experimental paradigm, that maximizes the distinguishability of the HRF models under investigation. The proposed approach may be used to evaluate the performance of HRF model estimation techniques from fMRI data.

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Efficient Design of Event-Related fMRI Experiments Using M-Sequences

Cited by 264 publications

References 16 publications

Role of ordinal contrast relationships in face encoding

Role of ordinal contrast relationships in face encoding

Selective and interactive neural correlates of visual dimension changes and response changes

On the distinguishability of HRF models in fMRI

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