FMRI cortical activity predicts pattern-detection performance

Journal of Neuroscience Methods, 2015

h i g h l i g h t s

Neuroimage, 2000

The results from a single functional magnetic resonance imaging session are typically reported as indicative of the subject's functional neuroanatomy. Underlying this interpretation is the implicit assumption that there are no responses specific to that particular session, i.e., that the potential variability of response between sessions is negligible. The present study sought to examine this assumption empirically. A total of 99 sessions, comprising 33 repeats of simple motor, visual, and cognitive paradigms, were collected over a period of 2 months on a single male subject. For each paradigm, the inclusion of session-by-condition interactions explained a significant amount of error variance (P < 0.05 corrected for multiple comparisons) over a model assuming a common activation magnitude across all sessions. However, many of those voxels displaying significant session-by-condition interactions were not seen in a multisession fixed-effects analysis of the same data set; i.e., they were not activated on average across all sessions. Most voxels that were both significantly variable and activated on average across all sessions did not survive a randomeffects analysis (modeling between-session variance). We interpret our results as demonstrating that correct inference about subject responses to activation tasks can be derived through the use of a statistical model which accounts for both within-and between-session variance, combined with an appropriately large session sample size. If researchers have access to only a single session from a single subject, erroneous conclusions are a possibility, in that responses specific to this single session may be claimed to be typical responses for this subject.

European archives of …, 1998

2001

ceptible to the choice of control task (typically passive Canada N6A 5C2 viewing), the subject's strategy during the control task 2 Department of Psychology (e.g., vegetating versus attending to other things), and Harvard University the subject's state of mind during the scan (e.g., drowsi-Cambridge, Massachusetts 02138 ness versus alertness). 3 Department of Brain and Cognitive Sciences A major goal of the present study was to use a para-Massachusetts Institute of Technology metric load manipulation to disentangle the functions Cambridge, Massachusetts 02139 of the cortical regions that have been shown to be acti-4 Massachusetts General Hospital NMR Center vated by both attention and eye movements (Corbetta, Charlestown, Massachusetts 02129 1998; Culham et al., 1998). While attention and eye movements are certainly tightly coupled, the nature of their relationship is highly debated (Klein, 1980; Klein Summary and Pontefract, 1994; Shepherd et al., 1986). In the most extreme view, covert attention can only be accom-We derived attention response functions for different plished by preparing a saccade to the attended location cortical areas by plotting neural activity (measured (Rizzolatti et al., 1994; Snyder et al., 1997). However,

Neuropsychologia, 2004

2018 IEEE Global Communications Conference (GLOBECOM), 2018

In this paper, we investigate whether we can distinguish that a subject is making a correct or incorrect behavioral response by analyzing the fMRI data of localized brain regions, obtained from a feature-based attention experiment. For each subject, we first construct the feature vectors for each region of interest (including V1, MT or IPS1) from the fMRI signals. Second, we project the feature vectors onto a lower dimensional subspace using Linear Discriminant Analysis (LDA), where the difference between two classes (correct vs. incorrect response) is maximized. Finally, we apply the Bayesian classifier to the projected data, and find that the classification accuracies corresponding to V1, MT and IPS1 are 87.2%, 90.8% and 81.7%, respectively, when all the trials are considered. Our analysis indicates that: when people make correct or incorrect responses, significant difference exists in the fMRI signals, especially in V1 and MT regions, and the difference can be effectively captured by the LDA-Bayesian classifier. We also prove that: when the original data are normally distributed, LDA, which aims to maximize the difference between different classes, is equivalent to the optimal Maximum Likelihood (ML) based classification method.

Proceedings of the Royal Society B: Biological Sciences, 2003

Theories of perception have proposed a basic distinction between parallel pre-attentive and serial attentive modes of processing. However, chronometric measures are often ambiguous in separating parallel and serial processes. We have used the activity of attention-related regions of the human brain, measured with functional magnetic resonance imaging, to separate parallel from serial processes at the single-trial level in a visual quantification task. In this task, some have suggested the deployment of two qualitatively different processes, a fast parallel 'subitizing' for sets of one, two or three objects and a slow serial counting for larger sets. Our results indicate that attention-related regions of the posterior parietal and frontal cortices show a sudden increase in activity only from numerosity four onwards, confirming the parallelserial dichotomy of subitizing and counting. Moreover, using the presence or absence of attentional shifts, as inferred from the activation of posterior parietal regions, we successfully predict whether, on a given trial, subjects deployed a serial exploration of the display or a parallel apprehension. Beyond the subitizing/counting debate, this approach may prove useful to probe the attentional demands of other cognitive tasks.

Experimental Brain Research, 2006

Using an uncertainty paradigm and functional magnetic resonance imaging (fMRI) we studied the effect of nonspatial selective and divided visual attention on the activity of specific areas of human extrastriate visual cortex. The stimuli were single ovals that differed from an implicit standard oval in either colour or width. The subjects' task was to classify the current stimulus as one of two possible alternatives per stimulus dimension.

An experimental design for functional MRI (fMRI) is presented whose conceptual units of analysis are behavioral trials, in contrast to blocks of trials. This type of design is referred to as a trial-based (TB) fMRI design. It is explained how TB designs can afford the ability to: (1) randomize the presentation of behavioral trials and (2) utilize intertrial variance in uncontrolled behavioral measures to examine their functional correlates. A particular type of TB design that involves modeling trial-evoked fMRI responses with one or more shifted impulse response functions is described. This design is capable of discriminating functional changes occurring during temporally separated behavioral subcomponents within trials. An example of such a design is implemented and its statistical specificity, functional sensitivity, and functional specificity are tested.

NeuroImage, 2001

Attention is, in part, a mechanism for identifying features of the sensory environment of potential relevance to behavior. The network of brain areas sensitive to the behavioral relevance of multimodal sensory events has not been fully characterized. We used event-related fMRI to identify brain regions responsive to changes in both visual and auditory stimuli when those changes were either behaviorally relevant or behaviorally irrelevant. A widespread network of "context-dependent" activations responded to both task-irrelevant and task-relevant events but responded more strongly to task-relevant events. The most extensive activations in this network were located in right and left temporoparietal junction (TPJ), with smaller activations in left precuneus, left anterior insula, left anterior cingulate cortex, and right thalamus. Another network of "context-independent" activations responded similarly to all events, regardless of task relevance. This network featured a large activation encompassing left supplementary and cingulate motor areas (SMA/CMA) as well as right IFG, right/left precuneus, and right anterior insula, with smaller activations in right/left inferior temporal gyrus and left posterior cingulate cortex. Distinct context-dependent and context-independent subregions of activation were also found within the left and right TPJ, left anterior insula, and left SMA/CMA. In the right TPJ, a subregion in the supramarginal gyrus showed sensitivity to the behavioral context (i.e., relevance) of stimulus changes, while two subregions in the superior temporal gyrus did not. The results indicate a role for the TPJ in detecting behaviorally relevant events in the sensory environment. The TPJ may serve to identify salient events in the sensory environment both within and independent of the current behavioral context.