Observed accuracies, number of voxels, and the p-value based on permutation distribution with 1, permutations are reported. Identification accuracies of object categories based on the patterns of functional activity of that or other participants. We would like to thank Vladimir Cherkassky and Sandesh Aryal for technical assistance, reviewers for helpful comments on the earlier version of the manuscript and Stacey Becker and Rachel Krishnaswami for help in the preparation of the manuscript.
Performed the experiments: SS. Analyzed the data: SS VM. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field.
Abstract Previous studies have succeeded in identifying the cognitive state corresponding to the perception of a set of depicted categories, such as tools , by analyzing the accompanying pattern of brain activity, measured with fMRI.
Introduction It has been a lasting challenge to establish the correspondence between a simple cognitive state such as the thought of a hammer and the underlying brain activity. Download: PPT. Materials and Methods Participants Twelve right-handed adults 8 female from the Carnegie Mellon community participated and gave written informed consent approved by the University of Pittsburgh and Carnegie Mellon Institutional Review Boards.
Experimental paradigm The stimuli depicted concrete objects from two semantic categories tools and dwellings , and took the form of white line drawings on a black background. Machine learning methods Classifiers were trained to identify cognitive states associated with viewing drawings, using the evoked pattern of functional activity mean PSC. Feature selection Feature selection first identified the voxels whose responses were the most stable over six presentations of objects within a participant, and then selected from among the stable voxels those that best discriminated among objects within the training set, using only the data in the training set.
Analyses of a single brain region at a time Single anatomical brain regions that consistently identified object exemplars or categories across participants were selected using cross-validation, and the significance of those identifications was tested across participants. Analysis of the confusion patterns Single brain regions were compared in terms of their confusion patterns using a generalization of the principal components analysis method [14] , [15]. Multiple participant analysis Data from all but one participant were used to train a classifier to identify the data from the left-out participant.
Results Identifying object exemplars: whole brain The highest rank accuracy achieved for any participant while identifying individual object exemplars was 0. Figure 2. High classification rank accuracies for object exemplars. Figure 3. Locations of the diagnostic voxels in object exemplar classification for the three participants having the highest accuracies are shown on the three-dimensional rendering of the T1 MNI single-subject brain.
Identifying object exemplars: single brain regions Previous studies have focused on one particular region, the ventral temporal cortex, in an attempt to relate cognitive states to activation patterns in a particular region e. Identifying object categories: whole brain A classifier was trained to decode which category that the object a participant was viewing belonged to, i. Figure 4. High classification accuracies for object categories.
Figure 5. Commonality in voxel locations across the three participants having the highest category classification accuracies. Figure 6.
Brain activation showing areas of greater activity for A objects compared to fixation, and B tools compared to dwellings. Identifying object categories: single brain regions As was the case for exemplar identification, the accuracies of the category identification using voxels from only a single anatomical region were high; in some cases, these approached the accuracy obtained when the whole cortex was used 0.
Table 1. Anatomical regions out of 71 that singly produced reliable average classification accuracies across the twelve participants for category identification. Figure 7. Brain regions in the space of the first two principal components of the compromise matrix based on the regions' confusion errors.
Commonality of neural representations across participants Classifiers were trained on data from 11 of the 12 participants to determine if it was possible to identify object exemplars and categories in the held-out 12 th participant's data; this procedure was repeated for all participants. Discussion The two main conceptual advances offered by these findings are that there is an identifiable neural pattern associated with perception and contemplation of individual objects, and that part of the pattern is shared across participants.
Distributed representation The fact that individual objects, and the categories they belong to, can be accurately decoded from fMRI activity in any of several regions indicates that there are multiple brain regions besides classical object-selective cortex that contain information about the objects and categories. Commonality of the neural representation of object categories and exemplars across participants The ability to identify object categories across participants reveals the striking commonality of the neural basis of this type of semantic knowledge.
Supporting Information. Table S1. Table S2. Acknowledgments We would like to thank Vladimir Cherkassky and Sandesh Aryal for technical assistance, reviewers for helpful comments on the earlier version of the manuscript and Stacey Becker and Rachel Krishnaswami for help in the preparation of the manuscript.
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The patient group showed widespread increased brain activation in both tasks. The typical fMRI task activation experiment utilizes visual, auditory or other stimuli to alternately induce two or more different cognitive states in the subject, while collecting MRI volumes continuously as described above.
With a two-condition design, one state is called the experimental condition, while the other is denoted the control condition, and the goal is to test the hypothesis that the signals differ between the two states.
Using a block design, the trials are arranged to alternate between the experimental and control conditions, as shown in Fig. The block design is optimum for detecting activation, but a jittered event-related ER design is superior when characterization of the amplitude or timing of the hemodynamic response is desired 11 , In the ER design, task events are relatively brief and occur at non-constant inter-trial intervals with longer periods of control condition, which allows the hemodynamic response to return more fully to baseline.
Jittering the timing serves to sample the hemodynamic response with higher temporal frequency in the overall time series, but may also be used to induce a desired cognitive strategy, e. Block design fMRI experiment. A neural response to the state change from A to B in the stimulus is accompanied by a hemodynamic response as shown in Fig.
Using single- or multi-variate time series analysis methods, the average signal difference between the two states is computed for the scan and a contrast map generated. A statistical activation map is finally obtained using a suitable threshold for the difference; the map depicts the probability that a voxel is activated given the uncertainty due to noise and the small BOLD signal differences.
The degree to which valid inferences can be drawn from the measured time series data depends in large part on careful design of the task.
The investigator must take care that only the effect of interest changes between experimental and control conditions, while confounding effects such as attention and valence are maintained constant or irrelevant. In some studies this is straightforward, such as the use of a sensory task for presurgical mapping, wherein the goal is only to localize activation so that important brain functions can be maintained after surgery.
In this case the signal intensity is of minor interest as long as it is adequate to characterize the functional substrates to be preserved during surgical intervention. In many other cases, however, comparative inferences are desired such as parametric studies of the influence of task difficulty on a cognitive process, and thus control of such factors as learning, adaptation, and salience must be considered. Once the images have been acquired, the time series data must be processed to obtain maps of brain activation.
The noise results from thermal sources in the subject and electronics, bulk motion of the head, cardiac and respiratory-induced noise, and variations in baseline neural metabolism. Because the noise can sometimes be larger than the signal of interest, fMRI analyses compare the signal difference between the states using a statistical test. These tests result in an activation map that is a function of the probability that the brain states differ.
The statistical test for activation can utilize a general linear model GLM 27 , 63 , cross-correlation with a modeled regressor 2 , or one of several data-driven approaches such as independent components analysis ICA In all cases, the activation testing is preceded by a series of preprocessing steps.
The analysis of fMRI data continues to be a subject of intense research at this time, and is one about which numerous books have been written, to which the reader is referred for further information e.
Sarty Resolution in fMRI is limited primarily by SNR because of the necessity for rapid acquisition of time series information.
Thus, as T acq is reduced for single shot imaging typically 20—30ms the pixel size must be increased over that for conventional anatomic imaging to maintain an acceptable SNR. Accordingly, the typical fMRI pixel size is 3—4 mm, although with higher field magnets 7T a pixel size of microns or less may be readily achieved NIRS resolution is low 10—20 mm and limited predominantly by the strong scatter and attenuation of IR photons which also limits the depth of cortex that can be imaged within a banana-shaped region connecting optodes , the modest density of optodes and the ill-conditioned inverse problem of reconstructing 3D maps of [Hb] from scalp recordings This is much slower than the underlying neural processes, and temporal information is thereby heavily blurred.
Nevertheless, by jittering event-related stimuli and using appropriate analysis methods 11 temporal inferences in the ms resolution range can be achieved PET scans require minutes to complete because of the low count rates of injected radio nuclides, so changes in neural processes can only be studied by repeated scanning.
EEG and MEG, on the other hand, have millisecond temporal resolution and can easily capture the dynamics of evoked responses that last a few ms to several hundred ms. Multimodal approaches combining fMRI and EEG use fMRI maps as spatial priors to reconstruct high temporal resolution electrophysiology, thereby gaining resolution in both dimensions From the discussion above, a primary strength of fMRI is its relatively high spatial resolution and availability.
In addition, it is readily available to both clinical and academic researchers, is noninvasive, and can provide high resolution anatomic scans in the same session to use for localization, vessel identification, 45 or development of maps of white matter connectivity through the use of diffusion tensor imaging DTI 5.
Because BOLD contrast derives from the sluggish hemodynamic response to metabolic changes, a significant weakness is its low temporal resolution. Many methods have been developed to diminish these susceptibility losses, although most involve some tradeoff of SNR in magnetically uniform brain regions 18 , 19 , 31 , 34 , 58 , 60 , Finally, the high magnetic fields require customized stimulus delivery and subject response systems, again limiting flexibility and complicating multimodal experiments such as concurrent EEG recording.
Over the years, a number of investigators have attempted to develop alternatives to BOLD contrast using direct neural current detection 9 , although by now it is understood 42 that the weak size of the neural current signal relative to physiological noise makes a breakthrough unlikely. Another alternative is the use of diffusion weighted imaging to demonstrate activation-related changes in populations of bound vs.
A potential advantage is that such diffusion related changes may have more rapid responses than BOLD methods. However, again the signals are weaker than BOLD contrast and their biophysical origin is still unclear While a modest research effort will continue in improving acquisition technology, the bulk of research in the development of fMRI has shifted to its application to answering more complex questions in cognitive neuroscience.
One promising area is that of using activation maps as input to classification and state change algorithms to predict or classify cognitive behavior, such as predicting brain states 44 , 53 also see, e. Norman for a review Other emerging uses of fMRI include the development of quantitative measures, i. A cautionary note, however, is that because of the small BOLD responses typical of cognitive processes, most studies are limited to employing group statistics to make inferences about populations rather than about individuals.
Finally, feedback derived from real-time fMRI has been shown to allow subjects to learn pain-reduction strategies 24 , enhance sensorimotor control 23 and to control relevant brain regions in mood disorder experiments The reader is also referred to Bandettini 1 for additional considerations regarding the future of fMRI. Informed by fMRI, more sophisticated modeling of brain networks is certain to lead to new levels of understanding of the human brain.
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