Article

Decoding brain states using functional magnetic resonance imaging

Authors:
To read the full-text of this research, you can request a copy directly from the authors.

Abstract

Most leading research in basic and clinical neuroscience has been carried out by functional magnetic resonance imaging (fMRI), which detects the blood oxygenation level dependent signals associated with neural activities. Among new fMRI applications, brain decoding is an emerging research area, which infers mental states from fMRI signals. Brain decoding using fMRI includes classification, identification, and reconstruction of brain states. It is generally conducted using multi-voxel pattern analysis based on neuroscientific evidence that brain functions are mediated by distributed activation patterns. Brain decoding techniques have been successful in diverse applications such as the brain computer interface, patient monitoring, and neurofeedback. These techniques have expanded our understanding of how the brain encodes distinct information. In the current paper, we reviewed recent fMRI-based brain decoding techniques and applications. We also discussed the potential implications of brain decoding in neuroscience. KeywordsfMRI–Mind reading–Brain decoding–Neurofeedback–Real time fMRI

No full-text available

Request Full-text Paper PDF

To read the full-text of this research,
you can request a copy directly from the authors.

... In EEG signals [23], it is difficult to find clear patterns generated as result of the pre-processing methods (based on vectors of characteristics) due to the complexity of these signals [24]. Mostly of the pattern classification methods used the support of medical specialists which classify a certain number of signals for training the PR systems. ...
Article
Full-text available
The aim of this study was to design an automatic classifier for electroencephalographic information (EEGI) registered in evoked potentials experiments. The classifier used a parallel associative memory based on recurrent neural networks (RNNs). Each RNN was trained to classify signals belonging to an individual class. A recurrent method based on the application of Lyapunov controlled functions served to design the training procedure of each RNN in the classifier. A parallel structure of RNN with fixed weights (obtained after training process) performed the validation stage. This structure formed a classifier assemble. The selected class assigned to a new segment of EEGI signal is estimated by the minimum value of the least mean square error among the RNNs forming the assemble. The generalization-regularization and a k-fold cross validation (\(k=5\)) were the validation methods evaluating the classifier efficiency. The confusion matrix method justified the application of the classification method introduced in this study. The EEGI obtained from two different annotated databases served to test the classifier based on RNNs. The first database contained signals divided in five different classes and collected from patients suffering from epilepsy. The second database has 90 signals divided in three classes that corresponded to EEGI signals corresponding to 3 different visual evoked potentials. The pattern classifier achieved a maximum correct classification percentage of 97.2% using the information of both databases. This value prevailed over results reported in similar studies using the first database. In comparison with other pattern recognition algorithms, the proposed RNNs based classifier attained similar or even better correct classification results.
... Encephalopathy is proved to be one of the major diseases threatening human health in recent years. 1 Electroencephalography (EEG), near-infrared spectroscopy (NIRS), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), positron emission tomography (PET), magnetoencephalography (MEG), and other advanced modalities are utilized separately or jointly for encephalopathy monitoring, medical diagnosis, and functional rehabilitation. [2][3][4][5] The EEG is the only non-invasive measure for neuronal function of the brain, and kinds of encephalopathy can be assessed by using EEG. 6 Generally, current monitoring by EEG devices is inpatient due to the huge volume. ...
Article
Full-text available
Wearable electroencephalography systems of out-of-hospital can both provide complementary recordings and offer several benefits over long-term monitoring. However, several limitations were present in these new-born systems, for example, uncomfortable for wearing, inconvenient for retrieving the recordings by patients themselves, unable to timely provide accurate classification, and early warning information. Therefore, we proposed a wireless wearable electroencephalography system for encephalopathy daily monitoring, named as Brain-Health, which focused on the following three points: (a) the monitoring device integrated with electroencephalography acquisition sensors, signal processing chip, and Bluetooth, attached to a sport hat or elastic headband; (b) the mobile terminal with dedicated application, which is not only for continuous recording and displaying electroencephalography signal but also for early warning in real time; and (c) the encephalopathy’s classification algorithm based on intelligent Support Vector Machine, which is used in a new application of wearable electroencephalography for encephalopathy daily monitoring. The results showed a high mean accuracy of 91.79% and 93.89% in two types of classification for encephalopathy. In conclusion, good performance of our Brain-Health system indicated the feasibility and effectiveness for encephalopathy daily monitoring and patients’ health self-management.
... As discussed by Park and Friston [2013], a network is not a simple combination of individual pathways, but rather, a systematic organization of interactions within the network. Thus, an altered corticospinal pathway or thalamocortical pathway found in previous studies on CP [Arzoumanian et al., 2003;Lee et al., 2011b;Pannek et al., 2014;Scheck et al., 2012;Thomas et al., 2005] or altered functional connectivity in CP [Dinomais et al., 2012;Lee et al., 2011a;Papadelis et al., 2014] may not be sufficient enough to explain the topological architecture of the motor-related brain regions, including cortical and subcortical regions. What is more important might be the nature of circuits and a combination of edges [Marder, 2012]. ...
Article
Manifestation of the functionalities from the structural brain network is becoming increasingly important to understand a brain disease. With the aim of investigating the differential structure–function couplings according to network systems, we investigated the structural and functional brain networks of patients with spastic diplegic cerebral palsy with periventricular leukomalacia compared to healthy controls. The structural and functional networks of the whole brain and motor system, constructed using deterministic and probabilistic tractography of diffusion tensor magnetic resonance images and Pearson and partial correlation analyses of resting-state functional magnetic resonance images, showed differential embedding of functional networks in the structural networks in patients. In the whole-brain network of patients, significantly reduced global network efficiency compared to healthy controls were found in the structural networks but not in the functional networks, resulting in reduced structural–functional coupling. On the contrary, the motor network of patients had a significantly lower functional network efficiency over the intact structural network and a lower structure–function coupling than the control group. This reduced coupling but reverse directionality in the whole-brain and motor networks of patients was prominent particularly between the probabilistic structural and partial correlation-based functional networks. Intact (or less deficient) functional network over impaired structural networks of the whole brain and highly impaired functional network topology over the intact structural motor network might subserve relatively preserved cognitions and impaired motor functions in cerebral palsy. This study suggests that the structure–function relationship, evaluated specifically using sparse functional connectivity, may reveal important clues to functional reorganization in cerebral palsy. Hum Brain Mapp, 2017.
... Therefore, alternative approaches that overcome this limitation should be devel­ oped to improve diagnosis and enhance our understanding of these pathological brain conditions. These approaches may include, for instance, brain function measured by using electroencephalography (EEG) [J4] and functional mag­ netic resonance imaging (tMRI) [4,18]. These approaches aim to characterize the dynamical brain behavior [22, 42] under healthy and pathological conditions. ...
Conference Paper
Full-text available
Table of contents I1 Introduction to the 2015 Brainhack Proceedings R. Cameron Craddock, Pierre Bellec, Daniel S. Margules, B. Nolan Nichols, Jörg P. Pfannmöller A1 Distributed collaboration: the case for the enhancement of Brainspell’s interface AmanPreet Badhwar, David Kennedy, Jean-Baptiste Poline, Roberto Toro A2 Advancing open science through NiData Ben Cipollini, Ariel Rokem A3 Integrating the Brain Imaging Data Structure (BIDS) standard into C-PAC Daniel Clark, Krzysztof J. Gorgolewski, R. Cameron Craddock A4 Optimized implementations of voxel-wise degree centrality and local functional connectivity density mapping in AFNI R. Cameron Craddock, Daniel J. Clark A5 LORIS: DICOM anonymizer Samir Das, Cécile Madjar, Ayan Sengupta, Zia Mohades A6 Automatic extraction of academic collaborations in neuroimaging Sebastien Dery A7 NiftyView: a zero-footprint web application for viewing DICOM and NIfTI files Weiran Deng A8 Human Connectome Project Minimal Preprocessing Pipelines to Nipype Eric Earl, Damion V. Demeter, Kate Mills, Glad Mihai, Luka Ruzic, Nick Ketz, Andrew Reineberg, Marianne C. Reddan, Anne-Lise Goddings, Javier Gonzalez-Castillo, Krzysztof J. Gorgolewski A9 Generating music with resting-state fMRI data Caroline Froehlich, Gil Dekel, Daniel S. Margulies, R. Cameron Craddock A10 Highly comparable time-series analysis in Nitime Ben D. Fulcher A11 Nipype interfaces in CBRAIN Tristan Glatard, Samir Das, Reza Adalat, Natacha Beck, Rémi Bernard, Najmeh Khalili-Mahani, Pierre Rioux, Marc-Étienne Rousseau, Alan C. Evans A12 DueCredit: automated collection of citations for software, methods, and data Yaroslav O. Halchenko, Matteo Visconti di Oleggio Castello A13 Open source low-cost device to register dog’s heart rate and tail movement Raúl Hernández-Pérez, Edgar A. Morales, Laura V. Cuaya A14 Calculating the Laterality Index Using FSL for Stroke Neuroimaging Data Kaori L. Ito, Sook-Lei Liew A15 Wrapping FreeSurfer 6 for use in high-performance computing environments Hans J. Johnson A16 Facilitating big data meta-analyses for clinical neuroimaging through ENIGMA wrapper scripts Erik Kan, Julia Anglin, Michael Borich, Neda Jahanshad, Paul Thompson, Sook-Lei Liew A17 A cortical surface-based geodesic distance package for Python Daniel S Margulies, Marcel Falkiewicz, Julia M Huntenburg A18 Sharing data in the cloud David O’Connor, Daniel J. Clark, Michael P. Milham, R. Cameron Craddock A19 Detecting task-based fMRI compliance using plan abandonment techniques Ramon Fraga Pereira, Anibal Sólon Heinsfeld, Alexandre Rosa Franco, Augusto Buchweitz, Felipe Meneguzzi A20 Self-organization and brain function Jörg P. Pfannmöller, Rickson Mesquita, Luis C.T. Herrera, Daniela Dentico A21 The Neuroimaging Data Model (NIDM) API Vanessa Sochat, B Nolan Nichols A22 NeuroView: a customizable browser-base utility Anibal Sólon Heinsfeld, Alexandre Rosa Franco, Augusto Buchweitz, Felipe Meneguzzi A23 DIPY: Brain tissue classification Julio E. Villalon-Reina, Eleftherios Garyfallidis
Conference Paper
Full-text available
Purpose To investigate self-organizing properties of the ocular dominance columns in the primary visual cortex computationally, using the Swift-Hohenberg equation (Hohenberg and Swift 1992). Introduction Self-organization is a fundamental property of complex systems, describing the order spontaneously arising by the local interactions of the system components not mediated by top-down inputs. Though self-organizing systems typically possess a large number of components and exhibit complex dynamics, their evolution is deterministic and governed by a small number of order parameters. This property is used here to model the self-organization of the ocular dominance columns of the striate cortex in patterns of neighboring stripes, which respond preferentially to inputs from the left or the right eye. Methods The Swift-Hohenberg equation was used to model the self-organization of the ocular dominance columns. There are two order parameters in this equation, the first one determines the spatial wavelength λ of the stripes and the second one the branchiness ε of the pattern. The algorithm used to generate the results has been modified from an open source script. Results Figures (a), (b) and (c) show the temporal evolution of the solution to the Swift-Hohenberg equation for random initial conditions (a), constant ε and time increasing from (a) to (c). In (c), (d) and (e) three solutions with different ε are shown. The branchiness increases with ε from (c) to (e). The wavelength λ was set to the same value in all figures and the pattern in (d) is similar to the ocular dominance layers found in the visual cortex. Conclusion A simple model suffices to study basic properties of ocular dominance self-organization. Possibly, a combination with models for self-organization in neighboring cortical layers would allow to investigate higher organizational principles of the cortex (Reichel et al. 2012), e.g. the coordination between ocular dominance, orientation, and cytochrome oxidase.
Article
Full-text available
Purpose The purpose of this study is to determine the effectiveness of an image analysis method called parametric response mapping (PRM) in predicting changes related to progression of Alzheimer’s disease (AD). Methods Forty patients were obtained from the ADNI database. Each patient underwent longitudinal 18F Fludeoxyglucose (FDG) positron emission tomography (PET) imaging. The patients were divided into four groups, the NC group (n = 10) with stable normal control (NC) patients, the NC-to-MCI group (n = 10) with converting patients from NC to mild cognitive impairment (MCI), the MCI group (n = 10) with stable MCI patients, and the MCI-to-AD group (n = 10) with converting patients from MCI to AD. The PRM approach was applied to longitudinal PET scans using the following procedures; 1) Longitudinal scans were co-registered, 2) intensities were normalized using the cerebral mask, 3) an expert drew regions of interest (ROIs) for tumor volumes, and 4) voxels were classified into unchanged, increased, and decreased categories. Results PRM analysis results were analyzed with linear regression and the slopes of regression lines corresponding to different groups were compared. The PRM analysis was able to distinguish between the NC and NC-to-MCI group with p-value 0.05, while it was not able to distinguish between the MCI and the MCI-to-AD group with p-value 0.54. Conclusions An image analysis method, PRM approach, was able to distinguish between stable NC patients and converting patients from NC to MCI.
Article
Full-text available
Biofeedback-assisted modulation of electrocortical activity has been established to have intrinsic clinical benefits and has been shown to improve cognitive performance in healthy humans. In order to further investigate the pedagogic relevance of electroencephalograph (EEG) biofeedback (neurofeedback) for enhancing normal function, a series of investigations assessed the training's impact on an ecologically valid real-life behavioural performance measure: music performance under stressful conditions in conservatoire students. In a pilot study, single-blind expert ratings documented improvements in musical performance in a student group that received training on attention and relaxation related neurofeedback protocols, and improvements were highly correlated with learning to progressively raise theta (5–8 Hz) over alpha (8–11 Hz) band amplitudes. These findings were replicated in a second experiment where an alpha/theta training group displayed significant performance enhancement not found with other neurofeedback training protocols or in alternative interventions, including the widely applied Alexander technique.
Article
Full-text available
Mechanisms of cortical reorganization underlying the enhancement of speech processing have been poorly investigated. In the present study, we addressed changes in functional and effective connectivity induced in subjects who learned to deliberately increase activation in the right inferior frontal gyrus (rIFG), and improved their ability to identify emotional intonations by using a real-time fMRI Brain-Computer Interface. At the beginning of their training process, we observed a massive connectivity of the rIFG to a widespread network of frontal and temporal areas, which decreased and lateralized to the right hemisphere with practice. Volitional control of activation strengthened connectivity of this brain region to the right prefrontal cortex, whereas training increased its connectivity to bilateral precentral gyri. These findings suggest that changes of connectivity in a functionally specific manner play an important role in the enhancement of speech processing. Also, these findings support previous accounts suggesting that motor circuits play a role in the comprehension of speech.
Article
Full-text available
In recent years, multivariate pattern analyses have been performed on functional magnetic resonance imaging (fMRI) data, permitting prediction of mental states from local patterns of blood oxygen-level-dependent (BOLD) signal across voxels. We previously demonstrated that it is possible to predict the position of individuals in a virtual-reality environment from the pattern of activity across voxels in the hippocampus. Although this shows that spatial memories can be decoded, substantially more challenging, and arguably only possible to investigate in humans, is whether it is feasible to predict which complex everyday experience, or episodic memory, a person is recalling. Here we document for the first time that traces of individual rich episodic memories are detectable and distinguishable solely from the pattern of fMRI BOLD signals across voxels in the human hippocampus. In so doing, we uncovered a possible functional topography in the hippocampus, with preferential episodic processing by some hippocampal regions over others. Moreover, our results imply that the neuronal traces of episodic memories are stable (and thus predictable) even over many re-activations. Finally, our data provide further evidence for functional differentiation within the medial temporal lobe, in that we show the hippocampus contains significantly more episodic information than adjacent structures.
Article
Full-text available
The differential diagnosis of disorders of consciousness is challenging. The rate of misdiagnosis is approximately 40%, and new methods are required to complement bedside testing, particularly if the patient's capacity to show behavioral signs of awareness is diminished. At two major referral centers in Cambridge, United Kingdom, and Liege, Belgium, we performed a study involving 54 patients with disorders of consciousness. We used functional magnetic resonance imaging (MRI) to assess each patient's ability to generate willful, neuroanatomically specific, blood-oxygenation-level-dependent responses during two established mental-imagery tasks. A technique was then developed to determine whether such tasks could be used to communicate yes-or-no answers to simple questions. Of the 54 patients enrolled in the study, 5 were able to willfully modulate their brain activity. In three of these patients, additional bedside testing revealed some sign of awareness, but in the other two patients, no voluntary behavior could be detected by means of clinical assessment. One patient was able to use our technique to answer yes or no to questions during functional MRI; however, it remained impossible to establish any form of communication at the bedside. These results show that a small proportion of patients in a vegetative or minimally conscious state have brain activation reflecting some awareness and cognition. Careful clinical examination will result in reclassification of the state of consciousness in some of these patients. This technique may be useful in establishing basic communication with patients who appear to be unresponsive.
Article
Full-text available
Tinnitus consists of a more or less constant aversive tone or noise and is associated with excess auditory activation. Transient distortion of this activation (repetitive transcranial magnetic stimulation, rTMS) may improve tinnitus. Recently proposed operant training in real-time functional magnetic resonance imaging (rtfMRI) neurofeedback allows voluntary modification of specific circumscribed neuronal activations. Combining these observations, we investigated whether patients suffering from tinnitus can (1) learn to voluntarily reduce activation of the auditory system by rtfMRI neurofeedback and whether (2) successful learning improves tinnitus symptoms. Six participants with chronic tinnitus were included. First, location of the individual auditory cortex was determined in a standard fMRI auditory block-design localizer. Then, participants were trained to voluntarily reduce the auditory activation (rtfMRI) with visual biofeedback of the current auditory activation. Auditory activation significantly decreased after rtfMRI neurofeedback. This reduced the subjective tinnitus in two of six participants. These preliminary results suggest that tinnitus patients learn to voluntarily reduce spatially specific auditory activations by rtfMRI neurofeedback and that this may reduce tinnitus symptoms. Optimized training protocols (frequency, duration, etc.) may further improve the results.
Article
Full-text available
We report that visual stimulation produces an easily detectable (5-20%) transient increase in the intensity of water proton magnetic resonance signals in human primary visual cortex in gradient echo images at 4-T magnetic-field strength. The observed changes predominantly occur in areas containing gray matter and can be used to produce high-spatial-resolution functional brain maps in humans. Reducing the image-acquisition echo time from 40 msec to 8 msec reduces the amplitude of the fractional signal change, suggesting that it is produced by a change in apparent transverse relaxation time T*2. The amplitude, sign, and echo-time dependence of these intrinsic signal changes are consistent with the idea that neural activation increases regional cerebral blood flow and concomitantly increases venous-blood oxygenation.
Article
Full-text available
In this study the use of functional MRI (fMRI) for measuring language lateralization non-invasively was examined. The subjects were seven patients with histories of temporal lobe epilepsy who had undergone Wada testing for pre-surgical evaluation. Four patients were left-hemisphere-dominant and three were right-hemisphere-dominant for language. They received fMRI scans while they made semantic or perceptual judgments about visually presented words. Regions of the inferior frontal gyrus (pars triangularis and pars orbitalis) and neighbouring orbital cortex, corresponding to portions of Brodmann areas 45, 46 and 47, exhibited significant increases in activation during semantic relative to perceptual judgments. Lateralization of the increases in activation were consistent with the Wada test assessments of hemispheric language dominance in each of the seven patients. These results suggest that, in addition to providing a tool for investigating human cognitive processes, fMRI has significant clinical potential as a non-invasive measure of language lateralization.
Article
Full-text available
To support the hypothesis about the potential compensatory role of ipsilateral corticofugal pathways when the contralateral pathways are impaired by brain tumours. Retrospective analysis was carried out on the results of functional MRI (fMRI) of a selected group of five paretic patients with Rolandic brain tumours who exhibited an abnormally high ipsilateral/contralateral ratio of activation-that is, movements of the paretic hand activated predominately the ipsilateral cortex. Brain activation was achieved with a flexion extension of the fingers. Statistical parametric activation was obtained using a t test and a threshold of p<0.001. These patients, candidates for tumour resection, also underwent cortical intraoperative stimulation that was correlated to the fMRI spatial data using three dimensional reconstructions of the brain. Three patients also had postoperative control fMRI. The absence of fMRI activation of the primary sensorimotor cortex normally innervating the paretic hand for the threshold chosen, was correlated with completely negative cortical responses of the cortical hand area during the operation. The preoperative fMRI activation of these patients predominantly found in the ipsilateral frontal and primary sensorimotor cortices could be related to the residual ipsilateral hand function. Postoperatively, the fMRI activation returned to more classic patterns of activation, reflecting the consequences of therapy. In paretic patients with brain tumours, ipsilateral control could be implicated in the residual hand function, when the normal primary pathways are impaired. The possibility that functional tissue still remains in the peritumorous sensorimotor cortex even when the preoperative fMRI and the cortical intraoperative stimulations are negative, should be taken into account when planning the tumour resection and during the operation.
Article
Full-text available
The functional architecture of the object vision pathway in the human brain was investigated using functional magnetic resonance imaging to measure patterns of response in ventral temporal cortex while subjects viewed faces, cats, five categories of man-made objects, and nonsense pictures. A distinct pattern of response was found for each stimulus category. The distinctiveness of the response to a given category was not due simply to the regions that responded maximally to that category, because the category being viewed also could be identified on the basis of the pattern of response when those regions were excluded from the analysis. Patterns of response that discriminated among all categories were found even within cortical regions that responded maximally to only one category. These results indicate that the representations of faces and objects in ventral temporal cortex are widely distributed and overlapping.
Article
Full-text available
Motor rehabilitation therapy is commonly employed after strokes, but outcomes are variable and there is little specific information about the changes in brain activity that are associated with improved function. We performed serial functional MRI (fMRI) on a group of seven patients receiving a form of rehabilitation therapy after stroke in order to characterize functional changes in the brain that correlate with behavioural improvements. Patients were scanned while performing a hand flexion-extension movement twice before and twice after a two-week home-based therapy programme combining restraint of the unaffected limb with progressive exercises for the affected limb. As expected, the extent of improvement in hand function after therapy varied between patients. Therapy-related improvements in hand function correlated with increases in fMRI activity in the premotor cortex and secondary somatosensory cortex contralateral to the affected hand, and in superior posterior regions of the cerebellar hemispheres bilaterally (Crus I and lobule VI). fMRI offers a promising, objective approach for specifically identifying changes in brain activity potentially responsible for rehabilitation-mediated recovery of function after stroke. Our results suggest that activity changes in sensorimotor regions are associated with successful motor rehabilitation.
Article
Full-text available
Here we describe a functional magnetic resonance imaging study of humans engaged in memory search during a free recall task. Patterns of cortical activity associated with the study of three categories of pictures (faces, locations, and objects) were identified by a pattern-classification algorithm. The algorithm was used to track the reappearance of these activity patterns during the recall period. The reappearance of a given category's activity pattern correlates with verbal recalls made from that category and precedes the recall event by several seconds. This result is consistent with the hypothesis that category-specific activity is cueing the memory system to retrieve studied items.
Article
Full-text available
We used functional magnetic resonance imaging to demonstrate preserved conscious awareness in a patient fulfilling the criteria for a diagnosis of vegetative state. When asked to imagine playing tennis or moving around her home, the patient activated predicted cortical areas in a manner indistinguishable from that of healthy volunteers.
Article
Full-text available
A challenging goal in neuroscience is to be able to read out, or decode, mental content from brain activity. Recent functional magnetic resonance imaging (fMRI) studies have decoded orientation, position and object category from activity in visual cortex. However, these studies typically used relatively simple stimuli (for example, gratings) or images drawn from fixed categories (for example, faces, houses), and decoding was based on previous measurements of brain activity evoked by those same stimuli or categories. To overcome these limitations, here we develop a decoding method based on quantitative receptive-field models that characterize the relationship between visual stimuli and fMRI activity in early visual areas. These models describe the tuning of individual voxels for space, orientation and spatial frequency, and are estimated directly from responses evoked by natural images. We show that these receptive-field models make it possible to identify, from a large set of completely novel natural images, which specific image was seen by an observer. Identification is not a mere consequence of the retinotopic organization of visual areas; simpler receptive-field models that describe only spatial tuning yield much poorer identification performance. Our results suggest that it may soon be possible to reconstruct a picture of a person's visual experience from measurements of brain activity alone.
Article
Full-text available
The assessment of residual brain function in the vegetative state, is extremely difficult and depends frequently on subjective interpretations of observed spontaneous and volitional behaviors. For those patients who retain peripheral motor function, rigorous behavioral assessment supported by structural imaging and electrophysiology is usually sufficient to establish a patient's level of wakefulness and awareness. However, it is becoming increasingly apparent that, in some patients, damage to the peripheral motor system may prevent overt responses to command, even though the cognitive ability to perceive and understand such commands may remain intact. Advances in functional neuroimaging suggest a novel solution to this problem; in several recent cases, so-called "activation" studies have been used to identify residual cognitive function and even conscious awareness in patients who are assumed to be vegetative, yet retain cognitive abilities that have evaded detection using standard clinical methods.
Article
Motor rehabilitation therapy is commonly employed after strokes, but outcomes are variable and there is little specific information about the changes in brain activity that are associated with improved function. We performed serial functional MRI (fMRI) on a group of seven patients receiving a form of rehabilitation therapy after stroke in order to characterize functional changes in the brain that correlate with behavioural improvements. Patients were scanned while performing a hand flexion–extension movement twice before and twice after a two‐week home‐based therapy programme combining restraint of the unaffected limb with progressive exercises for the affected limb. As expected, the extent of improvement in hand function after therapy varied between patients. Therapy‐related improvements in hand function correlated with increases in fMRI activity in the premotor cortex and secondary somatosensory cortex contralateral to the affected hand, and in superior posterior regions of the cerebellar hemispheres bilaterally (Crus I and lobule VI). fMRI offers a promising, objective approach for specifically identifying changes in brain activity potentially responsible for rehabilitation‐mediated recovery of function after stroke. Our results suggest that activity changes in sensorimotor regions are associated with successful motor rehabilitation. Received January 1, 2002. Revised March 19, 2002. Second revision June 27, 2002. Accepted June 28, 2002
Article
OBJECTIVE To support the hypothesis about the potential compensatory role of ipsilateral corticofugal pathways when the contralateral pathways are impaired by brain tumours. METHODS Retrospective analysis was carried out on the results of functional MRI (fMRI) of a selected group of five paretic patients with Rolandic brain tumours who exhibited an abnormally high ipsilateral/contralateral ratio of activation—that is, movements of the paretic hand activated predominately the ipsilateral cortex. Brain activation was achieved with a flexion extension of the fingers. Statistical parametric activation was obtained using a t test and a threshold of p<0.001. These patients, candidates for tumour resection, also underwent cortical intraoperative stimulation that was correlated to the fMRI spatial data using three dimensional reconstructions of the brain. Three patients also had postoperative control fMRI. RESULTS The absence of fMRI activation of the primary sensorimotor cortex normally innervating the paretic hand for the threshold chosen, was correlated with completely negative cortical responses of the cortical hand area during the operation. The preoperative fMRI activation of these patients predominantly found in the ipsilateral frontal and primary sensorimotor cortices could be related to the residual ipsilateral hand function. Postoperatively, the fMRI activation returned to more classic patterns of activation, reflecting the consequences of therapy. CONCLUSION In paretic patients with brain tumours, ipsilateral control could be implicated in the residual hand function, when the normal primary pathways are impaired. The possibility that functional tissue still remains in the peritumorous sensorimotor cortex even when the preoperative fMRI and the cortical intraoperative stimulations are negative, should be taken into account when planning the tumour resection and during the operation.
Article
A recursive algorithm suitable for functional magnetic resonance imaging (FMRI) calculations is presented. The correlation coefficient of a time course of images with a reference time series, with the mean and any linear trend projected out, may be computed with 22 operations per voxel, per image; the storage overhead is four numbers per voxel. A statistical model for the FMRI signal is presented, and thresholds for the correlation coefficient are derived from it. Selected images from the first real-time functional neuroimaging experiment (at 3 Tesla) are presented. Using a 50-MHz workstation equipped with a 14-bit analog-to-digital converter, each echo planar image was acquired, reconstructed, correlated, thresh-olded, and displayed in pseudocolor (highlighting active regions in the brain) within 500 ms of the RF pulse.
Article
This paper presents a general approach to the analysis of functional MRI time-series from one or more subjects. The approach is predicated on an extension of the general linear model that allows for correlations between error terms due to physiological noise or correlations that ensue after temporal smoothing. This extension uses the effective degrees of freedom associated with the error term. The effective degrees of freedom are a simple function of the number of scans and the temporal autocorrelation function. A specific form for the latter can be assumed if the data are smoothed, in time, to accentuate hemodynamic responses with a neural basis. This assumption leads to an expedient implementation of a flexible statistical framework. The importance of this small extension is that, in contradistinction to our previous approach, any parametric statistical analysis can be implemented. We demonstrate this point using a multiple regression analysis that tests for effects of interest (activations due to word generation), while taking explicit account of some obvious confounds.
Conference Paper
A 'Virtual Keyboard' (VK) is a letter spelling device operated by spontaneous EEG, whereby the EEG is modulated by mental hand and leg motor imagery. We report on a tetraplegic patient initially trained on an EEG-based orthosis control system operating the VK. He achieved an outstanding ability in the use of the VK and can operate it with 0.95 letters per minute.
Article
The brain's electrical signals enable people without muscle control to physically interact with the world.
Article
Estimating moment-to-moment changes in blood oxygenation level dependent (BOLD) activation levels from functional magnetic resonance imaging (fMRI) data has applications for learned regulation of regional activation, brain state monitoring, and brain-machine interfaces. In each of these contexts, accurate estimation of the BOLD signal in as little time as possible is desired. This is a challenging problem due to the low signal-to-noise ratio of fMRI data. Previous methods for real-time fMRI analysis have either sacrificed the ability to compute moment-to-moment activation changes by averaging several acquisitions into a single activation estimate or have sacrificed accuracy by failing to account for prominent sources of noise in the fMRI signal. Here we present a new method for computing the amount of activation present in a single fMRI acquisition that separates moment-to-moment changes in the fMRI signal intensity attributable to neural sources from those due to noise, resulting in a feedback signal more reflective of neural activation. This method computes an incremental general linear model fit to the fMRI time series, which is used to calculate the expected signal intensity at each new acquisition. The difference between the measured intensity and the expected intensity is scaled by the variance of the estimator in order to transform this residual difference into a statistic. Both synthetic and real data were used to validate this method and compare it to the only other published real-time fMRI method.
Article
A popular method for investigating whether stimulus information is present in fMRI response patterns is to attempt to "decode" the stimuli from the response patterns with a multivariate classifier. The sensitivity for detecting the information depends on the particular classifier used. However, little is known about the relative performance of different classifiers on fMRI data. Here we compared six multivariate classifiers and investigated how the response-amplitude estimate used (beta- or t-value) and different pattern normalizations affect classification performance. The compared classifiers were a pattern-correlation classifier, a k-nearest-neighbors classifier, Fisher's linear discriminant, Gaussian naïve Bayes, and linear and nonlinear (radial-basis-function kernel) support vector machines. We compared these classifiers' accuracy at decoding the category of visual objects from response patterns in human early visual and inferior temporal cortex acquired in an event-related design with BOLD fMRI at 3T using SENSE and isotropic voxels of about 2-mm width. Overall, Fisher's linear discriminant (with an optimal-shrinkage covariance estimator) and the linear support vector machine performed best. The pattern-correlation classifier often performed similarly as those two classifiers. The nonlinear classifiers never performed better and sometimes significantly worse than the linear classifiers, suggesting overfitting. Defining response patterns by t-values (or in error-standard-deviation units) rather than by beta estimates (in % signal change) to define the patterns appeared advantageous. Cross-validation by a leave-one-stimulus-pair-out method gave higher accuracies than a leave-one-run-out method, suggesting that generalization to independent runs (which more safely ensures independence of the test set) is more challenging than generalization to novel stimuli within the same category. Independent selection of fewer more visually responsive voxels tended to yield better decoding performance for all classifiers. Normalizing mean and standard deviation of the response patterns either across stimuli or across voxels had no significant effect on decoding performance. Overall our results suggest that linear decoders based on t-value patterns may perform best in the present scenario of visual object representations measured for about 60min per subject with 3T fMRI.
Article
Using multivariate pattern analysis of functional magnetic resonance imaging data, we found that the subjective experience of sound, in the absence of auditory stimulation, was associated with content-specific activity in early auditory cortices in humans. As subjects viewed sound-implying, but silent, visual stimuli, activity in auditory cortex differentiated among sounds related to various animals, musical instruments and objects. These results support the idea that early sensory cortex activity reflects perceptual experience, rather than sensory stimulation alone.
Article
Monitoring patients' cooperation for a given cognitive task is required in the clinical fMRI. The current study is aiming to develop a real-time fMRI platform to monitor the patient performance during the language task, which is usually done covertly. Successive estimation of task-related activation, deactivation and inter-regional connectivity was performed to evaluate patient's task involvement. The effect of scan numbers was assessed to determine minimal scan numbers in detecting the brain status. The preliminary results demonstrated the feasibility of real-time patient monitoring in the neurosurgical application of fMRI.
Article
Recent studies have used fMRI signals from early visual areas to reconstruct simple geometric patterns. Here, we demonstrate a new Bayesian decoder that uses fMRI signals from early and anterior visual areas to reconstruct complex natural images. Our decoder combines three elements: a structural encoding model that characterizes responses in early visual areas, a semantic encoding model that characterizes responses in anterior visual areas, and prior information about the structure and semantic content of natural images. By combining all these elements, the decoder produces reconstructions that accurately reflect both the spatial structure and semantic category of the objects contained in the observed natural image. Our results show that prior information has a substantial effect on the quality of natural image reconstructions. We also demonstrate that much of the variance in the responses of anterior visual areas to complex natural images is explained by the semantic category of the image alone.
Article
Perceptual experience consists of an enormous number of possible states. Previous fMRI studies have predicted a perceptual state by classifying brain activity into prespecified categories. Constraint-free visual image reconstruction is more challenging, as it is impractical to specify brain activity for all possible images. In this study, we reconstructed visual images by combining local image bases of multiple scales, whose contrasts were independently decoded from fMRI activity by automatically selecting relevant voxels and exploiting their correlated patterns. Binary-contrast, 10 x 10-patch images (2(100) possible states) were accurately reconstructed without any image prior on a single trial or volume basis by measuring brain activity only for several hundred random images. Reconstruction was also used to identify the presented image among millions of candidates. The results suggest that our approach provides an effective means to read out complex perceptual states from brain activity while discovering information representation in multivoxel patterns.
Article
Concealed information, which is information only known to oneself is sometimes crucial for criminal investigation. In this study, we examined cortical activations related to incidental responses to concealed information. We found that cortical responses to stimuli related to concealed information were different from those to other stimuli; the bilateral ventrolateral prefrontal (VLPF) areas, left inferior frontal gyrus, right middle frontal gyrus and right inferior parietal lobule were activated, and among those activated areas, the right VLPF was found to be crucial. Furthermore, we examined by discriminant analysis which cortical areas contribute to the determination of whether the subjects had concealed information. On the basis of the activity in the right VLPF, we were able to correctly identify 32 of the 38 subjects (84.21%) as who had concealed information. These results suggest that the right VLPF may play a crucial role in the incidental processing of concealed information, and we were able to determine whether a subject had concealed information without the need for deceptive responses.
Article
Real-time functional MRI (rtfMRI) has been used as a basis for brain-computer interface (BCI) due to its ability to characterize region-specific brain activity in real-time. As an extension of BCI, we present an rtfMRI-based brain-machine interface (BMI) whereby 2-dimensional movement of a robotic arm was controlled by the regulation (and concurrent detection) of regional cortical activations in the primary motor areas. To do so, the subjects were engaged in the right- and/or left-hand motor imagery tasks. The blood oxygenation level dependent (BOLD) signal originating from the corresponding hand motor areas was then translated into horizontal or vertical robotic arm movement. The movement was broadcasted visually back to the subject as a feedback. We demonstrated that real-time control of the robotic arm only through the subjects' thought processes was possible using the rtfMRI-based BMI trials.
Article
Some of the implications for law of recent discoveries in neuroscience are considered in a new program established by the MacArthur Foundation. A group of neuroscientists, lawyers, philosophers, and jurists are examining issues in criminal law and, in particular, problems in responsibility and prediction and problems in legal decision making.
Article
To compare self-regulation of low-frequency EEG components (slow cortical potentials, SCPs) with other methods of seizure control for patients with drug-refractory partial epilepsy and to separate the real anticonvulsive effect from placebo effects. Results of a treatment program of SCP self-regulation (experimental group) are compared with two groups of patients, one of which learned self-control of respiratory parameters (end-tidal CO2 and respiration rate: RES group); the other received medication with new anticonvulsive drugs (AEDs) in combination with psychosocial counseling (MED group). Clinical, cognitive, behavioral, and personality measures were assessed before and after treatment. In addition, to control for placebo responses, patients repeatedly estimated their beliefs in the efficiency of the respective treatment, their satisfaction and expectations, and the quality of the relationship with their therapists. SCP and MED groups showed a significant decrease of seizure frequency, but the RES group did not. Clear positive changes in the sociopsychological adjustment were obtained in all three groups, with the maximal improvement being attained in the RES group. All kinds of therapy result in considerable improvement of patients' emotional state, which may in part be due to potential placebo effects: however, this improvement is not related to the quality of the therapeutic effect proper (i.e., seizure reduction). Traditional double-blind control group designs are inappropriate for behavioral interventions or treatments with psychoactive pharmacologic drugs. Rather, specific tests can be developed to control the placebo effect and to separate it from the genuine therapeutic effects.
Article
To test a training procedure designed to enable severely paralyzed patients to communicate by means of self-regulation of slow cortical potentials. Application of the Thought Translation Device to evaluate the procedure in patients with late-stage amyotrophic lateral sclerosis (ALS). Training sessions in the patients' homes. Two male patients with late-stage ALS. Patients learned voluntary control of their slow cortical potentials by means of an interface between the brain and a computer. Training was based on visual feedback of slow cortical potentials shifts and operant learning principles. The learning process was divided into small steps of increasing difficulty. Accuracy of self-control of slow cortical potentials (percentage of correct responses). Learning progress calculated as a function of training session. Within 3 to 8 weeks, both patients learned to self-regulate their slow cortical potentials and to use this skill to select letters or words in the Language Support Program. This training schedule is the first to enable severely paralyzed patients to communicate without any voluntary muscle control by using self-regulation of an electroencephalogram potential only. The protocol could be a model for training patients in other brain-computer interface techniques.
Article
Magnetic resonance imaging (MRI) has been shown to be useful in the detection of brain activity via the relatively indirect coupling of neural activity to cerebral blood flow and subsequently to magnetic resonance signal intensity. Recent technical advances have made possible the continuous collection of successive images at a rate rapid compared with such signal changes and in the statistical processing of these image time series to produce tomographic maps of brain activity in real time, with updates of 10 frames/s or better. We describe here our preferred method of real-time functional MRI and some of the early results we have obtained with its use.
Article
For many years people have speculated that electroencephalographic activity or other electrophysiological measures of brain function might provide a new non-muscular channel for sending messages and commands to the external world – a brain–computer interface (BCI). Over the past 15 years, productive BCI research programs have arisen. Encouraged by new understanding of brain function, by the advent of powerful low-cost computer equipment, and by growing recognition of the needs and potentials of people with disabilities, these programs concentrate on developing new augmentative communication and control technology for those with severe neuromuscular disorders, such as amyotrophic lateral sclerosis, brainstem stroke, and spinal cord injury. The immediate goal is to provide these users, who may be completely paralyzed, or ‘locked in’, with basic communication capabilities so that they can express their wishes to caregivers or even operate word processing programs or neuroprostheses. Present-day BCIs determine the intent of the user from a variety of different electrophysiological signals. These signals include slow cortical potentials, P300 potentials, and mu or beta rhythms recorded from the scalp, and cortical neuronal activity recorded by implanted electrodes. They are translated in real-time into commands that operate a computer display or other device. Successful operation requires that the user encode commands in these signals and that the BCI derive the commands from the signals. Thus, the user and the BCI system need to adapt to each other both initially and continually so as to ensure stable performance. Current BCIs have
Article
We have studied 56-channel electroencephalograms (EEG) from three subjects who planned and performed three kinds of movements, left and right index finger, and right foot movement. Using autoregressive modeling of EEG time series and artificial neural nets (ANN), we have developed a classifier that can tell which movement is performed from a segment of the EEG signal from a single trial. The classifier's rate of recognition of EEGs not seen before was 92-99% on the basis of a 1s segment per trial. The recognition rate provides a pragmatic measure of the information content of the EEG signal. This high recognition rate makes the classifier suitable for a so-called 'Brain-Computer Interface', a system that allows one to control a computer, or another device, with ones brain waves. Our classifier Laplace filters the EEG spatially, but makes use of its entire frequency range, and automatically locates regions of relevant activity on the skull.
Article
Biofeedback-assisted modulation of electrocortical activity has been established to have intrinsic clinical benefits and has been shown to improve cognitive performance in healthy humans. In order to further investigate the pedagogic relevance of electroencephalograph (EEG) biofeedback (neurofeedback) for enhancing normal function, a series of investigations assessed the training's impact on an ecologically valid real-life behavioural performance measure: music performance under stressful conditions in conservatoire students. In a pilot study, single-blind expert ratings documented improvements in musical performance in a student group that received training on attention and relaxation related neurofeedback protocols, and improvements were highly correlated with learning to progressively raise theta (5-8 Hz) over alpha (8-11 Hz) band amplitudes. These findings were replicated in a second experiment where an alpha/theta training group displayed significant performance enhancement not found with other neurofeedback training protocols or in alternative interventions, including the widely applied Alexander technique.
Article
A brain-computer interface (BCI) is a way of conveying an individual's thoughts to control computer or electromechanical hardware. Capitalizing on the ability to characterize brain activity in a reproducible manner, we explored the possibility of using real-time fMRI to interpret the spatial distribution of brain function as BCI commands. Using a high-field (3T) MRI scanner, brain activities associated with four distinct covert functional tasks were detected and subsequently translated into predetermined computer commands for moving four directional cursors. The proposed fMRI-BCI method allowed volunteer subjects to navigate through a simple 2D maze solely through their thought processes.
Article
To study the efficacy of mental imagery at promoting relearning for people after a stroke. Prospective, randomized controlled trial. An inpatient rehabilitation stroke unit in Hong Kong. Forty-six inpatients, 60 years of age or older, after a cerebral infarction. Patients were randomized to receive 15 sessions (1 h/d for 3 wk) of either the mental imagery program or the conventional functional training intervention on the relearning of daily living tasks. Performance of 15 trained and 5 untrained tasks, including household, cooking, and shopping tasks; and the Fugl-Meyer Assessment and Color Trails Test (CTT). Patients engaged in mental imagery-based intervention showed better relearning of both trained and untrained tasks compared with the control group (trained tasks: P<.005; untrained tasks: P<.001). They also demonstrated a greater ability to retain the trained tasks after 1 month and transfer the skills relearned to other untrained tasks (P<.001). However, among the various ability measures, the mental imagery group showed a significant increase in the CTT scores only after the intervention (P<.005). Mental imagery can be used as a training strategy to promote the relearning of daily tasks for people after an acute stroke. The imagery process is likely to improve the planning and execution of both the trained and the untrained (novel) tasks. The effect of its relearning appears to help patients to retain and generalize the skills and tasks learned in the rehabilitation program.
Article
It is not currently known whether subjects can learn to voluntarily control activation in localized regions of their own brain using neuroimaging. Here, we show that subjects were able to learn enhanced voluntary control over task-specific activation in a chosen target region, the somatomotor cortex. During an imagined manual action task, subjects were provided with continuous direction regarding their cognitive processes. Subjects received feedback information about their current level of activation in a target region of interest (ROI) derived using real-time functional magnetic resonance imaging (rtfMRI), and they received automatically-adjusted instructions for the level of activation to achieve. Information was provided both as continously upated graphs and using a simple virtual reality interface that provided an image analog of the level of activation. Through training, subjects achieved an enhancement in their control over brain activation that was anatomically specific to the target ROI, the somatomotor cortex. The enhancement took place when rtfMRI-based training was provided, but not in a control group that received similar training without rtfMRI information, showing that the effect was not due to conventional, practice-based neural plasticity alone. Following training, using cognitive processes alone subjects could volitionally induce fMRI activation in the somatomotor cortex that was comparable in magnitude to the activation observed during actual movement. The trained subjects increased fMRI activation without muscle tensing, and were able to continue to control brain activation even when real-time fMRI information was no longer provided. These results show that rtfMRI information can be used to direct cognitive processes, and that subjects are able to learn volitionally regulate activation in an anatomically-targeted brain region, surpassing the task-driven activation present before training.
Article
Brain-computer interfaces (BCIs) enable humans or animals to communicate or control external devices without muscle activity using electric brain signals. The BCI used here is based on self-regulation of slow cortical potentials (SCPs), a skill that most people and paralyzed patients can acquire with training periods of several hours up to months. The neurophysiological mechanisms and anatomical sources of SCPs and other event-related brain potentials have been described but the neural mechanisms underlying the self-regulation skill for the use of a BCI are unknown. To uncover the relevant areas of brain activation during regulation of SCPs, the BCI was combined with functional magnetic resonance imaging. The electroencephalogram was recorded inside the magnetic resonance imaging scanner in 12 healthy participants who learned to regulate their SCP with feedback and reinforcement. The results demonstrate activation of specific brain areas during execution of the brain regulation skill allowing a person to activate an external device; a successful positive SCP shift compared with a negative shift was closely related to an increase of the blood oxygen level-dependent response in the basal ganglia. Successful negativity was related to an increased blood oxygen level-dependent response in the thalamus compared with successful positivity. These results may indicate learned regulation of a cortico-striatal-thalamic loop modulating local excitation thresholds of cortical assemblies. The data support the assumption that human subjects learn the regulation of cortical excitation thresholds of large neuronal assemblies as a prerequisite for direct brain communication using an SCP-driven BCI. This skill depends critically on an intact and flexible interaction between the cortico-basal ganglia-thalamic circuits.
Article
Can the rapid stream of conscious experience be predicted from brain activity alone? Recently, spatial patterns of activity in visual cortex have been successfully used to predict feature-specific stimulus representations for both visible [1 • Kamitani Y. • Tong F. Decoding the subjective contents of the human brain.Nat. Neurosci. 2005; 8: 679-685 • Crossref • PubMed • Scopus (1174) • Google Scholar , 2 • Haynes J.D.H. • Rees G. Predicting the orientation of invisible stimuli from activity in primary visual cortex.Nat. Neurosci. 2005; 6: 686-696 • Crossref • Scopus (592) • Google Scholar ] and invisible [2 • Haynes J.D.H. • Rees G. Predicting the orientation of invisible stimuli from activity in primary visual cortex.Nat. Neurosci. 2005; 6: 686-696 • Crossref • Scopus (592) • Google Scholar ] stimuli. However, because these studies examined only the prediction of static and unchanging perceptual states during extended periods of stimulation, it remains unclear whether activity in early visual cortex can also predict the rapidly and spontaneously changing stream of consciousness [3 • James W. The Principles of Psychology. Henry Holt, New York1890 • Crossref • Google Scholar ]. Here, we used binocular rivalry [4 • Blake R. • Logothetis N.K. Visual competition.Nat. Rev. Neurosci. 2002; 3: 13-21 • Crossref • PubMed • Scopus (925) • Google Scholar ] to induce frequent spontaneous and stochastic changes in conscious experience without any corresponding changes in sensory stimulation, while measuring brain activity with fMRI. Using information that was present in the multivariate pattern of responses to stimulus features, we could accurately predict, and therefore track, participants’ conscious experience from the fMRI signal alone while it underwent many spontaneous changes. Prediction in primary visual cortex primarily reflected eye-based signals, whereas prediction in higher areas reflected the color of the percept. Furthermore, accurate prediction during binocular rivalry could be established with signals recorded during stable monocular viewing, showing that prediction generalized across viewing conditions and did not require or rely on motor responses. It is therefore possible to predict the dynamically changing time course of subjective experience with only brain activity.
Article
A key challenge for cognitive neuroscience is determining how mental representations map onto patterns of neural activity. Recently, researchers have started to address this question by applying sophisticated pattern-classification algorithms to distributed (multi-voxel) patterns of functional MRI data, with the goal of decoding the information that is represented in the subject's brain at a particular point in time. This multi-voxel pattern analysis (MVPA) approach has led to several impressive feats of mind reading. More importantly, MVPA methods constitute a useful new tool for advancing our understanding of neural information processing. We review how researchers are using MVPA methods to characterize neural coding and information processing in domains ranging from visual perception to memory search.
Article
Traditional inference in neuroimaging consists in describing brain activations elicited and modulated by different kinds of stimuli. Recently, however, paradigms have been studied in which the converse operation is performed, thus inferring behavioral or mental states associated with activation images. Here, we use the well-known retinotopy of the visual cortex to infer the visual content of real or imaginary scenes from the brain activation patterns that they elicit. We present two decoding algorithms: an explicit technique, based on the current knowledge of the retinotopic structure of the visual areas, and an implicit technique, based on supervised classifiers. Both algorithms predicted the stimulus identity with significant accuracy. Furthermore, we extend this principle to mental imagery data: in five data sets, our algorithms could reconstruct and predict with significant accuracy a pattern imagined by the subjects.
Article
Imaging techniques have been used to elucidate the neural correlates that underlie deception. The scientifically best understood paradigm for the detection of deception, however, the guilty knowledge test (GKT), was rarely used in imaging studies. By transferring a GKT-paradigm to a functional magnetic resonance imaging (fMRI) study, while additionally quantifying reaction times and skin conductance responses (SCRs), this study aimed at identifying the neural correlates of the behavioral and electrodermal response pattern typically found in GKT examinations. Prior to MR scanning, subjects viewed two specific items (probes) and were instructed to hide their knowledge of these. Two other specific items were designated as targets and required a different behavioral response during the experiment and eight items served as irrelevant stimuli. Reaction times and SCR amplitudes differed significantly between all three item types. The neuroimaging data revealed that right inferior frontal and mid-cingulate regions were more active for probe and target trials compared to irrelevants. Moreover, the differential activation in the right inferior frontal region was modulated by stimulus conflicts. These results were interpreted as an increased top-down influence on the stimulus-response-mapping for concealed and task-relevant items. Additionally, the influence of working memory and retrieval processes on this activation pattern is discussed. Using parametric analyses, reaction times and SCR amplitudes were found to be linearly related to activity in the cerebellum, the right inferior frontal cortex, and the supplementary motor area. This result provides a first link between behavioral measures, sympathetic arousal, and neural activation patterns during a GKT examination.
Article
When humans are engaged in goal-related processing, activity in prefrontal cortex is increased. However, it has remained unclear whether this prefrontal activity encodes a subject's current intention. Instead, increased levels of activity could reflect preparation of motor responses, holding in mind a set of potential choices, tracking the memory of previous responses, or general processes related to establishing a new task set. Here we study subjects who freely decided which of two tasks to perform and covertly held onto an intention during a variable delay. Only after this delay did they perform the chosen task and indicate which task they had prepared. We demonstrate that during the delay, it is possible to decode from activity in medial and lateral regions of prefrontal cortex which of two tasks the subjects were covertly intending to perform. This suggests that covert goals can be represented by distributed patterns of activity in the prefrontal cortex, thereby providing a potential neural substrate for prospective memory. During task execution, most information could be decoded from a more posterior region of prefrontal cortex, suggesting that different brain regions encode goals during task preparation and task execution. Decoding of intentions was most robust from the medial prefrontal cortex, which is consistent with a specific role of this region when subjects reflect on their own mental states.
Article
With the increasing interest in the neuroimaging of deception and its commercial application, there is a need to pay more attention to methodology. The weakness of studying deception in an experimental setting has been discussed intensively for over half a century. However, even though much effort has been put into their development, paradigms are still inadequate. The problems that bedevilled the old technology have not been eliminated by the new. Advances will only be possible if experiments are designed that take account of the intentions of the subject and the context in which these occur.
Article
There is considerable interest in the use of neuroimaging techniques for forensic purposes. Memory detection techniques, including the well-publicized Brain Fingerprinting technique (Brain Fingerprinting Laboratories, Inc., Seattle WA), exploit the fact that the brain responds differently to sensory stimuli to which it has been exposed before. When a stimulus is specifically associated with a crime, the resulting brain activity should differentiate between someone who was present at the crime and someone who was not. This article reviews the scientific literature on three such techniques: priming, old/new, and P300 effects. The forensic potential of these techniques is evaluated based on four criteria: specificity, automaticity, encoding flexibility, and longevity. This article concludes that none of the techniques are devoid of forensic potential, although much research is yet to be done. Ethical issues, including rights to privacy and against self-incrimination, are discussed. A discussion of legal issues concludes that current memory detection techniques do not yet meet United States standards of legal admissibility.
Article
The question of how the human brain represents conceptual knowledge has been debated in many scientific fields. Brain imaging studies have shown that different spatial patterns of neural activation are associated with thinking about different semantic categories of pictures and words (for example, tools, buildings, and animals). We present a computational model that predicts the functional magnetic resonance imaging (fMRI) neural activation associated with words for which fMRI data are not yet available. This model is trained with a combination of data from a trillion-word text corpus and observed fMRI data associated with viewing several dozen concrete nouns. Once trained, the model predicts fMRI activation for thousands of other concrete nouns in the text corpus, with highly significant accuracies over the 60 nouns for which we currently have fMRI data.
Article
A "virtual keyboard" (VK) is a letter spelling device operated for example by spontaneous electroencephalogram (EEG), whereby the EEC is modulated by mental hand and leg motor imagery. We report on three able-bodied subjects, operating the VK. The ability in the use of the VK varies between 0.85 and 0.5 letters/min in error-free writing.