Instance-based learning with prototype reduction for real-time proportional myocontrol: a randomized user study demonstrating accuracy-preserving data reduction for prosthetic embedded systems.

This work presents the design, implementation and validation of learning techniques based on the kNN scheme for gesture detection in prosthetic control. To cope with high computational demands in instance-based prediction, methods of dataset reduction are evaluated considering real-time determinism to allow for the reliable integration into battery-powered portable devices. The influence of parameterization and varying proportionality schemes is analyzed, utilizing an eight-channel-sEMG armband. Besides offline cross-validation accuracy, success rates in real-time pilot experiments (online target achievement tests) are determined. Based on the assessment of specific dataset reduction techniques' adequacy for embedded control applications regarding accuracy and timing behaviour, decision surface mapping (DSM) proves itself promising when applying kNN on the reduced set. A randomized, double-blind user study was conducted to evaluate the respective methods (kNN and kNN with DSM-reduction) against ridge regression (RR) and RR with random Fourier features (RR-RFF). The kNN-based methods performed significantly better ([Formula: see text]) than the regression techniques. Between DSM-kNN and kNN, there was no statistically significant difference (significance level 0.05). This is remarkable in consideration of only one sample per class in the reduced set, thus yielding a reduction rate of over 99% while preserving success rate. The same behaviour could be confirmed in an extended user study. With [Formula: see text], which turned out to be an excellent choice, the runtime complexity of both kNN (in every prediction step) as well as DSM-kNN (in the training phase) becomes linear concerning the number of original samples, favouring dependable wearable prosthesis applications.

Progressive unsupervised control of myoelectric upper limbs.

Objective.Unsupervised myocontrol methods aim to create control models for myoelectric prostheses while avoiding the complications of acquiring reliable, regular, and sufficient labeled training data. A limitation of current unsupervised methods is that they fix the number of controlled prosthetic functions a priori, thus requiring an initial assessment of the user's motor skills and neglecting the development of novel motor skills over time.Approach.We developed a progressive unsupervised myocontrol (PUM) paradigm in which the user and the control model coadaptively identify distinct muscle synergies, which are then used to control arbitrarily associated myocontrol functions, each corresponding to a hand or wrist movement. The interaction starts with learning a single function and the user may request additional functions after mastering the available ones, which aligns the evolution of their motor skills with an increment in system complexity. We conducted a multi-session user study to evaluate PUM and compare it against a state-of-the-art non-progressive unsupervised alternative. Two participants with congenital upper-limb differences tested PUM, while ten non-disabled control participants tested either PUM or the non-progressive baseline. All participants engaged in myoelectric control of a virtual hand and wrist.Main results.PUM enabled autonomous learning of three myocontrol functions for participants with limb differences, and of all four available functions for non-disabled subjects, using both existing or newly identified muscle synergies. Participants with limb differences achieved similar success rates to non-disabled ones on myocontrol tests, but faced greater difficulties in internalizing new motor skills and exhibited slightly inferior movement quality. The performance was comparable with either PUM or the non-progressive baseline for the group of non-disabled participants.Significance.The PUM paradigm enables users to autonomously learn to operate the myocontrol system, adapts to the users' varied preexisting motor skills, and supports the further development of those skills throughout practice.

Simultaneous assessment and training of an upper-limb amputee using incremental machine-learning-based myocontrol: a single-case experimental design.

BACKGROUND: Machine-learning-based myocontrol of prosthetic devices suffers from a high rate of abandonment due to dissatisfaction with the training procedure and with the reliability of day-to-day control. Incremental myocontrol is a promising approach as it allows on-demand updating of the system, thus enforcing continuous interaction with the user. Nevertheless, a long-term study assessing the efficacy of incremental myocontrol is still missing, partially due to the lack of an adequate tool to do so. In this work we close this gap and report about a person with upper-limb absence who learned to control a dexterous hand prosthesis using incremental myocontrol through a novel functional assessment protocol called SATMC (Simultaneous Assessment and Training of Myoelectric Control). METHODS: The participant was fitted with a custom-made prosthetic setup with a controller based on Ridge Regression with Random Fourier Features (RR-RFF), a non-linear, incremental machine learning method, used to build and progressively update the myocontrol system. During a 13-month user study, the participant performed increasingly complex daily-living tasks, requiring fine bimanual coordination and manipulation with a multi-fingered hand prosthesis, in a realistic laboratory setup. The SATMC was used both to compose the tasks and continually assess the participant's progress. Patient satisfaction was measured using Visual Analog Scales. RESULTS: Over the course of the study, the participant progressively improved his performance both objectively, e.g., the time required to complete each task became shorter, and subjectively, meaning that his satisfaction improved. The SATMC actively supported the improvement of the participant by progressively increasing the difficulty of the tasks in a structured way. In combination with the incremental RR-RFF allowing for small adjustments when required, the participant was capable of reliably using four actions of the prosthetic hand to perform all required tasks at the end of the study. CONCLUSIONS: Incremental myocontrol enabled an upper-limb amputee to reliably control a dexterous hand prosthesis while providing a subjectively satisfactory experience. The SATMC can be an effective tool to this aim.

Mammillary body and hypothalamic volumes in mood disorders.

We have previously reported an in vivo enlargement of the left hypothalamus in mood disorders using 7 T magnetic resonance imaging. The aim of this follow-up study was to find out whether the hypothalamic volume difference may be located in the mammillary bodies (MB) rather than being widespread across the hypothalamus. We developed and evaluated a detailed segmentation algorithm that allowed a reliable segmentation of the MBs, and applied it to 20 unmedicated (MDDu) and 20 medicated patients with major depressive disorder, 21 medicated patients with bipolar disorder, and 23 controls. 20 out of 23 healthy controls were matched to the MDDu. We tested for group differences in MB and hypothalamus without MB (HTh) volumes using analyses of covariance. Associations between both volumes of interest were analysed using bivariate and partial correlations. In contrast to postmortem findings, we found no statistically significant differences of the MB volumes between the study groups. Left HTh volumes differed significantly across the study groups after correction for intracranial volume (ICV) and for ICV and sex. Our result of an HTh enlargement in mood disorders was confirmed by a paired t-test between the matched pairs of MDDu and healthy controls using the native MB and HTh volumes. In the whole sample, MB volumes correlated significantly with the ipsilateral HTh volumes. Our results indicate a structural relationship between both volumes, and that our previous in vivo finding of a hypothalamus enlargement does not extend to the MB, but is limited to the HTh. The enlargement is more likely related to the dysregulation of the HPA axis than to cognitive dysfunctions accompanying mood disorders.

Simultaneous and Proportional Real-Time Myocontrol of Up to Three Degrees of Freedom of the Wrist and Hand.

Achieving robust, intuitive, simultaneous and proportional control over multiple degrees of freedom (DOFs) is an outstanding challenge in the development of myoelectric prosthetic systems. Since the priority in myoelectric prosthesis solutions is robustness and stability, their number of functions is usually limited. OBJECTIVE: Here, we introduce a system for intuitive concurrent hand and wrist control, based on a robust feature-extraction protocol and machine-learning. METHODS: Using the mean absolute value of high-density EMG, we train a ridge-regressor (RR) on only the sustained portions of the single-DOF contractions and leverage the regressor's inherent ability to provide simultaneous multi-DOF estimates. In this way, we robustly capture the amplitude information of the inputs while harnessing the power of the RR to extrapolate otherwise noisy and often overfitted estimations of dynamic portions of movements. RESULTS: The real-time evaluation of the system on 13 able-bodied participants and an amputee shows that almost all single-DOF tasks could be reached (96% success rate), while at the same time users were able to complete most of the two-DOF (62%) and even some of the very challenging three-DOF tasks (37%). To further investigate the translational potential of the approach, we reduced the original 192-channel setup to a 16-channel configuration and the observed performance did not deteriorate. Notably, the amputee performed similarly well to the other participants, according to all considered metrics. CONCLUSION: This is the first real-time operated myocontrol system that consistently provides intuitive simultaneous and proportional control over 3-DOFs of wrist and hand, relying on only surface EMG signals from the forearm. SIGNIFICANCE: Focusing on reduced complexity, a real-time test and the inclusion of an amputee in the study demonstrate the translational potential of the control system for future applications in prosthetic control.

Action interference in simultaneous and proportional myocontrol: comparing force- and electromyography.

Myocontrol, that is, control of a prosthesis via muscle signals, is still a surprisingly hard problem. Recent research indicates that surface electromyography (sEMG), the traditional technique used to detect a subject's intent, could proficiently be replaced, or conjoined with, other techniques (multi-modal myocontrol), with the aim to improve both on dexterity and reliability. Objective. In this paper we present an online assessment of multi-modal sEMG and force myography (FMG) targeted at hand and wrist myocontrol. Approach. Twenty sEMG and FMG sensors in total were used to enforce simultaneous and proportional control of hand opening/closing, wrist pronation/supination and wrist flexion/extension of 12 intact subjects. Main results and Significance. We found that FMG yields in general a better performance than sEMG, and that the main drawback of the sEMG array we used is not the inability to perform a desired action, but rather action interference, that is, the undesired concurrent activation of another action. FMG, on the other hand, causes less interference.

Automated Instability Detection for Interactive Myocontrol of Prosthetic Hands.

Myocontrol is control of a prosthetic device using data obtained from (residual) muscle activity. In most myocontrol prosthetic systems, such biological data also denote the subject's intent: reliably interpreting what the user wants to do, exactly and only when she wants, is paramount to avoid instability, which can potentially lead to accidents, humiliation and trauma. Indeed, instability manifests itself as a failure of the myocontrol in interpreting the subject's intent, and the automated detection of such failures can be a specific step to improve myocontrol of prostheses-e.g., enabling the possibility of self-adaptation of the system via on-demand model updates for incremental learning, i.e., the interactive myocontrol paradigm. In this work we engaged six expert myocontrol users (five able-bodied subjects and one trans-radial amputee) in a simple, clear grasp-carry-release task, in which the subject's intent was reasonably determined by the task itself. We then manually ascertained when the intent would not coincide with the behavior of the prosthetic device, i.e., we labeled the failures of the myocontrol system. Lastly, we trained and tested a classifier to automatically detect such failures. Our results show that a standard classifier is able to detect myocontrol failures with a mean balanced error rate of 18.86% over all subjects. If confirmed in the large, this approach could pave the way to self-detection and correction of myocontrol errors, a tighter man-machine co-adaptation, and in the end the improvement of the reliability of myocontrol.

Automatic Detection of Myocontrol Failures Based upon Situational Context Information.

Myoelectric control systems for assistive devices are still unreliable. The user's input signals can become unstable over time due to e.g. fatigue, electrode displacement, or sweat. Hence, such controllers need to be constantly updated and heavily rely on user feedback. In this paper, we present an automatic failure detection method which learns when plausible predictions become unreliable and model updates are necessary. Our key insight is to enhance the control system with a set of generative models that learn sensible behaviour for a desired task from human demonstration. We illustrate our approach on a grasping scenario in Virtual Reality, in which the user is asked to grasp a bottle on a table. From demonstration our model learns the reach-to-grasp motion from a resting position to two grasps (power grasp and tridigital grasp) and how to predict the most adequate grasp from local context, e.g. tridigital grasp on the bottle cap or around the bottleneck. By measuring the error between new grasp attempts and the model prediction, the system can effectively detect which input commands do not reflect the user's intention. We evaluated our model in two cases: i) with both position and rotation information of the wrist pose, and ii) with only rotational information. Our results show that our approach detects statistically highly significant differences in error distributions with p<0.001 between successful and failed grasp attempts in both cases.

VITA-an everyday virtual reality setup for prosthetics and upper-limb rehabilitation.

OBJECTIVE: Currently, there are some 95 000 people in Europe suffering from upper-limb impairment. Rehabilitation should be undertaken right after the impairment occurs and should be regularly performed thereafter. Moreover, the rehabilitation process should be tailored specifically to both patient and impairment. APPROACH: To address this, we have developed a low-cost solution that integrates an off-the-shelf virtual reality (VR) setup with our in-house developed arm/hand intent detection system. The resulting system, called VITA, enables an upper-limb disabled person to interact in a virtual world as if her impaired limb were still functional. VITA provides two specific features that we deem essential: proportionality of force control and interactivity between the user and the intent detection core. The usage of relatively cheap commercial components enables VITA to be used in rehabilitation centers, hospitals, or even at home. The applications of VITA range from rehabilitation of patients with musculodegenerative conditions (e.g. ALS), to treating phantom-limb pain of people with limb-loss and prosthetic training. MAIN RESULTS: We present a multifunctional system for upper-limb rehabilitation in VR. We tested the system using a VR implementation of a standard hand assessment tool, the Box and Block test and performed a user study on this standard test with both intact subjects and a prosthetic user. Furthermore, we present additional applications, showing the versatility of the system. SIGNIFICANCE: The VITA system shows the applicability of a combination of our experience in intent detection with state-of-the art VR system for rehabilitation purposes. With VITA, we have an easily adaptable experimental tool available, which allows us to quickly and realistically simulate all kind of real-world problems and rehabilitation exercises for upper-limb impaired patients. Additionally, other scenarios such as prostheses simulations and control modes can be quickly implemented and tested.

Multi-modal myocontrol: Testing combined force- and electromyography.

Myocontrol, that is control of prostheses using bodily signals, has proved in the decades to be a surprisingly hard problem for the scientific community of assistive and rehabilitation robotics. In particular, traditional surface electromyography (sEMG) seems to be no longer enough to guarantee dexterity (i.e., control over several degrees of freedom) and, most importantly, reliability. Multi-modal myocontrol is concerned with the idea of using novel signal gathering techniques as a replacement of, or alongside, sEMG, to provide high-density and diverse signals to improve dexterity and make the control more reliable. In this paper we present an offline and online assessment of multi-modal sEMG and force myography (FMG) targeted at hand and wrist myocontrol. A total number of twenty sEMG and FMG sensors were used simultaneously, in several combined configurations, to predict opening/closing of the hand and activation of two degrees of freedom of the wrist of ten intact subjects. The analysis was targeted at determining the optimal sensor combination and control parameters; the experimental results indicate that sEMG sensors alone perform worst, yielding a nRMSE of 9.1%, while mixing FMG and sEMG or using FMG only reduces the nRMSE to 5.2-6.6%. To validate these results, we engaged the subject with median performance in an online goal-reaching task. Analysis of this further experiment reveals that the online behaviour is similar to the offline one.

Exploiting Knowledge Composition to Improve Real-Life Hand Prosthetic Control.

In myoelectric prosthesis control, one of the hottest topics nowadays is enforcing simultaneous and proportional (s/p) control over several degrees of freedom. This problem is particularly hard and the scientific community has so far failed to provide a stable and reliable s/p control, effective in daily-life activities. In order to improve the reliability of this form of control, in this paper we propose on-the-fly knowledge composition, thereby reducing the burden of matching several patterns at the same time, and simplifying the task of the system. In particular, we show that using our method it is possible to dynamically compose a model by juxtaposing subsets of previously gathered (sample, target) pairs in real-time, rather than composing a single model in the beginning and then hoping it can reliably distinguish all patterns. Fourteen intact subjects participated in an experiment, where repetitive daily-life tasks (e.g. ironing a cloth) were performed using a commercially available dexterous prosthetic hand mounted on a splint and wirelessly controlled using a machine learning method. During the experiment, the subjects performed these tasks using myocontrol with and without knowledge composition and the results demonstrate that employing knowledge composition allowed better performance, i.e. reducing the overall task completion time by 30%.

Online Bimanual Manipulation Using Surface Electromyography and Incremental Learning.

The paradigm of simultaneous and proportional myocontrol of hand prostheses is gaining momentum in the rehabilitation robotics community. As opposed to the traditional surface electromyography classification schema, in simultaneous and proportional control the desired force/torque at each degree of freedom of the hand/wrist is predicted in real-time, giving to the individual a more natural experience, reducing the cognitive effort and improving his dexterity in daily-life activities. In this study we apply such an approach in a realistic manipulation scenario, using 10 non-linear incremental regression machines to predict the desired torques for each motor of two robotic hands. The prediction is enforced using two sets of surface electromyography electrodes and an incremental, non-linear machine learning technique called Incremental Ridge Regression with Random Fourier Features. Nine able-bodied subjects were engaged in a functional test with the aim to evaluate the performance of the system. The robotic hands were mounted on two hand/wrist orthopedic splints worn by healthy subjects and controlled online. An average completion rate of more than 95% was achieved in single-handed tasks and 84% in bimanual tasks. On average, 5 min of retraining were necessary on a total session duration of about 1 h and 40 min. This work sets a beginning in the study of bimanual manipulation with prostheses and will be carried on through experiments in unilateral and bilateral upper limb amputees thus increasing its scientific value.

The LET Procedure for Prosthetic Myocontrol: Towards Multi-DOF Control Using Single-DOF Activations.

Simultaneous and proportional myocontrol of dexterous hand prostheses is to a large extent still an open problem. With the advent of commercially and clinically available multi-fingered hand prostheses there are now more independent degrees of freedom (DOFs) in prostheses than can be effectively controlled using surface electromyography (sEMG), the current standard human-machine interface for hand amputees. In particular, it is uncertain, whether several DOFs can be controlled simultaneously and proportionally by exclusively calibrating the intended activation of single DOFs. The problem is currently solved by training on all required combinations. However, as the number of available DOFs grows, this approach becomes overly long and poses a high cognitive burden on the subject. In this paper we present a novel approach to overcome this problem. Multi-DOF activations are artificially modelled from single-DOF ones using a simple linear combination of sEMG signals, which are then added to the training set. This procedure, which we named LET (Linearly Enhanced Training), provides an augmented data set to any machine-learning-based intent detection system. In two experiments involving intact subjects, one offline and one online, we trained a standard machine learning approach using the full data set containing single- and multi-DOF activations as well as using the LET-augmented data set in order to evaluate the performance of the LET procedure. The results indicate that the machine trained on the latter data set obtains worse results in the offline experiment compared to the full data set. However, the online implementation enables the user to perform multi-DOF tasks with almost the same precision as single-DOF tasks without the need of explicitly training multi-DOF activations. Moreover, the parameters involved in the system are statistically uniform across subjects.

Stable myoelectric control of a hand prosthesis using non-linear incremental learning.

Stable myoelectric control of hand prostheses remains an open problem. The only successful human-machine interface is surface electromyography, typically allowing control of a few degrees of freedom. Machine learning techniques may have the potential to remove these limitations, but their performance is thus far inadequate: myoelectric signals change over time under the influence of various factors, deteriorating control performance. It is therefore necessary, in the standard approach, to regularly retrain a new model from scratch. We hereby propose a non-linear incremental learning method in which occasional updates with a modest amount of novel training data allow continual adaptation to the changes in the signals. In particular, Incremental Ridge Regression and an approximation of the Gaussian Kernel known as Random Fourier Features are combined to predict finger forces from myoelectric signals, both finger-by-finger and grouped in grasping patterns. We show that the approach is effective and practically applicable to this problem by first analyzing its performance while predicting single-finger forces. Surface electromyography and finger forces were collected from 10 intact subjects during four sessions spread over two different days; the results of the analysis show that small incremental updates are indeed effective to maintain a stable level of performance. Subsequently, we employed the same method on-line to teleoperate a humanoid robotic arm equipped with a state-of-the-art commercial prosthetic hand. The subject could reliably grasp, carry and release everyday-life objects, enforcing stable grasping irrespective of the signal changes, hand/arm movements and wrist pronation and supination.

The mental self.

In meditation both the quality and the contents of consciousness may be voluntarily changed, making it an obvious target in the quest for the neural correlate of consciousness. Here we present the results of a positron emission tomography study of yoga nidra relaxation meditation when compared with the normal resting conscious state. Meditation is accompanied by a relatively increased perfusion in the sensory imagery system: hippocampus and sensory and higher order association regions, with decreased perfusion in the executive system: dorsolateral prefrontal cortex, anterior cingulate gyrus, striatum, thalamus, pons, and cerebellum. To identify regions active in both systems we performed a principal component analysis of the results. This separated the blood flow data into two groups of regions, explaining 25 and 18% of their variance: One group corresponded to the executive system, and the other to the systems supporting sensory imagery. A small group of regions contributed considerably to both networks: medial parietal and medial prefrontal cortices, together with the striatum. The inclusion of the striatum and our subsequent finding of increased striatal dopamine binding to D2 receptors during meditation suggested dopaminergic regulation of this circuit. We then investigated the neural networks supporting episodic retrieval of judgments of individuals with different degrees of self-relevance, in the decreasing order: self, best friend, and the Danish queen. We found that all conditions activated a medial prefrontal - precuneus/posterior cingulate cortex, thalamus, and cerebellum. This activation occurred together with the activation of the left lateral prefrontal/temporal cortex. The latter was dependent on the requirement of retrieval of semantic information, being most pronounced in the "queen" condition. Transcranial magnetic stimulation, targeting precuneus, was then applied to the medial parietal region to transiently disrupt the normal function of the circuitry. We found a decreased efficiency of retrieval of self-judgment compared to the judgment of best friend. This shows that the integrity of the function of precuneus is essential for self-reference, but not for reference to others.

"Central command" and insular activation during attempted foot lifting in paraplegic humans.

The relationship between cardiovascular regulation and brain activation was investigated during attempted foot lifting in paraplegic subjects and during rhythmic handgrip exercise at one-third of maximum voluntary contraction force. Brain areas of interest were the primary sensory-motor area and the insula, a hypothesized center for a central nervous feed-forward mechanism involved in cardiovascular control ("central command"). This mechanism is complementary to the usual known feedback pathways such as skeletal muscle afferent signals. Regional cerebral blood flow (rCBF) was measured in eight normal and three paraplegic subjects using positron emission tomography (PET) and oxygen-15-labeled water. Statistical parametric maps were calculated from the images comparing rest and handgrip. Paraplegics were also scanned during attempted foot lifting, a condition without sensory feedback. During activation tasks, heart rate and mean arterial pressure increased. PET activation responses (P < 0.05, corrected for multiple comparisons) were found in the contralateral primary sensory-motor area, the supplementary motor area, ipsilateral cerebellum, and bilaterally in the insula. A conjunction analysis showing responses common to handgrip and attempted foot lifting revealed activation in the right central insula (P < 0.05, corrected) in concordance with the concept of a central command feed-forward hypothesis.

Increased fluorine-18 2-fluoro-2-deoxy-D-glucose (FDG) uptake in childhood CNS tumors is correlated with malignancy grade: a study with FDG positron emission tomography/magnetic resonance imaging coregistration and image fusion.

PURPOSE Positron emission tomography (PET) has been used in grading of CNS tumors in adults, whereas studies of children have been limited. PATIENTS AND METHODS Nineteen boys and 19 girls (median age, 8 years) with primary CNS tumors were studied prospectively by fluorine-18 2-fluoro-2-deoxy-D-glucose (FDG) PET with (n = 16) or without (n = 22) H(2)(15)O-PET before therapy. Image processing included coregistration to magnetic resonance imaging (MRI) in all patients. The FDG uptake in tumors was semiquantitatively calculated by a region-of-interest-based tumor hotspot/brain index. Eight tumors without histologic confirmation were classified as WHO grade 1 based on location, MRI, and clinical course (22 to 42 months). Results Four grade 4 tumors had a mean index of 4.27 +/- 0.5, four grade 3 tumors had a mean index of 2.47 +/- 1.07, 10 grade 2 tumors had a mean index of 1.34 +/- 0.73, and eight of 12 grade 1 tumors had a mean index of -0.31 +/- 0.59. Eight patients with no histologic confirmation had a mean index of 1.04. For these 34 tumors, FDG uptake was positively correlated with malignancy grading (n = 34; r = 0.72; P < .01), as for the 26 histologically classified tumors (n = 26; r = 0.89; P < .01). The choroid plexus papilloma (n = 1) and the pilocytic astrocytomas (n = 3) had a mean index of 3.26 (n = 38; r = 0.57; P < .01). H(2)(15)O-uptake showed no correlation with malignancy. Digitally performed PET/MRI coregistration increased information on tumor characterization in 90% of cases. CONCLUSION FDG PET of the brain with MRI coregistration can be used to obtain a more specific diagnosis with respect to malignancy grading. Improved PET/MRI imaging of the benign hypermetabolic tumors is needed to optimize clinical use.

Parietal cortex and representation of the mental Self.

For a coherent and meaningful life, conscious self-representation is mandatory. Such explicit "autonoetic consciousness" is thought to emerge by retrieval of memory of personally experienced events ("episodic memory"). During episodic retrieval, functional imaging studies consistently show differential activity in medial prefrontal and medial parietal cortices. With positron-emission tomography, we here show that these medial regions are functionally connected and interact with lateral regions that are activated according to the degree of self-reference. During retrieval of previous judgments of Oneself, Best Friend, and the Danish Queen, activation increased in the left lateral temporal cortex and decreased in the right inferior parietal region with decreasing self-reference. Functionally, the former region was preferentially connected to medial prefrontal cortex, the latter to medial parietal. The medial parietal region may, then, be conceived of as a nodal structure in self-representation, functionally connected to both the right parietal and the medial prefrontal cortices. To determine whether medial parietal cortex in this network is essential for episodic memory retrieval with self-representation, we used transcranial magnetic stimulation over the region to transiently disturb neuronal circuitry. There was a decrease in the efficiency of retrieval of previous judgment of mental Self compared with retrieval of judgment of Other with transcranial magnetic stimulation at a latency of 160 ms, confirming the hypothesis. This network is strikingly similar to the network of the resting conscious state, suggesting that self-monitoring is a core function in resting consciousness.

Reflective self-awareness and conscious states: PET evidence for a common midline parietofrontal core.

A recent meta-analysis has shown precuneus, angular gyri, anterior cingulate gyri, and adjacent structures to be highly metabolically active in support of resting consciousness. We hypothesize that these regions constitute a functional network of reflective self-awareness thought to be a core function of consciousness. Seven normal volunteers were asked to think intensely on how they would describe the personality traits and physical appearance of themselves and a neutral reference person known to all the subjects (the Danish Queen). During each of the four conditions cerebral blood flow distribution was measured by the intravenous H(2)(15)O PET scanning technique. During scanning, no sensory or motor activity was intended. After each scan, the subjects reported the contents of their thoughts during the scan to ascertain that the instructions had been followed. The results confirmed our hypothesis: Statistical parametric mapping showed differential activity in precuneus and angular gyri during reflection on own personality traits and in anterior cingulate gyri during reflection on own physical traits. Connectivity analysis of synchrony showed these regions to be functionally connected during reflective self-awareness. The commonality between the neural networks of the resting conscious state and self-awareness reflects the phenomenological concept of a fundamental contribution of reflective self-awareness to the contents and coherence of the conscious state.