Transient Topographical Dynamics of the Electroencephalogram Predict Brain Connectivity and Behavioural Responsiveness During Drowsiness.

As we fall sleep, our brain traverses a series of gradual changes at physiological, behavioural and cognitive levels, which are not yet fully understood. The loss of responsiveness is a critical event in the transition from wakefulness to sleep. Here we seek to understand the electrophysiological signatures that reflect the loss of capacity to respond to external stimuli during drowsiness using two complementary methods: spectral connectivity and EEG microstates. Furthermore, we integrate these two methods for the first time by investigating the connectivity patterns captured during individual microstate lifetimes. While participants performed an auditory semantic classification task, we allowed them to become drowsy and unresponsive. As they stopped responding to the stimuli, we report the breakdown of alpha networks and the emergence of theta connectivity. Further, we show that the temporal dynamics of all canonical EEG microstates slow down during unresponsiveness. We identify a specific microstate (D) whose occurrence and duration are prominently increased during this period. Employing machine learning, we show that the temporal properties of microstate D, particularly its prolonged duration, predicts the response likelihood to individual stimuli. Finally, we find a novel relationship between microstates and brain networks as we show that microstate D uniquely indexes significantly stronger theta connectivity during unresponsiveness. Our findings demonstrate that the transition to unconsciousness is not linear, but rather consists of an interplay between transient brain networks reflecting different degrees of sleep depth

Formal Verification of the AAMP-FV Microcode

This report describes the experiences of Collins Avionics & Communications and SRI International in formally specifying and verifying the microcode in a Rockwell proprietary microprocessor, the AAMP-FV, using the PVS verification system. This project built extensively on earlier experiences using PVS to verify the microcode in the AAMP5, a complex, pipelined microprocessor designed for use in avionics displays and global positioning systems. While the AAMP5 experiment demonstrated the technical feasibility of formal verification of microcode, the steep learning curve encountered left unanswered the question of whether it could be performed at reasonable cost. The AAMP-FV project was conducted to determine whether the experience gained on the AAMP5 project could be used to make formal verification of microcode cost effective for safety-critical and high volume devices

Concurrent Program Verification with Invariant-Guided Underapproximation

Automatic verification of concurrent programs written in low-level languages like ANSI-C is an important task as multi-core architectures are gaining widespread adoption. Formal verification, although very valuable for this domain, rapidly runs into the state-explosion problem due to multiple thread interleavings. Recently, Bounded Model Checking (BMC) has been used for this purpose, which does not scale in practice. In this work, we develop a method to further constrain the search space for BMC techniques using underapproximations of data flow of shared memory and lazy demand-driven refinement of the approximation. A novel contribution of our method is that our underapproximation is guided by likely data-flow invariants mined from dynamic analysis and our refinement is based on proof-based learning. We have implemented our method in a prototype tool. Initial experiments on benchmark examples show potential performance benefit

Smart Metro - Rail System

Railway transport system has proved to be a very sturdy and convenient mode of transport over the centuries. It still serves as the economic and most efficient means of mass transport in many countries. It is a widespread practice even today that most operations are manually carried out, leading to several dangerous accidents and mismanagement of the system. When it comes to the matter of scores of lives, error margin is of utmost importance to ensure an efficient and safe mode of travel. There is an utter need for a system that provides automation of the critical systems that play a pivotal role in the smooth functioning. The focus remains on certain key functions including locomotion, data logging for position, speed and health of he locomotive. This can increase the safety levels, while also reducing the time for relief in-case of unfortunate emergencies

Expectation and Attention in Hierarchical Auditory Prediction

Hierarchical predictive coding suggests that attention in humans emerges from increased precision in probabilistic inference, whereas expectation biases attention in favor of contextually anticipated stimuli. We test these notions within auditory perception by independently manipulating top-down expectation and attentional precision alongside bottom-up stimulus predictability. Our findings support an integrative interpretation of commonly observed electrophysiological signatures of neurodynamics, namely mismatch negativity (MMN), P300, and contingent negative variation (CNV), as manifestations along successive levels of predictive complexity. Early first-level processing indexed by the MMN was sensitive to stimulus predictability: here, attentional precision enhanced early responses, but explicit top-down expectation diminished it. This pattern was in contrast to later, second-level processing indexed by the P300: although sensitive to the degree of predictability, responses at this level were contingent on attentional engagement and in fact sharpened by top-down expectation. At the highest level, the drift of the CNV was a fine-grained marker of top-down expectation itself. Source reconstruction of high-density EEG, supported by intracranial recordings, implicated temporal and frontal regions differentially active at early and late levels. The cortical generators of the CNV suggested that it might be involved in facilitating the consolidation of context-salient stimuli into conscious perception. These results provide convergent empirical support to promising recent accounts of attention and expectation in predictive coding

Relationship between aetiology and covert cognition in the minimally-conscious state

Objectives: Functional neuroimaging has shown that the absence of externally observable signs of consciousness and cognition in severely brain-injured patients does not necessarily indicate the true absence of such abilities. However, relative to traumatic brain injury, nontraumatic injury is known to be associated with a reduced likelihood of regaining overtly measurable levels of consciousness. We investigated the relationships between etiology and both overt and covert cognitive abilities in a group of patients in the minimally conscious state (MCS). Methods: Twenty-three MCS patients (15 traumatic and 8 nontraumatic) completed a motor imagery EEG task in which they were required to imagine movements of their right-hand and toes to command. When successfully performed, these imagined movements appear as distinct sensorimotor modulations, which can be used to determine the presence of reliable command-following. The utility of this task has been demonstrated previously in a group of vegetative state patients. Results: Consistent and robust responses to command were observed in the EEG of 22% of the MCS patients (5 of 23). Etiology had a significant impact on the ability to successfully complete this task, with 33% of traumatic patients (5 of 15) returning positive EEG outcomes compared with none of the nontraumatic patients (0 of 8). Conclusions: The overt behavioral signs of awareness (measured with the Coma Recovery Scale–Revised) exhibited by nontraumatic MCS patients appear to be an accurate reflection of their covert cognitive abilities. In contrast, one-third of a group of traumatically injured patients in the MCS possess a range of high-level cognitive faculties that are not evident from their overt behavior

A hierarchy of event-related potential markers of auditory processing in disorders of consciousness.

Functional neuroimaging of covert perceptual and cognitive processes can inform the diagnoses and prognoses of patients with disorders of consciousness, such as the vegetative and minimally conscious states (VS;MCS). Here we report an event-related potential (ERP) paradigm for detecting a hierarchy of auditory processes in a group of healthy individuals and patients with disorders of consciousness. Simple cortical responses to sounds were observed in all 16 patients; 7/16 (44%) patients exhibited markers of the differential processing of speech and noise; and 1 patient produced evidence of the semantic processing of speech (i.e. the N400 effect). In several patients, the level of auditory processing that was evident from ERPs was higher than the abilities that were evident from behavioural assessment, indicating a greater sensitivity of ERPs in some cases. However, there were no differences in auditory processing between VS and MCS patient groups, indicating a lack of diagnostic specificity for this paradigm. Reliably detecting semantic processing by means of the N400 effect in passively listening single-subjects is a challenge. Multiple assessment methods are needed in order to fully characterise the abilities of patients with disorders of consciousness

Spectral signatures of reorganised brain networks in disorders of consciousness.

Theoretical advances in the science of consciousness have proposed that it is concomitant with balanced cortical integration and differentiation, enabled by efficient networks of information transfer across multiple scales. Here, we apply graph theory to compare key signatures of such networks in high-density electroencephalographic data from 32 patients with chronic disorders of consciousness, against normative data from healthy controls. Based on connectivity within canonical frequency bands, we found that patient networks had reduced local and global efficiency, and fewer hubs in the alpha band. We devised a novel topographical metric, termed modular span, which showed that the alpha network modules in patients were also spatially circumscribed, lacking the structured long-distance interactions commonly observed in the healthy controls. Importantly however, these differences between graph-theoretic metrics were partially reversed in delta and theta band networks, which were also significantly more similar to each other in patients than controls. Going further, we found that metrics of alpha network efficiency also correlated with the degree of behavioural awareness. Intriguingly, some patients in behaviourally unresponsive vegetative states who demonstrated evidence of covert awareness with functional neuroimaging stood out from this trend: they had alpha networks that were remarkably well preserved and similar to those observed in the controls. Taken together, our findings inform current understanding of disorders of consciousness by highlighting the distinctive brain networks that characterise them. In the significant minority of vegetative patients who follow commands in neuroimaging tests, they point to putative network mechanisms that could support cognitive function and consciousness despite profound behavioural impairment.This work was supported by grants from the Wellcome Trust [WT093811MA to T.B.]; the James S. McDonnell Foundation [to A.M.O. and J.D.P.]; the UK Medical Research Council [U.1055.01.002.00001.01 to A.M.O. and J.D.P.]; the Canada Excellence Research Chairs program [to A.M.O.]; the National Institute for Health Research Cambridge Biomedical Research Centre [to J.D.P.]; and the National Institute for Health Research Senior Investigator and Healthcare Technology Cooperative awards [to J.D.P.].This is the final version of the article. It first appeared from PLOS via http://dx.doi.org

Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions.

We developed a systematic approach to map human genetic networks by combinatorial CRISPR-Cas9 perturbations coupled to robust analysis of growth kinetics. We targeted all pairs of 73 cancer genes with dual guide RNAs in three cell lines, comprising 141,912 tests of interaction. Numerous therapeutically relevant interactions were identified, and these patterns replicated with combinatorial drugs at 75% precision. From these results, we anticipate that cellular context will be critical to synthetic-lethal therapies