Sleep spindles are resilient to extensive white matter deterioration

Sleep spindles are an essential part of non-rapid eye movement sleep, notably involved in sleep consolidation, cognition, learning and memory. These oscillatory waves depend on an interaction loop between the thalamus and the cortex, which relies on a structural backbone of thalamo-cortical white matter tracts. It is still largely unknown if the brain can properly produce sleep spindles when it underwent extensive white matter deterioration in these tracts, and we hypothesized that it would affect sleep spindle generation and morphology. We tested this hypothesis with chronic moderate to severe traumatic brain injury (n ¼ 23; 30.5 6 11.1 years old; 17 m/6f), a unique human model of extensive white matter deterioration, and a healthy control group (n ¼ 27; 30.3 6 13.4 years old; 21m/6f). Sleep spindles were analysed on a full night of polysomnography over the frontal, central and parietal brain regions, and we measured their density, morphology and sigma-band power. White matter deterioration was quantified using diffusion-weighted MRI, with which we performed both whole-brain voxel-wise analysis (Tract-Based Spatial Statistics) and probabilistic tractography (with High Angular Resolution Diffusion Imaging) to target the thalamo-cortical tracts. Group differences were assessed for all variables and correlations were performed separately in each group, corrected for age and multiple comparisons. Surprisingly, although extensive white matter damage across the brain including all thalamo-cortical tracts was evident in the brain-injured group, sleep spindles remained completely undisrupted when compared to a healthy control group. In addition, almost all sleep spindle characteristics were not associated with the degree of white matter deterioration in the braininjured group, except that more white matter deterioration correlated with lower spindle frequency over the frontal regions. This study highlights the resilience of sleep spindles to the deterioration of all white matter tracts critical to their existence, as they conserve normal density during non-rapid eye movement sleep with mostly unaltered morphology. We show that even with such a severe traumatic event, the brain has the ability to adapt or to withstand alterations in order to conserve normal sleep spindles

Audio watermarking using transformation techniques

Watermarking is a technique, which is used in protecting digital information like images, videos and audio as it provides copyrights and ownership. Audio watermarking is more challenging than image watermarking due to the dynamic supremacy of hearing capacity over the visual field. This thesis attempts to solve the quantization based audio watermarking technique based on both the Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). The underlying system involves the statistical characteristics of the signal. This study considers different wavelet filters and quantization techniques. A comparison is performed on diverge algorithms and audio signals to help examine the performance of the proposed method. The embedded watermark is a binary image and different encryption techniques such as Arnold Transform and Linear Feedback Shift Register (LFSR) are considered. The watermark is distributed uniformly in the areas of low frequencies i.e., high energy, which increases the robustness of the watermark. Further, spreading of watermark throughout the audio signal makes the technique robust against desynchronized attacks. Experimental results show that the signals generated by the proposed algorithm are inaudible and robust against signal processing techniques such as quantization, compression and resampling. We use Matlab (version 2009b) to implement the algorithms discussed in this thesis. Audio transformation techniques for compression in Linux (Ubuntu 9.10) are applied on the signal to simulate the attacks such as re-sampling, re-quantization, and mp3 compression; whereas, Matlab program for de-synchronized attacks like jittering and cropping. We envision that the proposed algorithm may work as a tool for securing intellectual properties of the musicians and audio distribution companies because of its high robustness and imperceptibility

Exploiting Innocuous Activity for Correlating Users Across Sites

International audienceWe study how potential attackers can identify accounts on different social network sites that all belong to the same user, exploiting only innocuous activity that inherently comes with posted content. We examine three specific features on Yelp, Flickr, and Twitter: the geo-location attached to a user's posts, the timestamp of posts, and the user's writing style as captured by language models. We show that among these three features the location of posts is the most powerful feature to identify accounts that belong to the same user in different sites. When we combine all three features, the accuracy of identifying Twitter accounts that belong to a set of Flickr users is comparable to that of existing attacks that exploit usernames. Our attack can identify 37% more accounts than using usernames when we instead correlate Yelp and Twitter. Our results have significant privacy implications as they present a novel class of attacks that exploit users' tendency to assume that, if they maintain different personas with different names, the accounts cannot be linked together; whereas we show that the posts themselves can provide enough information to correlate the accounts

Resting State Network Dynamics Across Wakefulness and Sleep

The function of sleep is a longstanding mystery of the brain. By contrast, the function of resting state networks (RSNs) is one of its most recent mysteries. The relationship between RSNs and neuronal activity has been unclear since RSNs were discovered during the advent of functional magnetic resonance imaging (fMRI). Somewhat paradoxically, investigating these enigmatic phenomena in parallel can help to illuminate the function of both. The three studies described as part of this thesis all involve an evaluation of RSN dynamics across wakefulness and sleep. They are all based on the same dataset, derived from an experimental paradigm in which healthy, non sleep-deprived participants (N=36, 21 female) slept in an MRI scanner, as their brain activity was recorded using simultaneous electroencephalography (EEG)-fMRI. An independent component analysis (ICA) was performed in the first study. Spatial boundaries of components in each sleep stage were compared with those of wakefulness, in the first attempt to catalogue RSNs across all healthy alternate functional modes of the brain. Against expectations, all non-wake-RSN components were positively identified as noise. This indicated that sleep is supported by much the same RSN architecture as wakefulness, despite the unique operations performed during sleep. In the second study, between-RSN functional connectivity (FC) dynamics were evaluated across wakefulness and sleep, in order to determine whether they reflect known cortical neurophysiological dynamics. This was confirmed, highlighting the connection between RSNs and neuronal activity. Moreover, the dynamic pattern suggested that one of the functions of sleep may be to homeostatically counterbalance wakefulness RSN FC. A further pattern, indicating increased FC of “higher-order” RSNs (e.g., default mode network), suggested that slow wave sleep might manifest an altered, rather than a reduced state of awareness, in contrast to historical depictions. Finally, the third study correlated frequency-banded oscillatory activity, as measured by EEG, with RSN activity, as measured with fMRI. This was done in order to track changes in representations of frequency-banded neuronal activity in each RSN across stages. It was discovered that the pattern of frequency band representation dynamics reflects the aforementioned cortical neurophysiological dynamics, further strengthening the connection between RSNs and neuronal activity

Identification and neuromodulation of brain states to promote recovery of consciousness

Experimental and clinical studies of consciousness identify brain states (i.e., transient, relevant features of the brain associated with the state of consciousness) in a non-systematic manner and largely independent from the research into the induction of state changes. In this narrative review with a focus on patients with a disorder of consciousness (DoC), we synthesize advances on the identification of brain states associated with consciousness in animal models and physiological (sleep), pharmacological (anesthesia) and pathological (DoC) states of altered consciousness in human. We show that in reduced consciousness the frequencies in which the brain operates are slowed down and that the pattern of functional communication in the brain is sparser, less efficient, and less complex. The results also highlight damaged resting state networks, in particular the default mode network, decreased connectivity in long-range connections and in the thalamocortical loops. Next, we show that therapeutic approaches to treat DoC, through pharmacology (e.g., amantadine, zolpidem), and (non-)invasive brain stimulation (e.g., transcranial current stimulation, deep brain stimulation) have shown some effectiveness to promote consciousness recovery. It seems that these deteriorated features of conscious brain states may improve in response to these neuromodulation approaches, yet, targeting often remains non-specific and does not always lead to (behavioral) improvements. Furthermore, in silico model-based approaches allow the development of personalized assessment of the effect of treatment on brain-wide dynamics. Although still in infancy, the fields of brain state identification and neuromodulation of brain states in relation to consciousness are showing fascinating developments that, when united, might propel the development of new and better targeted techniques for DoC. For example, brain states could be identified in a predictive setting, and the theoretical and empirical testing (i.e., in animals, under anesthesia and patients with a DoC) of neuromodulation techniques to promote consciousness could be investigated. This review further helps to identify where challenges and opportunities lay for the maturation of brain state research in the context of states of consciousness. Finally, it aids in recognizing possibilities and obstacles for the clinical translation of these diagnostic techniques and neuromodulation treatment options across both the multi-modal and multi-species approaches outlined throughout the review. This paper presents interactive figures, supported by the Live Paper initiative of the Human Brain Project, enabling the interaction with data and figures illustrating the concepts in the paper through EBRAINS (go to https://wiki.ebrains.eu/bin/view/Collabs/live-paper-states-altered-consciousness and get started with an EBRAINS account).NA is research fellow, OG is Research Associate, and SL is research director at FRS-FNRS. JA is postdoctoral fellow at the FWO. The study was further supported by the University and University Hospital of Liège, the BIAL Foundation, the Belgian National Funds for Scientific Research (FRS-FNRS), the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3), the FNRS PDR project (T.0134.21), the ERA-Net FLAG-ERA JTC2021 project ModelDXConsciousness (Human Brain Project Partnering Project), the fund Generet, the King Baudouin Foundation, the Télévie Foundation, the European Space Agency (ESA) and the Belgian Federal Science Policy Office (BELSPO) in the framework of the PRODEX Programme, the Public Utility Foundation 'Université Européenne du Travail', "Fondazione Europea di Ricerca Biomedica", the BIAL Foundation, the Mind Science Foundation, the European Commission, the Fondation Leon Fredericq, the Mind-Care foundation, the DOCMA project (EU-H2020-MSCA–RISE–778234), the National Natural Science Foundation of China (Joint Research Project 81471100) and the European Foundation of Biomedical Research FERB Onlus

The (un)conscious mouse as a model for human brain functions: key principles of anesthesia and their impact on translational neuroimaging

In recent years, technical and procedural advances have brought functional magnetic resonance imaging (fMRI) to the field of murine neuroscience. Due to its unique capacity to measure functional activity non-invasively, across the entire brain, fMRI allows for the direct comparison of large-scale murine and human brain functions. This opens an avenue for bidirectional translational strategies to address fundamental questions ranging from neurological disorders to the nature of consciousness. The key challenges of murine fMRI are: (1) to generate and maintain functional brain states that approximate those of calm and relaxed human volunteers, while (2) preserving neurovascular coupling and physiological baseline conditions. Low-dose anesthetic protocols are commonly applied in murine functional brain studies to prevent stress and facilitate a calm and relaxed condition among animals. Yet, current mono-anesthesia has been shown to impair neural transmission and hemodynamic integrity. By linking the current state of murine electrophysiology, Ca(2+) imaging and fMRI of anesthetic effects to findings from human studies, this systematic review proposes general principles to design, apply and monitor anesthetic protocols in a more sophisticated way. The further development of balanced multimodal anesthesia, combining two or more drugs with complementary modes of action helps to shape and maintain specific brain states and relevant aspects of murine physiology. Functional connectivity and its dynamic repertoire as assessed by fMRI can be used to make inferences about cortical states and provide additional information about whole-brain functional dynamics. Based on this, a simple and comprehensive functional neurosignature pattern can be determined for use in defining brain states and anesthetic depth in rest and in response to stimuli. Such a signature can be evaluated and shared between labs to indicate the brain state of a mouse during experiments, an important step toward translating findings across species

On the Use of Speech and Face Information for Identity Verification

{T}his report first provides a review of important concepts in the field of information fusion, followed by a review of important milestones in audio-visual person identification and verification. {S}everal recent adaptive and non-adaptive techniques for reaching the verification decision (i.e., to accept or reject the claimant), based on speech and face information, are then evaluated in clean and noisy audio conditions on a common database; it is shown that in clean conditions most of the non-adaptive approaches provide similar performance and in noisy conditions most exhibit a severe deterioration in performance; it is also shown that current adaptive approaches are either inadequate or utilize restrictive assumptions. A new category of classifiers is then introduced, where the decision boundary is fixed but constructed to take into account how the distributions of opinions are likely to change due to noisy conditions; compared to a previously proposed adaptive approach, the proposed classifiers do not make a direct assumption about the type of noise that causes the mismatch between training and testing conditions. {T}his report is an extended and revised version of {IDIAP-RR} 02-33

Identity Verification Using Speech and Face Information

This article first provides an review of important concepts in the field of information fusion, followed by a review of important milestones in audio–visual person identification and verification. Several recent adaptive and nonadaptive techniques for reaching the verification decision (i.e., to accept or reject the claimant), based on speech and face information, are then evaluated in clean and noisy audio conditions on a common database; it is shown that in clean conditions most of the nonadaptive approaches provide similar performance and in noisy conditions most exhibit a severe deterioration in performance; it is also shown that current adaptive approaches are either inadequate or utilize restrictive assumptions. A new category of classifiers is then introduced, where the decision boundary is fixed but constructed to take into account how the distributions of opinions are likely to change due to noisy conditions; compared to a previously proposed adaptive approach, the proposed classifiers do not make a direct assumption about the type of noise that causes the mismatch between training and testing conditions