HardIDX: Practical and Secure Index with SGX

Software-based approaches for search over encrypted data are still either challenged by lack of proper, low-leakage encryption or slow performance. Existing hardware-based approaches do not scale well due to hardware limitations and software designs that are not specifically tailored to the hardware architecture, and are rarely well analyzed for their security (e.g., the impact of side channels). Additionally, existing hardware-based solutions often have a large code footprint in the trusted environment susceptible to software compromises. In this paper we present HardIDX: a hardware-based approach, leveraging Intel's SGX, for search over encrypted data. It implements only the security critical core, i.e., the search functionality, in the trusted environment and resorts to untrusted software for the remainder. HardIDX is deployable as a highly performant encrypted database index: it is logarithmic in the size of the index and searches are performed within a few milliseconds rather than seconds. We formally model and prove the security of our scheme showing that its leakage is equivalent to the best known searchable encryption schemes. Our implementation has a very small code and memory footprint yet still scales to virtually unlimited search index sizes, i.e., size is limited only by the general - non-secure - hardware resources

Alzheimer Disease and Oxidative Stress

Research in Alzheimer disease has recently demonstrated compelling evidence on the importance of oxidative processes in its pathogenesis. Cellular changes show that oxidative stress is an event that precedes the appearance of the hallmark pathologies of the disease, neurofibrillary tangles, and senile plaques. While it is still unclear what the initial source of the oxidative stress is in Alzheimer disease, it is likely that the process is highly dependent on redox-active transition metals such as iron and copper. Further investigation into the role that oxidative stress mechanisms seem to play in the pathogenesis of Alzheimer disease may lead to novel clinical interventions

Avalanche analysis from multi-electrode ensemble recordings in cat, monkey and human cerebral cortex during wakefulness and sleep

Self-organized critical states are found in many natural systems, from earthquakes to forest fires, they have also been observed in neural systems, particularly, in neuronal cultures. However, the presence of critical states in the awake brain remains controversial. Here, we compared avalanche analyses performed on different in vivo preparations during wakefulness, slow-wave sleep and REM sleep, using high-density electrode arrays in cat motor cortex (96 electrodes), monkey motor cortex and premotor cortex and human temporal cortex (96 electrodes) in epileptic patients. In neuronal avalanches defined from units (up to 160 single units), the size of avalanches never clearly scaled as power-law, but rather scaled exponentially or displayed intermediate scaling. We also analyzed the dynamics of local field potentials (LFPs) and in particular LFP negative peaks (nLFPs) among the different electrodes (up to 96 sites in temporal cortex or up to 128 sites in adjacent motor and pre-motor cortices). In this case, the avalanches defined from nLFPs displayed power-law scaling in double log representations, as reported previously in monkey. However, avalanche defined as positive LFP (pLFP) peaks, which are less directly related to neuronal firing, also displayed apparent power-law scaling. Closer examination of this scaling using more reliable cumulative distribution functions (CDF) and other rigorous statistical measures, did not confirm power-law scaling. The same pattern was seen for cats, monkey and human, as well as for different brain states of wakefulness and sleep. We also tested other alternative distributions. Multiple exponential fitting yielded optimal fits of the avalanche dynamics with bi-exponential distributions. Collectively, these results show no clear evidence for power-law scaling or self-organized critical states in the awake and sleeping brain of mammals, from cat to man.Comment: In press in: Frontiers in Physiology, 2012, special issue "Critical Brain Dynamics" (Edited by He BY, Daffertshofer A, Boonstra TW); 33 pages, 13 figures. 3 table

A New Family of Multistep Methods with Improved Phase Lag Characteristics for the Integration of Orbital Problems

In this work we introduce a new family of ten-step linear multistep methods for the integration of orbital problems. The new methods are constructed by adopting a new methodology which improves the phase lag characteristics by vanishing both the phase lag function and its first derivatives at a specific frequency. The efficiency of the new family of methods is proved via error analysis and numerical applications.Comment: 21 pages, 3 figures, 1 tabl

Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion

Clinical annotations, such as voxel-wise binary or probabilistic tissue segmentations, structural parcellations, pathological regionsof- interest and anatomical landmarks are key to many clinical studies. However, due to the time consuming nature of manually generating these annotations, they tend to be scarce and limited to small subsets of data. This work explores a novel framework to propagate voxel-wise annotations between morphologically dissimilar images by diffusing and mapping the available examples through intermediate steps. A spatially-variant graph structure connecting morphologically similar subjects is introduced over a database of images, enabling the gradual diffusion of information to all the subjects, even in the presence of large-scale morphological variability. We illustrate the utility of the proposed framework on two example applications: brain parcellation using categorical labels and tissue segmentation using probabilistic features. The application of the proposed method to categorical label fusion showed highly statistically significant improvements when compared to state-of-the-art methodologies. Significant improvements were also observed when applying the proposed framework to probabilistic tissue segmentation of both synthetic and real data, mainly in the presence of large morphological variability

Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment

Accurately identifying the patients that have mild cognitive impairment (MCI) who will go on to develop Alzheimer's disease (AD) will become essential as new treatments will require identification of AD patients at earlier stages in the disease process. Most previous work in this area has centred around the same automated techniques used to diagnose AD patients from healthy controls, by coupling high dimensional brain image data or other relevant biomarker data to modern machine learning techniques. Such studies can now distinguish between AD patients and controls as accurately as an experienced clinician. Models trained on patients with AD and control subjects can also distinguish between MCI patients that will convert to AD within a given timeframe (MCI-c) and those that remain stable (MCI-s), although differences between these groups are smaller and thus, the corresponding accuracy is lower. The most common type of classifier used in these studies is the support vector machine, which gives categorical class decisions. In this paper, we introduce Gaussian process (GP) classification to the problem. This fully Bayesian method produces naturally probabilistic predictions, which we show correlate well with the actual chances of converting to AD within 3 years in a population of 96 MCI-s and 47 MCI-c subjects. Furthermore, we show that GPs can integrate multimodal data (in this study volumetric MRI, FDG-PET, cerebrospinal fluid, and APOE genotype with the classification process through the use of a mixed kernel). The GP approach aids combination of different data sources by learning parameters automatically from training data via type-II maximum likelihood, which we compare to a more conventional method based on cross validation and an SVM classifier. When the resulting probabilities from the GP are dichotomised to produce a binary classification, the results for predicting MCI conversion based on the combination of all three types of data show a balanced accuracy of 74%. This is a substantially higher accuracy than could be obtained using any individual modality or using a multikernel SVM, and is competitive with the highest accuracy yet achieved for predicting conversion within three years on the widely used ADNI dataset

Altered visual and haptic verticality perception in posterior cortical atrophy and Alzheimer's disease

Abstract: There is increasing theoretical and empirical support for the brain combining multisensory information to determine the direction of gravity and hence uprightness. A fundamental part of the process is the spatial transformation of sensory signals between reference frames: eye-centred, head-centred, body-centred, etc. The question ‘Am I the right way up?’ posed by a patient with posterior cortical atrophy (PCA) suggests disturbances in upright perception, subsequently investigated in PCA and typical Alzheimer's disease (tAD) based on what looks or feels upright. Participants repeatedly aligned to vertical a rod presented either visually (visual-vertical) or haptically (haptic-vertical). Visual-vertical involved orienting a projected rod presented without or with a visual orientation cue (circle, tilted square (±18°)). Haptic-vertical involved orientating a grasped rod with eyes closed using a combination of side (left, right) and hand (unimanual, bimanual) configurations. Intraindividual uncertainty and bias defined verticality perception. Uncertainty was consistently greater in both patient groups than in control groups, and greater in PCA than tAD. Bias in the frontal plane was strongly directionally affected by visual cue tilt (visual-vertical) and grip side (haptic-vertical). A model was developed that assumed verticality information from multiple sources is combined in a statistically optimal way to produce observed uncertainties and biases. Model results suggest the mechanism that spatially transforms graviceptive information between body parts is disturbed in both patient groups. Despite visual dysfunction being typically considered the primary feature of PCA, disturbances were greater in PCA than tAD particularly for haptic-vertical, and are considered in light of posterior parietal vulnerability. (Figure presented.). Key points: The perception of upright requires accurate and precise estimates of orientation based on multiple noisy sensory signals. The question ‘Am I the right way up?’ posed by a patient with posterior cortical atrophy (PCA; purported ‘visual variant Alzheimer's’) suggests disturbances in the perception of upright. What looks or feels upright in PCA and typical Alzheimer's disease (tAD) was investigated by asking participants to repeatedly align to vertical a rod presented visually (visual-vertical) or haptically (haptic-vertical). PCA and tAD groups exhibited not only greater perceptual uncertainty than controls, but also exaggerated bias induced by tilted visual orientation cues (visual-vertical) and grip side (haptic-vertical). When modelled, these abnormalities, which were particularly evident in PCA haptic-vertical performance, were compatible with disruption of a mechanism that spatially transforms verticality information between body parts. The findings suggest an important role of posterior parietal cortex in verticality perception, and have implications for understanding spatial disorientation in dementia. © 2021 The Authors. The Journal of Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society

Altered visual and haptic verticality perception in posterior cortical atrophy and Alzheimer's disease

There is increasing theoretical and empirical support for the brain combining multisensory information to determine the direction of gravity and hence uprightness. A fundamental part of the process is the spatial transformation of sensory signals between reference frames: eye-centred, head-centred, body-centred, etc. The question ‘Am I the right way up?’ posed by a patient with posterior cortical atrophy (PCA) suggests disturbances in upright perception, subsequently investigated in PCA and typical Alzheimer's disease (tAD) based on what looks or feels upright. Participants repeatedly aligned to vertical a rod presented either visually (visual-vertical) or haptically (haptic-vertical). Visual-vertical involved orienting a projected rod presented without or with a visual orientation cue (circle, tilted square (±18°)). Haptic-vertical involved orientating a grasped rod with eyes closed using a combination of side (left, right) and hand (unimanual, bimanual) configurations. Intraindividual uncertainty and bias defined verticality perception. Uncertainty was consistently greater in both patient groups than in control groups, and greater in PCA than tAD. Bias in the frontal plane was strongly directionally affected by visual cue tilt (visual-vertical) and grip side (haptic-vertical). A model was developed that assumed verticality information from multiple sources is combined in a statistically optimal way to produce observed uncertainties and biases. Model results suggest the mechanism that spatially transforms graviceptive information between body parts is disturbed in both patient groups. Despite visual dysfunction being typically considered the primary feature of PCA, disturbances were greater in PCA than tAD particularly for haptic-vertical, and are considered in light of posterior parietal vulnerability