DTI measurements for Alzheimer's classification

Diffusion tensor imaging (DTI) is a promising imaging technique that provides insight into white matter microstructure integrity and it has greatly helped identifying white matter regions affected by Alzheimer's disease (AD) in its early stages. DTI can therefore be a valuable source of information when designing machine-learning strategies to discriminate between healthy control (HC) subjects, AD patients and subjects with mild cognitive impairment (MCI). Nonetheless, several studies have reported so far conflicting results, especially because of the adoption of biased feature selection strategies. In this paper we firstly analyzed DTI scans of 150 subjects from the Alzheimer's disease neuroimaging initiative (ADNI) database. We measured a significant effect of the feature selection bias on the classification performance (p-value < 0.01), leading to overoptimistic results (10% up to 30% relative increase in AUC). We observed that this effect is manifest regardless of the choice of diffusion index, specifically fractional anisotropy and mean diffusivity. Secondly, we performed a test on an independent mixed cohort consisting of 119 ADNI scans; thus, we evaluated the informative content provided by DTI measurements for AD classification. Classification performances and biological insight, concerning brain regions related to the disease, provided by cross-validation analysis were both confirmed on the independent test

detecting beam offsets in laser welding of closed square butt joints by wavelet analysis of an optical process signal

Abstract Robotized laser beam welding of closed-square-butt joints is sensitive to the positioning of the laser beam with respect to the joint since even a small offset may result in a detrimental lack of sidewall fusion. An evaluation of a system using a photodiode aligned coaxial to the processing laser beam confirms the ability to detect variations of the process conditions, such as when there is an evolution of an offset between the laser beam and the joint. Welding with different robot trajectories and with the processing laser operating in both continuous and pulsed mode provided data for this evaluation. The detection method uses wavelet analysis of the photodetector signal that carries information of the process condition revealed by the plasma plume optical emissions during welding. This experimental data have been evaluated offline. The results show the potential of this detection method that is clearly beneficial for the development of a system for welding joint tracking

Spatio-temporal dynamics in semiconductor microresonators with thermal effects.

In this paper we study the dynamics of the intracavity field, carriers and lattice temperature in externally driven semiconductor microcavities. The combination/competition of the different time-scales of the dynamical variables together with diffraction and carrier/thermal diffusions are responsible for new dynamical behaviors. We report here the occurrence of a spatio-temporal instability of the Hopf type giving rise to Regenerative Oscillations and travelling patterns and cavity solitons

Multiplex Networks for Early Diagnosis of Alzheimer's Disease

Analysis and quantification of brain structural changes, using Magnetic Resonance Imaging (MRI), are increasingly used to define novel biomarkers of brain pathologies, such as Alzheimer's disease (AD). Several studies have suggested that brain topological organization can reveal early signs of AD. Here, we propose a novel brain model which captures both intra- and inter-subject information within a multiplex network approach. This model localizes brain atrophy effects and summarizes them with a diagnostic score. On an independent test set, our multiplex-based score segregates (i) normal controls (NC) from AD patients with a 0.86±0.01 accuracy and (ii) NC from mild cognitive impairment (MCI) subjects that will convert to AD (cMCI) with an accuracy of 0.84±0.01. The model shows that illness effects are maximally detected by parceling the brain in equal volumes of 3, 000 mm3 (“patches”), without any a priori segmentation based on anatomical features. The multiplex approach shows great sensitivity in detecting anomalous changes in the brain; the robustness of the obtained results is assessed using both voxel-based morphometry and FreeSurfer morphological features. Because of its generality this method can provide a reliable tool for clinical trials and a disease signature of many neurodegenerative pathologies

Cavity Light Bullets: 3D Localized Structures in a Nonlinear Optical Resonator

We consider the paraxial model for a nonlinear resonator with a saturable absorber beyond the mean-field limit and develop a method to study the modulational instabilities leading to pattern formation in all three spatial dimensions. For achievable parametric domains we observe total radiation confinement and the formation of 3D localised bright structures. At difference from freely propagating light bullets, here the self-organization proceeds from the resonator feedback, combined with diffraction and nonlinearity. Such "cavity" light bullets can be independently excited and erased by appropriate pulses, and once created, they endlessly travel the cavity roundtrip. Also, the pulses can shift in the transverse direction, following external field gradients.Comment: 4 pages, 3 figures, simulations files available at http://www.ba.infn.it/~maggipin/PRLmovies.htm, submitted to Physical Review Letters on 24 March 200

Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age

Recent works have extensively investigated the possibility to predict brain aging from T1-weighted MRI brain scans. The main purposes of these studies are the investigation of subject-specific aging mechanisms and the development of accurate models for age prediction. Deviations between predicted and chronological age are known to occur in several neurodegenerative diseases; as a consequence, reaching higher levels of age prediction accuracy is of paramount importance to develop diagnostic tools. In this work, we propose a novel complex network model for brain based on segmenting T1-weighted MRI scans in rectangular boxes, called patches, and measuring pairwise similarities using Pearson's correlation to define a subject-specific network. We fed a deep neural network with nodal metrics, evaluating both the intensity and the uniformity of connections, to predict subjects' ages. Our model reaches high accuracies which compare favorably with state-of-the-art approaches. We observe that the complex relationships involved in this brain description cannot be accurately modeled with standard machine learning approaches, such as Ridge and Lasso regression, Random Forest, and Support Vector Machines, instead a deep neural network has to be used

Disturbances in a VLF radio signal prior the M =4.7 offshore Anzio (central Italy) earthquake on 22 August 2005

Abstract. On 22 August 2005 an earthquake with magnitude M=4.7 occurred in the Anzio (central Italy) offshore area. From 2002, a VLF-LF radio receiver is into operation in Bari (southern Italy). The intensity and the phase of the signals transmitted by GB (f=16 kHz, United Kingdom), FR (f=20.9 kHz, France), GE (f=23.4 kHz, Germany), IC (f=37.5 kHz, Island) and IT (f=54 kHz, Sicily, Italy) has been monitored with a 5 s sampling rate. The previous epicenter is near enough to some of the radio paths and the data collected were studied in order to reveal possible seismic effects. The raw analysis revealed a clear drop in the intensity of the FR radio signal on 19 August. Then the wavelet analysis was applied to the intensity and the phase data of the different radio signals. In the mentioned day an increase in the band 60–120 min was revealed in the spectra of the FR signal. Then the principal component analysis was applied and again the 19 August stood up as an anomalous day for the FR radio signal. The path of this signal, among the paths of the five radio signals collected by the Bari receiver, is the nearest to the mentioned epicentre and the anomaly revealed on 19 August appears as a precursor of the earthquake. This result confirms the possible precursor revealed by other researchers in the air Rn content in a site located 5 miles far from the epicenter

Predicting brain age with complex networks: From adolescence to adulthood.

In recent years, several studies have demonstrated that machine learning and deep learning systems can be very useful to accurately predict brain age. In this work, we propose a novel approach based on complex networks using 1016 T1-weighted MRI brain scans (in the age range 7-64years). We introduce a structural connectivity model of the human brain: MRI scans are divided in rectangular boxes and Pearson's correlation is measured among them in order to obtain a complex network model. Brain connectivity is then characterized through few and easy-to-interpret centrality measures; finally, brain age is predicted by feeding a compact deep neural network. The proposed approach is accurate, robust and computationally efficient, despite the large and heterogeneous dataset used. Age prediction accuracy, in terms of correlation between predicted and actual age r=0.89and Mean Absolute Error MAE =2.19years, compares favorably with results from state-of-the-art approaches. On an independent test set including 262 subjects, whose scans were acquired with different scanners and protocols we found MAE =2.52. The only imaging analysis steps required in the proposed framework are brain extraction and linear registration, hence robust results are obtained with a low computational cost. In addition, the network model provides a novel insight on aging patterns within the brain and specific information about anatomical districts displaying relevant changes with aging

Three-dimensional self-organized patterns in the field profile of a ring cavity resonator

We consider the paraxial model for a nonlinear resonator filled with a saturable absorber, beyond the mean field limit. We develop a general treatment to study the modulational instabilities which give rise to pattern formation in the propagation direction z and in the transverse plane (x, y). For appropriate parametric domains we observe the first example of system self-organization in space and time proceeding from competition of linear effects with nonlinear medium properties and resonator feedback. In particular, we could observe either 3D global structures or structures which are localized in the transverse plane, showing different lengths in the longitudinal direction and endlessly travelling in the resonator