Disambiguating the role of blood flow and global signal with partial information decomposition

Global signal (GS) is an ubiquitous construct in resting state functional magnetic resonance imaging (rs-fMRI), associated to nuisance, but containing by definition most of the neuronal signal. Global signal regression (GSR) effectively removes the impact of physiological noise and other artifacts, but at the same time it alters correlational patterns in unpredicted ways. Performing GSR taking into account the underlying physiology (mainly the blood arrival time) has been proven to be beneficial. From these observations we aimed to: 1) characterize the effect of GSR on network-level functional connectivity in a large dataset; 2) assess the complementary role of global signal and vessels; and 3) use the framework of partial information decomposition to further look into the joint dynamics of the global signal and vessels, and their respective influence on the dynamics of cortical areas. We observe that GSR affects intrinsic connectivity networks in the connectome in a non-uniform way. Furthermore, by estimating the predictive information of blood flow and the global signal using partial information decomposition, we observe that both signals are present in different amounts across intrinsic connectivity networks. Simulations showed that differences in blood arrival time can largely explain this phenomenon, while using hemodynamic and calcium mouse recordings we were able to confirm the presence of vascular effects, as calcium recordings lack hemodynamic information. With these results we confirm network-specific effects of GSR and the importance of taking blood flow into account for improving de-noising methods. Additionally, and beyond the mere issue of data denoising, we quantify the diverse and complementary effect of global and vessel BOLD signals on the dynamics of cortical areas

Measurements, Models, Systems and Design

531 s.

Graph analysis of functional brain networks: practical issues in translational neuroscience

The brain can be regarded as a network: a connected system where nodes, or units, represent different specialized regions and links, or connections, represent communication pathways. From a functional perspective communication is coded by temporal dependence between the activities of different brain areas. In the last decade, the abstract representation of the brain as a graph has allowed to visualize functional brain networks and describe their non-trivial topological properties in a compact and objective way. Nowadays, the use of graph analysis in translational neuroscience has become essential to quantify brain dysfunctions in terms of aberrant reconfiguration of functional brain networks. Despite its evident impact, graph analysis of functional brain networks is not a simple toolbox that can be blindly applied to brain signals. On the one hand, it requires a know-how of all the methodological steps of the processing pipeline that manipulates the input brain signals and extract the functional network properties. On the other hand, a knowledge of the neural phenomenon under study is required to perform physiological-relevant analysis. The aim of this review is to provide practical indications to make sense of brain network analysis and contrast counterproductive attitudes

Analysis of Signal Decomposition and Stain Separation methods for biomedical applications

Nowadays, the biomedical signal processing and classification and medical image interpretation play an essential role in the detection and diagnosis of several human diseases. The problem of high variability and heterogeneity of information, which is extracted from digital data, can be addressed with signal decomposition and stain separation techniques which can be useful approaches to highlight hidden patterns or rhythms in biological signals and specific cellular structures in histological color images, respectively. This thesis work can be divided into two macro-sections. In the first part (Part I), a novel cascaded RNN model based on long short-term memory (LSTM) blocks is presented with the aim to classify sleep stages automatically. A general workflow based on single-channel EEG signals is developed to enhance the low performance in staging N1 sleep without reducing the performances in the other sleep stages (i.e. Wake, N2, N3 and REM). In the same context, several signal decomposition techniques and time-frequency representations are deployed for the analysis of EEG signals. All extracted features are analyzed by using a novel correlation-based timestep feature selection and finally the selected features are fed to a bidirectional RNN model. In the second part (Part II), a fully automated method named SCAN (Stain Color Adaptive Normalization) is proposed for the separation and normalization of staining in digital pathology. This normalization system allows to standardize digitally, automatically and in a few seconds, the color intensity of a tissue slide with respect to that of a target image, in order to improve the pathologist’s diagnosis and increase the accuracy of computer-assisted diagnosis (CAD) systems. Multiscale evaluation and multi-tissue comparison are performed for assessing the robustness of the proposed method. In addition, a stain normalization based on a novel mathematical technique, named ICD (Inverse Color Deconvolution) is developed for immunohistochemical (IHC) staining in histopathological images. In conclusion, the proposed techniques achieve satisfactory results compared to state-of-the-art methods in the same research field. The workflow proposed in this thesis work and the developed algorithms can be employed for the analysis and interpretation of other biomedical signals and for digital medical image analysis

Third- and Fifth-order Nonlinear Time-resolved Spectroscopies for Ultrafast Molecular Dynamics in Carotenoids

Multipulse optical technique is an essential tool on the direct observation of electron-nuclear motions responsible for various molecular properties. For example, light-energy harvesting or anti-oxidation, which flora and fauna have achieved along the course of evolution, are initiated by the molecular dynamics in conjugated hydrocarbons such as conjugated polyenes or porphyrin rings. In the case of the conjugated systems in photosynthetic pigments, a part of the dynamics has been revealed as an electronic-state dynamics. However, it is required to disentangle the remaining part of the molecular dynamics mainly consisting of the vibronic interactions induced by nuclear motions. In the scope of this thesis, the vibronic interactions between the electronic states having Bu or Ag symmetries in conjugated polyenes were detected by use of pump-probe and pump-degenerate four-wave mixing (DFWM) experiments. In addition, as an example of multimodal time-resolved spectroscopies, the combination of the two optical experiments was demonstrated to overcome analytical problems related to precision and accuracy of functional analysis for pump-probe spectra. The significant influences of the vibronic interactions between the electronic states with Ag-Ag or Bu-Bu symmetries were observed by pump-DFWM experiments for a series of the conjugated polyenes having four different conjugated double bond length N = 9, 10, 11 and 13. The frequency shifts of C–C (1100 cm–1) and C=C (1500 cm–1 for Bu state, 1800 cm–1 for Ag states) stretching modes indicated the features and some difference of the two couplings. The coupling between Ag– states appeared for all polyenes under the existence of the excited state with Ag symmetry. On the other hand, the coupling between Bu states only appeared for the polyenes with N = 9 and 10, in which strong degeneracy of two Bu states can exist. In addition, solvent polarizability changed the coupling strength which was examined for lutein (N = 9.5) in three different solvents (hexane, THF and benzene). While the coupling appeared in hexane and in THF, it was absent in benzene since the degeneracy of the ionic Bu+ state and covalent Bu– state were very sensitive to solvent polarizability. The observation could be connected to environmental effects on the photosynthetic polyenes surrounded by proteins and lipids in photosynthetic apparatus. In addition, an example of multimodal approach, which combines two different optical experiments, was demonstrated by the simultaneous analysis of a pair of data sets recorded by pump-probe and pump-DFWM experiments. This approach overcame conventional analytical problems of rotation ambiguity and local minimum in global target fitting. While the characterization of the relaxation model for rhodamine 6G was not uniquely done by global target fitting, the multimodal approach uniquely determined the appropriate kinetic model by the evaluation of four error functions. Moreover, the interpretation of the spectral and temporal elements were based on the response functions of pump-probe and pump-DFWM experiments. The direct detection of vibronic coupling and the methodological development to disentangle the ultrafast molecular dynamics contributes to the investigation of nonadiabatic processes which is crucial to understand molecular properties