A group model for stable multi-subject ICA on fMRI datasets

Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract sets of mutually correlated brain regions without prior information on the time course of these regions. Some of these sets of regions, interpreted as functional networks, have recently been used to provide markers of brain diseases and open the road to paradigm-free population comparisons. Such group studies raise the question of modeling subject variability within ICA: how can the patterns representative of a group be modeled and estimated via ICA for reliable inter-group comparisons? In this paper, we propose a hierarchical model for patterns in multi-subject fMRI datasets, akin to mixed-effect group models used in linear-model-based analysis. We introduce an estimation procedure, CanICA (Canonical ICA), based on i) probabilistic dimension reduction of the individual data, ii) canonical correlation analysis to identify a data subspace common to the group iii) ICA-based pattern extraction. In addition, we introduce a procedure based on cross-validation to quantify the stability of ICA patterns at the level of the group. We compare our method with state-of-the-art multi-subject fMRI ICA methods and show that the features extracted using our procedure are more reproducible at the group level on two datasets of 12 healthy controls: a resting-state and a functional localizer study

Independent Component Analysis for Group Comparison of Functional MRI Images in Individuals with Parkinson's Disease

Parkinson's Disease (PD) is an age-related disorder that affects cognitive and motor abilities and lowers quality of life. As there is currently no cure, it is an area of interest for many research efforts. Parkinson's disease has a substantial effect on structures in the basal ganglia, which may be used to indicate signs of Parkinson's disease progression. Functional MRI (fMRI) is a means of measuring metabolic functioning in the brain. Brain imaging studies are not used to diagnose Parkinson's disease because it is unclear how it manifests in neuroimages. However, Parkinson's disease has a preclinical phase during which structures within the brain are affected, but external symptoms have not yet manifested. In this study, we sought to identify effects of Parkinson's disease that may be seen in functional imaging scans to allow earlier detection. We used independent component analysis (ICA) to identify functional brain networks followed by dual regression to estimate subject-level components for making comparisons of the functional images between the two groups: a group of subjects with Parkinson's disease and a group of healthy control subjects. We were able to generate subject-level components; however, identifying one component at the group level that included the basal ganglia proved problematic. Methods of identifying neural structures within the application we used provided conflicting evidence. Therefore, we were unable to determine if differences between the study groups existed that could be seen in functional imaging scans. Testing our primary endpoint using a voxel-wise linear regression with each of the components was not successful because most of the p values on the coefficient of interest were non-significant. In addition, there was a poor model fit seen in the regression models. We were unable to provide scientific evidence of differences that might be seen in functional MRI studies between subjects with Parkinson's disease and healthy control subjects

QRS Complex Separation from Convolutive Mixtures of Biolectrical Signals Acquired by Wearable Systems

Independent component analysis (ICA) has been widely used to remove artefacts from multichannel biomedical signal acquisitions under the hypothesis that there is statistical independence among the original sources. However, the basic ICA model does not take into account the influence on the mixing process of the different paths from the signal sources to the sensors In this study we propose a convolutive mixtures model in order to overcome the limitations of the basic ICA approach. The independent components are estimated in the frequency domain, where the convolutive model can be solved through an instantaneous mixing model. The signals are reconstructed back to the observation space resolving the ICA model ambiguities. Simulations are carried out to optimize of the proposed method for convolutive mixtures of electrocardiographic (ECG) and motion artefacts signals. The algorithm is tested on real ECG signals acquired by wearable systems in order to preserve the QRS complex when the signals are degraded by real life conditions of acquisition

Modeling sparse connectivity between underlying brain sources for EEG/MEG

We propose a novel technique to assess functional brain connectivity in EEG/MEG signals. Our method, called Sparsely-Connected Sources Analysis (SCSA), can overcome the problem of volume conduction by modeling neural data innovatively with the following ingredients: (a) the EEG is assumed to be a linear mixture of correlated sources following a multivariate autoregressive (MVAR) model, (b) the demixing is estimated jointly with the source MVAR parameters, (c) overfitting is avoided by using the Group Lasso penalty. This approach allows to extract the appropriate level cross-talk between the extracted sources and in this manner we obtain a sparse data-driven model of functional connectivity. We demonstrate the usefulness of SCSA with simulated data, and compare to a number of existing algorithms with excellent results.Comment: 9 pages, 6 figure

Flexible methods for blind separation of complex signals

One of the main matter in Blind Source Separation (BSS) performed with a neural network approach is the choice of the nonlinear activation function (AF). In fact if the shape of the activation function is chosen as the cumulative density function (c.d.f.) of the original source the problem is solved. For this scope in this thesis a flexible approach is introduced and the shape of the activation functions is changed during the learning process using the so-called “spline functions”. The problem is complicated in the case of separation of complex sources where there is the problem of the dichotomy between analyticity and boundedness of the complex activation functions. The problem is solved introducing the “splitting function” model as activation function. The “splitting function” is a couple of “spline function” which wind off the real and the imaginary part of the complex activation function, each of one depending from the real and imaginary variable. A more realistic model is the “generalized splitting function”, which is formed by a couple of two bi-dimensional functions (surfaces), one for the real and one for the imaginary part of the complex function, each depending by both the real and imaginary part of the complex variable. Unfortunately the linear environment is unrealistic in many practical applications. In this way there is the need of extending BSS problem in the nonlinear environment: in this case both the activation function than the nonlinear distorting function are realized by the “splitting function” made of “spline function”. The complex and instantaneous separation in linear and nonlinear environment allow us to perform a complex-valued extension of the well-known INFOMAX algorithm in several practical situations, such as convolutive mixtures, fMRI signal analysis and bandpass signal transmission. In addition advanced characteristics on the proposed approach are introduced and deeply described. First of all it is shows as splines are universal nonlinear functions for BSS problem: they are able to perform separation in anyway. Then it is analyzed as the “splitting solution” allows the algorithm to obtain a phase recovery: usually there is a phase ambiguity. Finally a Cramér-Rao lower bound for ICA is discussed. Several experimental results, tested by different objective indexes, show the effectiveness of the proposed approaches

Flexible methods for blind separation of complex signals

One of the main matter in Blind Source Separation (BSS) performed with a neural network approach is the choice of the nonlinear activation function (AF). In fact if the shape of the activation function is chosen as the cumulative density function (c.d.f.) of the original source the problem is solved. For this scope in this thesis a flexible approach is introduced and the shape of the activation functions is changed during the learning process using the so-called “spline functions”. The problem is complicated in the case of separation of complex sources where there is the problem of the dichotomy between analyticity and boundedness of the complex activation functions. The problem is solved introducing the “splitting function” model as activation function. The “splitting function” is a couple of “spline function” which wind off the real and the imaginary part of the complex activation function, each of one depending from the real and imaginary variable. A more realistic model is the “generalized splitting function”, which is formed by a couple of two bi-dimensional functions (surfaces), one for the real and one for the imaginary part of the complex function, each depending by both the real and imaginary part of the complex variable. Unfortunately the linear environment is unrealistic in many practical applications. In this way there is the need of extending BSS problem in the nonlinear environment: in this case both the activation function than the nonlinear distorting function are realized by the “splitting function” made of “spline function”. The complex and instantaneous separation in linear and nonlinear environment allow us to perform a complex-valued extension of the well-known INFOMAX algorithm in several practical situations, such as convolutive mixtures, fMRI signal analysis and bandpass signal transmission. In addition advanced characteristics on the proposed approach are introduced and deeply described. First of all it is shows as splines are universal nonlinear functions for BSS problem: they are able to perform separation in anyway. Then it is analyzed as the “splitting solution” allows the algorithm to obtain a phase recovery: usually there is a phase ambiguity. Finally a Cramér-Rao lower bound for ICA is discussed. Several experimental results, tested by different objective indexes, show the effectiveness of the proposed approaches