Journal of Integrative Neuroscience

We propose a neural mass model for anatomically-constrained effective connectivity among neuronal populations residing in four layers (L2/3, L4, L5 and L6) within a cortical column. Eight neuronal populations in a given column — an excitatory population and an inhibitory population per layer — are assumed to be coupled via effective connections of unknown strengths that need to be estimated. The effective connections are constrained to anatomical connections that have been shown to exist in previous anatomical studies. The neural input to a cortical column is directed into the two populations in L4. The anatomically-constrained effective connectivity is captured by a system of 16 stochastic differential equations. Solving these equations yields the average postsynaptic potentials and transmembrane currents generated in each population. The current source density (CSD) responses in each layer, which serve as the model observations, are equated in the model to the sum of all currents generated within that layer. The model is implemented in a continuous-discrete state-space framework, and the innovation method is used for estimating the model parameters from CSD data. To this end, local field potential (LFP) responses to forepaw stimulation were recorded in rat area S1 using multi-channel linear probes. LFPs were converted to CSD signals, which were averaged within each layer, yielding one CSD response per layer. To estimate the effective strengths of connections between all cortical layers, the model was fitted to these CSD signals. The results show that the pattern of effective interactions is strongly influenced by the pattern of strengths of the anatomical connections; however, these two patterns are not identical. The estimated anatomically-constrained effective connectivity matrix and the anatomical connectivity matrix shared five of their six strongest connections, although rankings according to connection strength differed. The strongest effective connections were from excitatory neurons in layer 4 to excitatory neurons in layer 2/3. Our study shows the feasibility of estimating anatomically-constrained effective connectivity within a cortical column, and indicates that there is a strong influence of anatomical connectivity on effective connectivity between cortical layers.

For the purpose of statistical characterization of the spatio-temporal correlation structure of brain functioning from high-dimensional fMRI time series, we introduce an innovation approach. This is based on whitening the data by the Nearest-Neighbors AutoRegressive model with external inputs (NN-ARx). Correlations between the resulting innovations are an extension of the usual correlations, in which mean-correction is carried out by the dynamic NN-ARx model instead of the static, standard linear model for fMRI time series. Measures of dependencies between regions are defined by summarizing correlations among innovations at several time lags over pairs of voxels. Such summarization does not involve averaging the data over each region, which prevents loss of information in case of non-homogeneous regions. Statistical tests based on these measures are elaborated, which allow for assessing the correlation structure in search of connectivity. Results of application of the NN-ARx approach to fMRI data recorded in visual stimuli experiments are shown. Finally, a number of issues related with its potential and limitations are commented.

This article presents a novel method for activation detection in task-related functional magnetic resonance imaging (fMRI) based on the Empirical Mode Decomposition (EMD) algorithm. The basic concept stems mainly from the idea that the EMD performs well in isolating the imbedded stimulus from the activated Blood Oxygen Level Dependent (BOLD) signal. The power of the proposed method was compared with the General Linear Model (GLM), spatial Independent Component Analysis (ICA) and Region Growing (RG) methods on simulated and real datasets. Experimental results suggest an almost identical performance for the proposed method compared with the standard approach of fMRI signal detection (the GLM), which indicates that it is to become a viable alternative to fMRI analysis.

The analysis of time series obtained by functional magnetic resonance imaging (FMRI) may be approached by fitting predictive parametric models, such as nearest-neighbor autoregressive models with exogeneous input (NNARX). As a part of the modeling procedure, it is possible to apply instantaneous linear transformations to the data. Spatial smoothing, a common preprocessing step, may be interpreted as such a transformation. The autoregressive parameters may be constrained, such that they provide a response behavior that corresponds to the canonical haemodynamic response function (HRF). We present an algorithm for estimating the parameters of the linear transformations and of the HRF within a rigorous maximum-likelihood framework. Using this approach, an optimal amount of both the spatial smoothing and the HRF can be estimated simultaneously for a given FMRI data set. An example from a motor-task experiment is discussed. It is found that, for this data set, weak, but non-zero, spatial smoothing is optimal. Furthermore, it is demonstrated that activated regions can be estimated within the maximum-likelihood framework.

The diverse nature of cerebral activity, as measured using neuroimaging techniques, has been recognised long ago. It seems obvious that using single modality recordings can be limited when it comes to capturing its complex nature. Thus, it has been argued that moving to a multimodal approach will allow neuroscientists to better understand the dynamics and structure of this activity. This means that integrating information from different techniques, such as electroencephalography (EEG) and the blood oxygenated level dependent (BOLD) signal recorded with functional magnetic resonance imaging (fMRI), represents an important methodological challenge. In this work, we review the work that has been done thus far to derive EEG/fMRI integration approaches. This leads us to inspect the conditions under which such an integration approach could work or fail, and to disclose the types of scientific questions one could (and could not) hope to answer with it.

The integrity of the neuronal connections between eye and brain plays an important role in the performance of the mammalian visual system. However, the developmental and pathophysiological mechanisms in the visual system are largely unexplored due to the lack of a sensitive technique for directly assessing both anterior and posterior visual pathways longitudinally under the same experimental conditions. This paper reviewed the recent use of magnetic resonance imaging and spectroscopic (MRI/MRS) methods (contrast-enhanced MRI, diffusion MRI, proton MRS and functional MRI) at high magnetic field strengths, for in vivo and global assessments of the structure, metabolism and function of the visual system in normal, developing and injured rodent brains. Using animal models of ocular diseases, optic neuropathies, developmental plasticity and neonatal hypoxic-ischemic brain injury, focus is put on the feasibility of MRI/MRS to evaluate axonal transport and cellular activity along segregated fibers of the visual pathways, to characterize lesion-induced neurodegeneration in the retina and the optic nerve and tract, to detect steady-state metabolite changes in the posterior visual nuclei, and blood-ocular dynamic exchanges in the eye, and to understand the neurovascular coupling and functions in the retina and the visual brain nuclei. These studies suggested the significant values of high-field multiparametric MRI/MRS for providing early diagnoses and comprehensive therapeutic strategies for promoting functional recovery upon partial vision loss.