Eduardo Martínez-Montes

Characterizing brain anatomical connections using diffusion weighted MRI and graph theory

A new methodology based on Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) and Graph Theory is presented for characterizing the anatomical connections between brain gray matter areas. In a first step, brain voxels are modeled as nodes of a non-directed graph in which the weight of an arc linking two neighbor nodes is assumed to be proportional to the probability of being connected by nervous fibers. This probability is estimated by means of probabilistic tissue segmentation and intravoxel white matter orientational distribution function, obtained from anatomical MRI and DW-MRI, respectively. A new tractography algorithm for finding white matter routes is also introduced. This algorithm solves the most probable path problem between any two nodes, leading to the assessment of probabilistic brain anatomical connection maps. In a second step, for assessing anatomical connectivity between K gray matter structures, the previous graph is redefined as a K+1 partite graph by partitioning the initial nodes set in K non-overlapped gray matter subsets and one subset clustering the remaining nodes. Three different measures are proposed for quantifying anatomical connections between any pair of gray matter subsets: Anatomical Connection Strength (ACS), Anatomical Connection Density (ACD) and Anatomical Connection Probability (ACP). This methodology was applied to both artificial and actual human data. Results show that nervous fiber pathways between some regions of interest were reconstructed correctly. Additionally, mean connectivity maps of ACS, ACD and ACP between 71 gray matter structures for five healthy subjects are presented.

Decomposing EEG data into space–time–frequency components using Parallel Factor Analysis

Abstract Finding the means to efficiently summarize electroencephalographic data has been a long-standing problem in electrophysiology. A popular approach is identification of component modes on the basis of the time-varying spectrum of multichannel EEG recordings—in other words, a space/frequency/time atomic decomposition of the time-varying EEG spectrum. Previous work has been limited to only two of these dimensions. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) have been used to create space/time decompositions; suffering an inherent lack of uniqueness that is overcome only by imposing constraints of orthogonality or independence of atoms. Conventional frequency/time decompositions ignore the spatial aspects of the EEG. Framing of the data being as a three-way array indexed by channel, frequency, and time allows the application of a unique decomposition that is known as Parallel Factor Analysis (PARAFAC). Each atom is the tri-linear decomposition into a spatial, spectral, and temporal signature. We applied this decomposition to the EEG recordings of five subjects during the resting state and during mental arithmetic. Common to all subjects were two atoms with spectral signatures whose peaks were in the theta and alpha range. These signatures were modulated by physiological state, increasing during the resting stage for alpha and during mental arithmetic for theta. Furthermore, we describe a new method (Source Spectra Imaging or SSI) to estimate the location of electric current sources from the EEG spectrum. The topography of the theta atom is frontal and the maximum of the corresponding SSI solution is in the anterior frontal cortex. The topography of the alpha atom is occipital with maximum of the SSI solution in the visual cortex. We show that the proposed decomposition can be used to search for activity with a given spectral and topographic profile in new recordings, and that the method may be useful for artifact recognition and removal.

Mother and Stranger: An Electrophysiological Study of Voice Processing in Newborns

In the mature adult brain, there are voice selective regions that are especially tuned to familiar voices. Yet, little is known about how the infants brain treats such information. Here, we investigated, using electrophysiology and source analyses, how newborns process their mothers voice compared with that of a stranger. Results suggest that, shortly after birth, newborns distinctly process their mothers voice at an early preattentional level and at a later presumably cognitive level. Activation sources revealed that exposure to the maternal voice elicited early language-relevant processing, whereas the strangers voice elicited more voice-specific responses. A central probably motor response was also observed at a later time, which may reflect an innate auditory-articulatory loop. The singularity of left-dominant brain activation pattern together with its ensuing sustained greater central activation in response to the mothers voice may provide the first neurophysiologic index of the preferential mothers role in language acquisition.

White matter architecture rather than cortical surface area correlates with the EEG alpha rhythm

There are few studies on the neuroanatomical determinants of EEG spectral properties that would explain its substantial inter-individual variability in spite of decades of biophysical modeling that predicts this type of relationship. An exception is the negative relation between head size and the spectral position of the alpha peak (P(alpha)) reported in Nunez et al. (1978)-proposed as evidence of the influence of global boundary conditions on slightly damped neocortical waves. Here, we attempt to reexamine this finding by computing the correlations of occipital P(alpha) with various measures of head size and cortical surface area, for 222 subjects from the EEG/MRI database of the Cuban Human Brain Mapping Project. No relation is found (p>0.05). On the other hand, biophysical models also predict that white matter architecture, determining time delays and connectivities, could have an important influence on P(alpha). This led us to explore relations between P(alpha) and DTI fractional anisotropy by means of a multivariate penalized regression. Clusters of voxels with highly significant relations were found. These were positive within the Posterior and Superior Corona Radiata for both hemispheres, supporting biophysical theories predicting that the period of cortico-thalamocortical cycles might be modulating the alpha frequency. Posterior commissural fibers of the Corpus Callosum present the strongest relationships, negative in the inferior part (Splenium), connecting the inferior occipital lobes and positive in the superior part (Isthmus and Tapetum), connecting the superior occipital cortices. We found that white matter architecture rather than neocortical area determines the dynamics of the alpha rhythm.

Glucose Metabolism during Resting State Reveals Abnormal Brain Networks Organization in the Alzheimer’s Disease and Mild Cognitive Impairment

This paper aims to study the abnormal patterns of brain glucose metabolism co-variations in Alzheimer disease (AD) and Mild Cognitive Impairment (MCI) patients compared to Normal healthy controls (NC) using the Alzheimer Disease Neuroimaging Initiative (ADNI) database. The local cerebral metabolic rate for glucose (CMRgl) in a set of 90 structures belonging to the AAL atlas was obtained from Fluro-Deoxyglucose Positron Emission Tomography data in resting state. It is assumed that brain regions whose CMRgl values are significantly correlated are functionally associated; therefore, when metabolism is altered in a single region, the alteration will affect the metabolism of other brain areas with which it interrelates. The glucose metabolism network (represented by the matrix of the CMRgl co-variations among all pairs of structures) was studied using the graph theory framework. The highest concurrent fluctuations in CMRgl were basically identified between homologous cortical regions in all groups. Significant differences in CMRgl co-variations in AD and MCI groups as compared to NC were found. The AD and MCI patients showed aberrant patterns in comparison to NC subjects, as detected by global and local network properties (global and local efficiency, clustering index, and others). MCI network’s attributes showed an intermediate position between NC and AD, corroborating it as a transitional stage from normal aging to Alzheimer disease. Our study is an attempt at exploring the complex association between glucose metabolism, CMRgl covariations and the attributes of the brain network organization in AD and MCI.

EEG source imaging with spatio‐temporal tomographic nonnegative independent component analysis

This article describes a spatio‐temporal EEG/MEG source imaging (ESI) that extracts a parsimonious set of “atoms” or components, each the outer product of both a spatial and a temporal signature. The sources estimated are localized as smooth, minimally overlapping patches of cortical activation that are obtained by constraining spatial signatures to be nonnegative (NN), orthogonal, sparse, and smooth‐in effect integrating ESI with NN‐ICA. This constitutes a generalization of work by this group on the use of multiple penalties for ESI. A multiplicative update algorithm is derived being stable, fast and converging within seconds near the optimal solution. This procedure, spatio‐temporal tomographic NN ICA (STTONNICA), is equally able to recover superficial or deep sources without additional weighting constraints as tested with simulations. STTONNICA analysis of ERPs to familiar and unfamiliar faces yields an occipital‐fusiform atom activated by all faces and a more frontal atom that only is active with familiar faces. The temporal signatures are at present unconstrained but can be required to be smooth, complex, or following a multivariate autoregressive model. Hum Brain Mapp, 2009.

Hemispheric modulations of alpha-band power reflect the rightward shift in attention induced by enhanced attentional load.

Rightward shifts in attention are a common consequence of brain injury. A growing body of evidence appears to suggest that increases in attentional load, and decreases in alertness can lead to rightward shifts in attention in healthy and patient populations. It is unclear however whether these factors affect spatial biases in attention at the level of preparatory control processes or at the level of stimulus driven expression mechanisms. Whilst such effects cannot easily be dissociated behaviourally, the robust association between changes in alpha-band activity and shifts in visual attention provides a neural marker by which the temporal dynamics of effects of attentional load on spatial processing might be examined. Here we use electroencephalography to examine the relationship between modulations in alpha-band activity and behavioural outcome on a dual task paradigm comprising a detection task (t1), closely followed by a temporal order judgment task (t2). We examine the effects of high (respond to t1 and t2) and low (t2 only) attentional load conditions on spatial bias and changes in lateralization of alpha-band activity over the course of the trial. As anticipated a rightward bias in detecting target onsets was observed in the temporal order judgment task (t2) under conditions of high attentional load. This rightward shift in attention was associated with changes in the lateralization of alpha-band activity that occurred only after the presentation of t2, suggesting that attentional load may primarily influence expression mechanisms.

A comparison of methods for assessing alpha phase resetting in electrophysiology, with application to intracerebral EEG in visual areas.

There are two competing views on the mechanisms underlying the generation of visual evoked potentials/fields in EEG/MEG. The classical hypothesis assumes an additive wave on top of background noise. Another hypothesis states that the evoked activity can totally or partially arise from a phase resetting of the ongoing alpha rhythm. There is no consensus however, on the best tools for distinguishing between these two hypotheses. In this study, we have tested different measures on a large series of simulations under a variety of scenarios, involving in particular trial-to-trial variability and different dynamics of ongoing alpha rhythm. No single measure or set of measures was found to be necessary or sufficient for defining phase resetting in the context of our simulations. Still, simulations permitted to define criteria that were the most reliable in practice for distinguishing additive and phase resetting hypotheses. We have then applied these criteria on intracerebral EEG data recordings in the visual areas during a visual discrimination task. We investigated the intracerebral channels that presented both ERP and ongoing alpha oscillations (n=37). Within these channels, a total of 30% fulfilled phase resetting criteria during the generation of the visual evoked potential, based on criteria derived from simulations. Moreover, 19% of the 37 channels presented dependence of the ERP on the level of pre-stimulus alpha. Only 5% of channels fulfilled both the simulation-related criteria and dependence on baseline alpha level. Our simulation study points out to the difficulty of clearly assessing phase resetting based on observed macroscopic electrophysiological signals. Still, some channels presented an indication of phase resetting in the context of our simulations. This needs to be confirmed by further work, in particular at a smaller recording scale.

Source Analysis of Alpha Rhythm Reactivity Using LORETA Imaging with 64-Channel EEG and Individual MRI:

Conventional EEG and quantitative EEG visual stimuli (close-open eyes) reactivity analysis have shown their usefulness in clinical practice; however studies at the level of EEG generators are limited. The focus of the study was visual reactivity of cortical resources in healthy subjects and in a stroke patient. The 64 channel EEG and T1 magnetic resonance imaging (MRI) studies were obtained from 32 healthy subjects and a middle cerebral artery stroke patient. Low Resolution Electromagnetic Tomography (LORETA) was used to estimate EEG sources for both close eyes (CE) vs. open eyes (OE) conditions using individual MRI. The t-test was performed between source spectra of the two conditions. Thresholds for statistically significant t values were estimated by the local false discovery rate (lfdr) method. The Z transform was used to quantify the differences in cortical reactivity between the patient and healthy subjects. Closed-open eyes alpha reactivity sources were found mainly in posterior regions (occipito-parietal zones), extended in some cases to anterior and thalamic regions. Significant cortical reactivity sources were found in frequencies different from alpha (lower t-values). Significant changes at EEG reactivity sources were evident in the damaged brain hemisphere. Reactivity changes were also found in the “healthy” hemisphere when compared with the normal population. In conclusion, our study of brain sources of EEG alpha reactivity provides information that is not evident in the usual topographic analysis.

Identifying Complex Brain Networks Using Penalized Regression Methods

The recorded electrical activity of complex brain networks through the EEG reflects their intrinsic spatial, temporal and spectral properties. In this work we study the application of new penalized regression methods to i) the spatial characterization of the brain networks associated with the identification of faces and ii) the PARAFAC analysis of resting-state EEG. The use of appropriate constraints through non-convex penalties allowed three types of inverse solutions (Loreta, Lasso Fusion and ENet L) to spatially localize networks in agreement with previous studies with fMRI. Furthermore, we propose a new penalty based in the Information Entropy for the constrained PARAFAC analysis of resting EEG that allowed the identification in time, frequency and space of those brain networks with minimum spectral entropy. This study is an initial attempt to explicitly include complexity descriptors as a constraint in multilinear EEG analysis.