262 research outputs found
Disruption of transfer entropy and inter-hemispheric brain functional connectivity in patients with disorder of consciousness
Severe traumatic brain injury can lead to disorders of consciousness (DOC)
characterized by deficit in conscious awareness and cognitive impairment
including coma, vegetative state, minimally consciousness, and lock-in
syndrome. Of crucial importance is to find objective markers that can account
for the large-scale disturbances of brain function to help the diagnosis and
prognosis of DOC patients and eventually the prediction of the coma outcome.
Following recent studies suggesting that the functional organization of brain
networks can be altered in comatose patients, this work analyzes brain
functional connectivity (FC) networks obtained from resting-state functional
magnetic resonance imaging (rs-fMRI). Two approaches are used to estimate the
FC: the Partial Correlation (PC) and the Transfer Entropy (TE). Both the PC and
the TE show significant statistical differences between the group of patients
and control subjects; in brief, the inter-hemispheric PC and the
intra-hemispheric TE account for such differences. Overall, these results
suggest two possible rs-fMRI markers useful to design new strategies for the
management and neuropsychological rehabilitation of DOC patients.Comment: 25 pages; 4 figures; 3 tables; 1 supplementary figure; 4
supplementary tables; accepted for publication in Frontiers in
Neuroinformatic
New neuroimaging methods for clinical neuroscience and neurological disorders
236 p.Clinical neuroscience today makes use of state-of-the-art neuroimaging to study structural and functional brain data to improve diagnosis and prognosis in different neurological disorders.In this thesis dissertation, I focused on Magnetic Resonance Imaging (MRI), a non-invasive neuroimaging modality to study brain functional and structural data. Different new methods for brain connectivity analysis are described and applied to three pathologies: Disorder of Consciousness, Alzheimer's Disease and Traumatic Axonal Injury. This work is at the frontiers between two fields, the Biomedical Engineering of Image Processing and the Clinical Neuroscience
Information flow between resting state networks
The resting brain dynamics self-organizes into a finite number of correlated
patterns known as resting state networks (RSNs). It is well known that
techniques like independent component analysis can separate the brain activity
at rest to provide such RSNs, but the specific pattern of interaction between
RSNs is not yet fully understood. To this aim, we propose here a novel method
to compute the information flow (IF) between different RSNs from resting state
magnetic resonance imaging. After haemodynamic response function blind
deconvolution of all voxel signals, and under the hypothesis that RSNs define
regions of interest, our method first uses principal component analysis to
reduce dimensionality in each RSN to next compute IF (estimated here in terms
of Transfer Entropy) between the different RSNs by systematically increasing k
(the number of principal components used in the calculation). When k = 1, this
method is equivalent to computing IF using the average of all voxel activities
in each RSN. For k greater than one our method calculates the k-multivariate IF
between the different RSNs. We find that the average IF among RSNs is
dimension-dependent, increasing from k =1 (i.e., the average voxels activity)
up to a maximum occurring at k =5 to finally decay to zero for k greater than
10. This suggests that a small number of components (close to 5) is sufficient
to describe the IF pattern between RSNs. Our method - addressing differences in
IF between RSNs for any generic data - can be used for group comparison in
health or disease. To illustrate this, we have calculated the interRSNs IF in a
dataset of Alzheimer's Disease (AD) to find that the most significant
differences between AD and controls occurred for k =2, in addition to AD
showing increased IF w.r.t. controls.Comment: 47 pages, 5 figures, 4 tables, 3 supplementary figures. Accepted for
publication in Brain Connectivity in its current for
Evaluation of brain functional connectivity from electroencephalographic signals under different emotional states
The identification of the emotional states corresponding to the four quadrants of the valence/arousal space has been widely analyzed in the scientific literature by means of multiple techniques. Nevertheless, most of these methods were based on the assessment of each brain region separately, without considering the possible interactions among different areas. In order to study these interconnections, this study computes for the first time the functional connectivity metric called cross-sample entropy for the analysis of the brain synchronization in four groups of emotions from electroencephalographic signals. Outcomes reported a strong synchronization in the interconnections among central, parietal and occipital areas, while the interactions between left frontal and temporal structures with the rest of brain regions presented the lowest coordination. These differences were statistically significant for the four groups of emotions. All emotions were simultaneously classified with a 95.43% of accuracy, overcoming the results reported in previous studies. Moreover, the differences between high and low levels of valence and arousal, taking into account the state of the counterpart dimension, also provided notable findings about the degree of synchronization in the brain within different emotional conditions and the possible implications of these outcomes from a psychophysiological point of view.- This publication is part of the R&D Projects Nos. PID2020-115220RB-C21, EQC2019-006063P, funded by MCIN/AEI/10.13039/501100011033/, and 2018/11744, funded by "ERDF A way to make Europe". This work was partially supported by Biomedical Research Networking Centre in Mental Health (CIBERSAM) of the Instituto de Salud Carlos III. Beatriz Garcia-Martinez holds FPU16/03740 scholarship from Spanish Ministerio de Educacion y Formacion Profesional
Mutual Information in Coupled Double Quantum Dots: A Simple Analytic Model for Potential Artificial Consciousness
The integrated information theory is thought to be a key clue towards the
theoretical understanding of consciousness. In this study, we propose a simple
numerical model comprising a set of coupled double quantum dots, where the
disconnection of the elements is represented by the removal of Coulomb
interaction between the quantum dots, for the quantitative investigation of
integrated information. As a measure of integrated information, we calculate
the mutual information in the model system, as the Kullback-Leibler divergence
between the connected and disconnected status, through the probability
distribution of the electronic states from the master transition-rate
equations. We reasonably demonstrate that the increase in the strength of
interaction between the quantum dots leads to higher mutual information, owing
to the larger divergence in the probability distributions of the electronic
states. Our model setup could be a useful basic tool for numerical analyses in
the field of integrated information theory.Comment: 10 pages, 6 figure
Delirium screening in the intensive care unit using emerging QEEG techniques : A pilot study
This work was supported by an Alzheimer’s Society grant (project grant number AS-PG-14-039 to BP) and a British Journal of Anaesthesia / Royal College of Anaesthetists funded John Snow Anaesthesia iBSc Award (to AH).Peer reviewedPublisher PD
High-density EEG power topography and connectivity during confusional arousal.
Confusional arousal is the milder expression of a family of disorders known as Disorders of Arousal (DOA) from non-REM sleep. These disorders are characterized by recurrent abnormal behaviors that occur in a state of reduced awareness for the external environment. Despite frequent amnesia for the nocturnal events, when actively probed, patients are able to report vivid hallucinatory/dream-like mental imagery. Traditional (low-density) scalp and stereo-electroencephalographic (EEG) recordings previously showed a pathological admixture of slow oscillations typical of NREM sleep and wake-like fast-mixed frequencies during these phenomena. However, our knowledge about the specific neural EEG dynamics over the entire brain is limited. We collected 2 consecutive in-laboratory sleep recordings using high-density (hd)-EEG (256 vertex-referenced geodesic system) coupled with standard video-polysomnography (v-PSG) from a 12-year-old drug-naïve and otherwise healthy child with a long-lasting history of sleepwalking. Source power topography and functional connectivity were computed during 20 selected confusional arousal episodes (from -6 to +18 sec after motor onset), and during baseline slow wave sleep preceding each episode (from - 3 to -2 min before onset). We found a widespread increase in slow wave activity (SWA) theta, alpha, beta, gamma power, associated with a parallel decrease in the sigma range during behavioral episodes compared to baseline sleep. Bilateral Broadman area 7 and right Broadman areas 39 and 40 were relatively spared by the massive increase in SWA power. Functional SWA connectivity analysis revealed a drastic increase in the number and complexity of connections from baseline sleep to full-blown episodes, that mainly involved an increased out-flow from bilateral fronto-medial prefrontal cortex and left temporal lobe to other cortical regions. These effects could be appreciated in the 6 sec window preceding behavioral onset. Overall, our results support the idea that DOA are the expression of peculiar brain states, compatible with a partial re-emergence of consciousness
Methods and models for brain connectivity assessment across levels of consciousness
The human brain is one of the most complex and fascinating systems in nature. In the last decades, two events have boosted the investigation of its functional and structural properties. Firstly, the emergence of novel noninvasive neuroimaging modalities, which helped improving the spatial and temporal resolution of the data collected from in vivo human brains. Secondly, the development of advanced mathematical tools in network science and graph theory, which has recently translated into modeling the human brain as a network, giving rise to the area of research so called Brain Connectivity or Connectomics.
In brain network models, nodes correspond to gray-matter regions (based on functional or structural, atlas-based parcellations that constitute a partition), while links or edges correspond either to structural connections as modeled based on white matter fiber-tracts or to the functional coupling between brain regions by computing statistical dependencies between measured brain activity from different nodes.
Indeed, the network approach for studying the brain has several advantages:
1) it eases the study of collective behaviors and interactions between regions;
2) allows to map and study quantitative properties of its anatomical pathways;
3) gives measures to quantify integration and segregation of information processes in the brain, and the flow (i.e. the interacting dynamics) between different cortical and sub-cortical regions.
The main contribution of my PhD work was indeed to develop and implement new models and methods for brain connectivity assessment in the human brain, having as primary application the analysis of neuroimaging data coming from subjects at different levels of consciousness. I have here applied these methods to investigate changes in levels of consciousness, from normal wakefulness (healthy human brains) or drug-induced unconsciousness (i.e. anesthesia) to pathological (i.e. patients with disorders of consciousness)
Lagged and instantaneous dynamical influences related to brain structural connectivity
Contemporary neuroimaging methods can shed light on the basis of human neural
and cognitive specializations, with important implications for neuroscience and
medicine. Different MRI acquisitions provide different brain networks at the
macroscale; whilst diffusion-weighted MRI (dMRI) provides a structural
connectivity (SC) coincident with the bundles of parallel fibers between brain
areas, functional MRI (fMRI) accounts for the variations in the
blood-oxygenation-level-dependent T2* signal, providing functional connectivity
(FC).Understanding the precise relation between FC and SC, that is, between
brain dynamics and structure, is still a challenge for neuroscience. To
investigate this problem, we acquired data at rest and built the corresponding
SC (with matrix elements corresponding to the fiber number between brain areas)
to be compared with FC connectivity matrices obtained by 3 different methods:
directed dependencies by an exploratory version of structural equation modeling
(eSEM), linear correlations (C) and partial correlations (PC). We also
considered the possibility of using lagged correlations in time series; so, we
compared a lagged version of eSEM and Granger causality (GC). Our results were
two-fold: firstly, eSEM performance in correlating with SC was comparable to
those obtained from C and PC, but eSEM (not C nor PC) provides information
about directionality of the functional interactions. Second, interactions on a
time scale much smaller than the sampling time, captured by instantaneous
connectivity methods, are much more related to SC than slow directed influences
captured by the lagged analysis. Indeed the performance in correlating with SC
was much worse for GC and for the lagged version of eSEM. We expect these
results to supply further insights to the interplay between SC and functional
patterns, an important issue in the study of brain physiology and function.Comment: Accepted and published in Frontiers in Psychology in its current
form. 27 pages, 1 table, 5 figures, 2 suppl. figure
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