Brain networks in people after a first unprovoked seizure

Background: A single unprovoked seizure occurs in up to 10% of the population. Some develop epilepsy, but the majority do not. Brain network changes are observed in people with epilepsy, but it is unknown if they are present after this first seizure. This study examines network connectivity after the first seizure to determine if any changes exist. Methods: Twelve patients after a single unprovoked seizure and twelve age- and sex-matched healthy controls were recruited. All underwent 7T resting-state fMRI scanning. Whole brain and limbic, default mode and salience network connectivity were analyzed with graph theory. Results: Baseline characteristics were similar between groups. No network connectivity differences were observed between groups. Conclusions: No network connectivity differences were found between patients and controls. This suggests that there are not inherent connectivity differences predisposing an individual to seizures; however, the small sample size and considerable variability could prevent realization of small group differences

Characterization of the spontaneous EEG activity in the Alzheimer's disease continuum: from local activation to network organization

La presente Tesis Doctoral se presenta como un compendio de cuatro publicaciones indexadas en el Journal Citation Reports. El objetivo de estas publicaciones es la caracterización de los cambios neuronales subyacentes en las diferentes etapas de la enfermedad de Alzheimer (EA) y su etapa prodrómica, el deterioro cognitivo leve (DCL), siguiendo tres niveles de análisis: activación local, interacción entre pares de sensores, y organización de red. Los principales cambios encontrados a medida que progresa la enfermedad son: (i) una lentificación, y una pérdida de complejidad e irregularidad de la actividad EEG espontánea; (ii) una disminución significativa de la conectividad en bandas altas de frecuencia y un aumento en las bandas bajas; y (iii) una pérdida en la integración y la segregación de las redes neuronales. Estos hallazgos han proporcionado información adicional sobre las alteraciones cerebrales de la EA en sus diferentes etapas, útiles para comprender mejor sus mecanismos fisiopatológicos.Departamento de Teoría de la Señal y Comunicaciones e Ingeniería TelemáticaDoctorado en Tecnologías de la Información y las Telecomunicacione

Automatic classification of medical images based on functional connectivity measurements - methodological exploration

The study of patterns of neuronal activity constitutes a tool of extreme value in the attempt to unveil neural pathological mechanisms. Hence, functional connectivity studies using images from Resting State fMRI (rs-fMRI) are crucial, and there are several metrics which can be used to assess brain connections. Nonetheless, no clear evidence exists that some may be better than others. In this study, in an attempt to discover if certain metrics better characterized certain connections, two different approaches were followed. Data from a public dataset was used - Addiction Connectome Preprocessed Initiative (ACPI) - as well as one toolbox for matrix construction - Multiple Connectivity Analysis (MULAN) - and another for statistical comparison - GraphVar. Both toolboxes run in MATLAB. Metrics under analysis were: correlation, coherence, mutual information, transfer entropy and non-linear correlation. To that end, 116 brain regions were considered. First, considering only healthy subjects, it was done a pairwise comparison between results from different metrics. It was verified that each of them led to different results regarding the same connections. Then, connectivity results between a healthy and a pathological group of subjects with Attention-Deficit/Hyperactivity Disorder (ADHD) were compared. Concerning the differences, several similarities with the known affected areas described amongst the literature were found. However, discrepancies were observed which may be related to differences in sample size and/or the metric used in these studies. In general, it was shown that there is indeed variability between functional metrics and regional specificity. Still, the anatomical and physiological reasons for these differences remain unknown. It was clear that using more than one metric may be important and that the use of more general metrics may have advantages in the study of the pathological brain as it may have more complex dynamics. Furthermore, ensemble tools that have into consideration more than one metric to characterize brain connections may represent invaluable tools for autonomic image classification

Neural dynamics in brain networks during the resting state and visual word recognition

This thesis investigates the dynamics of information flow within brain networks during the resting state and visual word recognition. Functional connectivity within brain networks has become increasingly prominent across cognitive neuroscience and neuroimaging in recent years and conventional approaches for identifying instantaneous interactions within brain network and across the whole head are now commonplace. Magnetoencephalography (MEG) recordings have a very high temporal resolution which allows for the characterisation of delayed interactions between distant brain regions such as those caused by limited conduction speeds along white matter fibres. This thesis presents an approach to characterising such time-delayed interactions and critically, inferring the direction of information flow. This approach is used to demonstrate the existence of statistically significant differences in the information flow in each direction of a connection between two nodes in a resting state network. A Hidden Markov Model is then used to characterise dynamic changes in this directionality. Task driven directional connectivity is then investigated in the context of visual word recognition. A complex and rapidly evolving pattern of connectivity arises during visual word recognition, with specific connections modulated by the psycholinguistic properties of the stimulus. Critically, the influence from the Left Inferior Frontal Gyrus is shown to transfer more information to visual regions when reading a challenging stimulus

Fluctuations of attention network activation during the resting state reflect fluctuations in subjective attentional state.

peer reviewe

Structural connectivity and white matter health in adults born very low birthweight.

Children born very preterm or with a very low birthweight (VLBW), are at a significantly elevated risk of brain injury from inflammation or hypoxic-ischaemic events. Resulting damage to pre-myelinating cells and other issues, commonly lead to diffuse and acute white matter injury with long term impacts on cognitive and motor function. On average, over one in ten births are now preterm. Mortality rates for preterm births have declined, but unfortunately, neurological disorders remain a major impairment for this group. The New Zealand VLBW cohort enrolled all infants born VLBW in 1986, and has undergone several multi-disciplinary follow up studies. Most recently, this included comprehensive cranial MRI scans at an age between 26 and 30 years. The MRI session included diffusion-weighted imaging, to examine white matter health in early adulthood. Probabilistic tractography has been used to isolate whole white matter tracts and to construct structural networks. Of the 42 tracts identified, 12 showed significantly reduced volumes in the VLBW cohort (n= 141) compared to controls born normal birthweight (n = 49). These included the acoustic radiations, left cortico-spinal tract, left superior thalamic radiation, forceps major and minor, and the inferior longitudinal fasciculi. This indicates that the impact of an early birth remains as smaller WM volumes in early adulthood. Only three tracts showed altered diffusion properties. The forceps major and left temporal cingulum subsection showed a reduction in fractional anisotropy; these two tracts, along with the right optic radiation, also show an increase in radial diffusivity. These diffusion properties indicate poorer white matter health for these tracts, but this is much less pronounced than is commonly reported in child and adolescent studies. Taken together, these results suggest that the white matter of VLBW individuals may eventually mature similarly to their term born peers, but with lasting reductions in volume. Structural network analysis used: AAL3 parcellation; FSL’s probabilistic tractography; and two normalisations, a standard approach (waytotal) and a novel algorithm developed for this thesis (node strength normalisation). This analysis found the VLBW group had marginally increased global efficiency, with an unchanged characteristic path length, suggesting that the short paths may be shorter in the VLBW group. Mean clustering coefficient was significantly decreased, and node-wise clustering generally reflected this trend. Notably, the cerebellum showed a slightly higher clustering in the VLBW group potentially in relating to impaired motor function. Modularity was higher, indicating a stronger community structure in the scale of 20-40 nodes.This thesis also introduced a novel normalisation algorithm: node strength normalisation (NSN). This algorithm allows nodes to have their strengths estimated and scaled relative to each other, allowing meaningful comparisons between subjects, with minimal underlying assumptions. It is the hope that NSN will be applicable more broadly, improving the validity of structural network analyses across a wide range of neuroimaging applications

Enhancing visual motion discrimination by desynchronizing bifocal oscillatory activity

Visual motion discrimination involves reciprocal interactions in the alpha band between the primary visual cortex (V1) and mediotemporal areas (V5/MT). We investigated whether modulating alpha phase synchronization using individualized multisite transcranial alternating current stimulation (tACS) over V5 and V1 regions would improve motion discrimination. We tested 3 groups of healthy subjects with the following conditions: (1) individualized In-Phase V1alpha-V5alpha tACS (0° lag), (2) individualized Anti-Phase V1alpha-V5alpha tACS (180° lag) and (3) sham tACS. Motion discrimination and EEG activity were recorded before, during and after tACS. Performance significantly improved in the Anti-Phase group compared to the In-Phase group 10 and 30Â min after stimulation. This result was explained by decreases in bottom-up alpha-V1 gamma-V5 phase-amplitude coupling. One possible explanation of these results is that Anti-Phase V1alpha-V5alpha tACS might impose an optimal phase lag between stimulation sites due to the inherent speed of wave propagation, hereby supporting optimized neuronal communication