Reducing connectivity by using cortical modular bands

The way information is represented and processed in a neural network may have important consequences on its computational power and complexity. Basically, information representation refers to distributed or localist encoding and information processing refers to schemes of connectivity that can be complete or minimal. In the past, theoretical and biologically inspired approaches of neural computation have insisted on complementary views (respectively distributed and complete versus localist and minimal) with complementary arguments (complexity versus expressiveness). In this paper, we report experiments on biologically inspired neural networks performing sensorimotor coordination that indicate that a localist and minimal view may have good performances if some connectivity constraints (also coming from biological inspiration) are respected

EEG-Based Quantification of Cortical Current Density and Dynamic Causal Connectivity Generalized across Subjects Performing BCI-Monitored Cognitive Tasks.

Quantification of dynamic causal interactions among brain regions constitutes an important component of conducting research and developing applications in experimental and translational neuroscience. Furthermore, cortical networks with dynamic causal connectivity in brain-computer interface (BCI) applications offer a more comprehensive view of brain states implicated in behavior than do individual brain regions. However, models of cortical network dynamics are difficult to generalize across subjects because current electroencephalography (EEG) signal analysis techniques are limited in their ability to reliably localize sources across subjects. We propose an algorithmic and computational framework for identifying cortical networks across subjects in which dynamic causal connectivity is modeled among user-selected cortical regions of interest (ROIs). We demonstrate the strength of the proposed framework using a "reach/saccade to spatial target" cognitive task performed by 10 right-handed individuals. Modeling of causal cortical interactions was accomplished through measurement of cortical activity using (EEG), application of independent component clustering to identify cortical ROIs as network nodes, estimation of cortical current density using cortically constrained low resolution electromagnetic brain tomography (cLORETA), multivariate autoregressive (MVAR) modeling of representative cortical activity signals from each ROI, and quantification of the dynamic causal interaction among the identified ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting cortical network and the computed causal dynamics among its nodes exhibited physiologically plausible behavior, consistent with past results reported in the literature. This physiological plausibility of the results strengthens the framework's applicability in reliably capturing complex brain functionality, which is required by applications, such as diagnostics and BCI

Quantitative Multimodal Mapping Of Seizure Networks In Drug-Resistant Epilepsy

Over 15 million people worldwide suffer from localization-related drug-resistant epilepsy. These patients are candidates for targeted surgical therapies such as surgical resection, laser thermal ablation, and neurostimulation. While seizure localization is needed prior to surgical intervention, this process is challenging, invasive, and often inconclusive. In this work, I aim to exploit the power of multimodal high-resolution imaging and intracranial electroencephalography (iEEG) data to map seizure networks in drug-resistant epilepsy patients, with a focus on minimizing invasiveness. Given compelling evidence that epilepsy is a disease of distorted brain networks as opposed to well-defined focal lesions, I employ a graph-theoretical approach to map structural and functional brain networks and identify putative targets for removal. The first section focuses on mesial temporal lobe epilepsy (TLE), the most common type of localization-related epilepsy. Using high-resolution structural and functional 7T MRI, I demonstrate that noninvasive neuroimaging-based network properties within the medial temporal lobe can serve as useful biomarkers for TLE cases in which conventional imaging and volumetric analysis are insufficient. The second section expands to all forms of localization-related epilepsy. Using iEEG recordings, I provide a framework for the utility of interictal network synchrony in identifying candidate resection zones, with the goal of reducing the need for prolonged invasive implants. In the third section, I generate a pipeline for integrated analysis of iEEG and MRI networks, paving the way for future large-scale studies that can effectively harness synergy between different modalities. This multimodal approach has the potential to provide fundamental insights into the pathology of an epileptic brain, robustly identify areas of seizure onset and spread, and ultimately inform clinical decision making

Dynamic Configuration of Large-Scale Cortical Networks: A Useful Framework for Clarifying the Heterogeneity Found in Attention-Deficit/Hyperactivity Disorder

The heterogeneity of attention-deficit/hyperactivity disorder(ADHD) traits (inattention vs. hyperactivity/impulsivity) complicates diagnosis and intervention. Identifying how the configuration of large-scale functional brain networks during cognitive processing correlate with this heterogeneity could help us understand the neural mechanisms altered across ADHD presentations. Here, we recorded high-density EEG while 62 non-clinical participants (ages 18-24; 32 male) underwent an inhibitory control task (Go/No-Go). Functional EEG networks were created using sensors as nodes and across-trial phase-lag index values as edges. Using cross-validated LASSO regression, we examined whether graph-theory metrics applied to both static networks (averaged across time-windows: -500–0ms, 0–500ms) and dynamic networks (temporally layered with 2ms intervals), were associated with hyperactive/impulsive and inattentive traits. Network configuration during response execution/inhibition was associated with hyperactive/impulsive (mean R2across test sets = .20, SE = .02), but not inattentive traits. Post-stimulus results at higher frequencies (Beta, 14-29Hz; Gamma, 30-90Hz) showed the strongest association with hyperactive/impulsive traits, and predominantly reflected less burst-like integration between modules in oscillatory beta networks during execution, and increased integration/small-worldness in oscillatory gamma networks during inhibition. We interpret the beta network results as reflecting weaker integration between specialized pre-frontal and motor systems during motor response preparation, and the gamma results as reflecting a compensatory mechanism used to integrate processing between less functionally specialized networks. This research demonstrates that the neural network mechanisms underlying response execution/inhibition might be associated with hyperactive/impulsive traits, and that dynamic, task-related changes in EEG functional networks may be useful in disentangling ADHD heterogeneity

Temporal ordering of input modulates connectivity formation in a developmental neuronal network model of the cortex

Preterm infant brain activity is discontinuous; bursts of activity recorded using EEG (electroencephalography), thought to be driven by subcortical regions, display scale free properties and exhibit a complex temporal ordering known as long-range temporal correlations (LRTCs). During brain development, activity-dependent mechanisms are essential for synaptic connectivity formation, and abolishing burst activity in animal models leads to weak disorganised synaptic connectivity. Moreover, synaptic pruning shares similar mechanisms to spike-timing dependent plasticity (STDP), suggesting that the timing of activity may play a critical role in connectivity formation. We investigated, in a computational model of leaky integrate-and-fire neurones, whether the temporal ordering of burst activity within an external driving input could modulate connectivity formation in the network. Connectivity evolved across the course of simulations using an approach analogous to STDP, from networks with initial random connectivity. Small-world connectivity and hub neurones emerged in the network structure—characteristic properties of mature brain networks. Notably, driving the network with an external input which exhibited LRTCs in the temporal ordering of burst activity facilitated the emergence of these network properties, increasing the speed with which they emerged compared with when the network was driven by the same input with the bursts randomly ordered in time. Moreover, the emergence of small-world properties was dependent on the strength of the LRTCs. These results suggest that the temporal ordering of burst activity could play an important role in synaptic connectivity formation and the emergence of small-world topology in the developing brain

Deterministic networks for probabilistic computing

Neural-network models of high-level brain functions such as memory recall and reasoning often rely on the presence of stochasticity. The majority of these models assumes that each neuron in the functional network is equipped with its own private source of randomness, often in the form of uncorrelated external noise. However, both in vivo and in silico, the number of noise sources is limited due to space and bandwidth constraints. Hence, neurons in large networks usually need to share noise sources. Here, we show that the resulting shared-noise correlations can significantly impair the performance of stochastic network models. We demonstrate that this problem can be overcome by using deterministic recurrent neural networks as sources of uncorrelated noise, exploiting the decorrelating effect of inhibitory feedback. Consequently, even a single recurrent network of a few hundred neurons can serve as a natural noise source for large ensembles of functional networks, each comprising thousands of units. We successfully apply the proposed framework to a diverse set of binary-unit networks with different dimensionalities and entropies, as well as to a network reproducing handwritten digits with distinct predefined frequencies. Finally, we show that the same design transfers to functional networks of spiking neurons.Comment: 22 pages, 11 figure

The functional organization of area V2, I: Specialization across stripes and layers

We used qualitative tests to assess the sensitivity of 1043 V2 neurons (predominantly multiunits) in anesthetised macaque monkeys to direction, length. orientation. and color of moving bar stimuli. Spectral sensitivity was additionally tested by noting ON or OFF responses to flashed stimuli of varied size and color. The location of 649 units was identified with respect to cycles of cytochrome oxidase stripes (thick-inter-thin-inter) and cortical layer. We used an initial 8-way stripe classification (4 stripes. and 4 "marginal" zones at interstripes boundaries), and a 9-way layer classification (5 standard layers (2-6), and 4 "marginal" Strata at layer boundaries). These classes were collapsed differently for particular analyses of functional distribution:. the main stripe-by-layer analysis was performed on 18 compartments (3 stripes X 6 layers), We found direction sensitivity only within thick stripes, orientation sensitivity mainly in thick stripes and interstripes. and spectral sensitivity mainly in thin stripes. Positive length summation was relatively more frequent in thick stripes and interstripes. and negative length/size summation in thin stripes. All these "majority" characteristics of stripes were most prominent in layers 3A and 3B. By contrast, "minority" characteristics (e.g. spectral sensitivity in thick stripes positive size summation in thin stripes) tended to be most frequent in the outer layers, that is, layers 2 and 6. In consequence, going by the four functions tested, the distinctions between stripes were maximal in layer 3, moderate in layer 2, and minimal in layer 6. Pooling all layers, there was some indication of asymmetry in the stripe cycle, in that thin stripe characteristics (spectral sensitivity, orientation insensitivity, and negative size summation) were also evident in the marginal zone and interstripe immediately lateral to a thin stripe, but less so medially. Within thin stripes, spectral and orientation selectivities were negatively correlated this was still more accentuated amongst the minority spectrally tuned cells of thick stripes. but absent from interstripes, where these two properties were randomly assorted. Directional and spectral sensitivities were each coupled to negative size summation. but not to each other. We conclude that these functional characteristics of stripes are consistent with segregated. specialized pathways ascending through their middle layers, whilst the outer layers. 1, 2, and 6, utilize feedback from higher areas to adopt a more integrative role

Redes cerebrales en quejas subjetivas de memoria y deterioro cognitivo leve: caracterización de las etapas de pre-demencia mediante magnetoencefalografía

Tesis de la Universidad Complutense de Madrid, Facultad de Psicología, leída el 22/03/2018. Tesis formato europeo (compendio de artículos)La demencia es un cuadro que puede ser originado por múltiples causas, produciendo un deterioro cognitivo muy marcado y limitando la independencia del paciente. La causa más común de demencia es la Enfermedad de Alzheimer (EA) que representa aproximadamente el 60% de los casos totales. Aunque existen numerosos factores que parecen modular el riesgo de desarrollar EA tales como factores genéticos (APOE, PS1, etc.) o variables relacionadas con el estilo de vida (estudios, ocupación, dieta, etc.), la edad es sin duda la variable más influyente y el mayor factor de riesgo ante la aparición de la EA. Por este motivo, el número de personas mayores afectadas por esta enfermedad no ha parado de aumentar durante las últimas décadas, y se espera que aumente su incidencia aún más. Debido al fracaso generalizado de los ensayos farmacológicos, numerosos esfuerzos en investigación se centran ahora en la detección temprana de la EA. El curso de la EA es lento e insidioso, y la acumulación de neuropatología puede comenzar hasta 15 años antes de su diagnóstico. A lo largo de esta etapa preclínica los pacientes atraviesan un estadio conocido como deterioro cognitivo leve (DCL). Esta etapa se caracteriza por alteraciones en uno o varios dominios cognitivos que no genera aún graves alteraciones del funcionamiento diario. Este estadio está altamente asociado al desarrollo posterior de EA y por tanto se considera bajo determinadas condiciones una etapa prodrómica de la enfermedad. Las personas mayores con DCL suelen presentar alteraciones a nivel cerebral o metabólico característicos de la EA, tales como atrofia cortical, alteraciones sinápticas o acumulación de proteínas relacionadas con la fisiopatología de la EA. La literatura científica reciente ha descrito una etapa anterior incluso al DCL que podría asociarse al desarrollo de demencia futuro. Las quejas subjetivas de memoria (QSM) se caracterizarían por la presencia de un sentimiento subjetivo de deterioro cognitivo en ausencia de afectación objetiva, es decir, la evaluación neuropsicológica de estas personas mayores se encuentra en el rango normal. Sin embargo, el estado de la actividad cerebral en esta etapa, o su integridad estructural aún no ha sido apenas descrito. Existen resultados contradictorios con respecto a si la presencia de QSM en personas mayores se asocia a un riesgo más elevado de desarrollar demencia. Además, mientras algunos estudios reportan alteraciones a nivel cerebral compatibles con EA en esta etapa, otros no encuentran tales signos. El objetivo fundamental de esta tesis es la caracterización de las alteraciones en las redes cerebrales en personas mayores sanas, personas mayores con QSM y personas mayores con DCL. El estado actual de la literatura nos permite anticipar la presencia de alteraciones cerebrales relacionadas con EA en el grupo con DCL, sin embargo este trabajo pretende estudiar si dichas alteraciones, o formas más sutiles, se encuentran presentes en el grupo con QSM. Esto nos permitirá en primer lugar clarificar si las QSM tienen alguna relevancia clínica y si se encuentran asociadas a cambios objetivos en la actividad cerebral. Además, se podrá describir el curso exacto de las alteraciones que tienen lugar a lo largo de las etapas preclínicas en la EA gracias a la inclusión del grupo con DCL, caracterizando así en cada estudio las dos etapas que anteceden a la EA descritas a día de hoy...Dementia is a clinical entity producing major cognitive impairment that interferes with daily living activities that can be caused by a variety of conditions. Among them, Alzheimer´s Disease (AD) represents around a 60% of the total dementia cases. AD risk is modulated by multiple variables such as genotype (APOE, PS1, etc.) or lifestyle variables (studies, occupation, dietary patterns, etc.), although age is the most crucial risk factor for AD development. As a consequence, the number of AD patients has rapidly grown over the last few decades and is expected to increase even more dramatically in the near future. Given the poor results obtained in pharmacological trials to cure or slow AD progression, early AD detection is receiving increasing research efforts over the last few years. Considering the slow and insidious progression of AD, brain pathology starts accumulating in the brain as soon as 15 years before clinical symptoms are severe enough to establish an AD diagnostic. Before reaching AD dementia, patients develop mild cognitive impairment (MCI). This stage is characterized by the presence of a significant cognitive impairment affecting one or more domains. However, this cognitive decline does not significantly limit patients’ daily functioning. MCI patients are known to show increased conversion rates to AD with respect to healthy elders and thus this stage is commonly accepted as a prodromal stage of AD according to recent MCI criteria. MCI patients are known to exhibit AD-like brain and metabolic alterations such as cortical atrophy or AD-related protein accumulation. Recent scientific literature has described a stage preceding MCI which could be associated with future dementia development. Subjective cognitive decline is defined by the presence of a subjective feeling of cognitive worsening in the absence of objective impairment in classical neuropsychological assessment. However, the integrity of brain activity or structure has been scarcely described yet. Furthermore, there exist some contradictory results regarding whether the presence of cognitive concerns is truly related to increased dementia risk. In the same vein, some studies have found brain alterations in SCD patients resembling of those associated with AD while others failed to find such signs. The main objective of this thesis is characterizing brain network alterations in healthy elders, elders with SCD and elders with MCI. The current state-of-the-art lets us anticipate the presence of brain disruption in the MCI group, nonetheless, this work aims to provide evidence of whether similar alterations are already present in the SCD stage. The results presented in this thesis will clarify the clinical relevance of SCD by discerning whether cognitive concerns are truly mediated by network disruption or not. Moreover, the exact course and development of electrophysiological brain alterations during the preclinical stages of the disease will be described by including also MCI patients. By including these three groups we will be able to characterize brain function in the different AD preclinical stages considered in current literature...Fac. de PsicologíaTRUEunpu

Altered dynamical integration/segregation balance during anesthesia-induced loss of consciousness

In recent years, brain imaging studies have begun to shed light on the neural correlates of physiologically-reversible altered states of consciousness such as deep sleep, anesthesia, and psychedelic experiences. The emerging consensus is that normal waking consciousness requires the exploration of a dynamical repertoire enabling both global integration i.e., long-distance interactions between brain regions, and segregation, i.e., local processing in functionally specialized clusters. Altered states of consciousness have notably been characterized by a tipping of the integration/segregation balance away from this equilibrium. Historically, functional MRI (fMRI) has been the modality of choice for such investigations. However, fMRI does not enable characterization of the integration/segregation balance at sub-second temporal resolution. Here, we investigated global brain spatiotemporal patterns in electrocorticography (ECoG) data of a monkey (Macaca fuscata) under either ketamine or propofol general anesthesia. We first studied the effects of these anesthetics from the perspective of band-specific synchronization across the entire ECoG array, treating individual channels as oscillators. We further aimed to determine whether synchrony within spatially localized clusters of oscillators was differently affected by the drugs in comparison to synchronization over spatially distributed subsets of ECoG channels, thereby quantifying changes in integration/segregation balance on physiologically-relevant time scales. The findings reflect global brain dynamics characterized by a loss of long-range integration in multiple frequency bands under both ketamine and propofol anesthesia, most pronounced in the beta (13–30 Hz) and low-gamma bands (30–80 Hz), and with strongly preserved local synchrony in all bands