Anatomical connectivity in children with developmental dyscalculia: A graph theory study.

Current theories postulate that numerical processing depends upon a brain circuit formed by regions and their connections; specialized in the representation and manipulation of the numerical properties of stimuli. It has been suggested that the damage of these network may cause Developmental Dyscalculia (DD): a persistent neurodevelopmental disorder that significantly interferes with academic performance and daily life activities that require mastery of mathematical notions and operations. However, most of the studies on the brain foundations of DD have focused on regions of interest associated with numerical processing, and have not addressed numerical cognition as a complex network phenomenon. The present study explored DD using a Graph Theory network approach. We studied the association between topological measures of integration and segregation of information processing in the brain proposed by Graph Theory; and individual variability in numerical performance in a group of 11 school-aged children with DD (5 of which presented with comorbidity with Developmental Dyslexia, the specific learning disorder for reading) and 17 typically developing peers. A statistically significant correlation was found between the Weber fraction (a measure of numerical representations' precision) and the Clustering Index (a measure of segregation of information processing) in the whole sample. The DD group showed significantly lower Characteristic Path Length (average shortest path length among all pairs of regions in the brain network) compared to controls. Also, differences in critical regions for the brain network performance (hubs) were found between groups. The presence of limbic hubs characterized the DD brain network while right Temporal and Frontal hubs found in controls were absent in the DD group. Our results suggest that the DD may be associated with alterations in anatomical brain connectivity that hinder the capacity to integrate and segregate numerical information.

Brain cortical alterations in COVID-19 patients with neurological symptoms.

Background: Growing evidence suggests that the central nervous system is affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), since infected patients suffer from acute and long-term neurological sequelae. Nevertheless, it is currently unknown whether the virus affects the brain cortex. The purpose of this study was to assess the cortical gray matter volume, the cortical thickness, and the cortical surface area in a group of SARS-CoV-2 infected patients with neurological symptoms compared to healthy control subjects. Additionally, we analyzed the cortical features and the association with inflammatory biomarkers in the cerebrospinal fluid (CSF) and plasma. Materials and methods: Thirty-three patients were selected from a prospective cross-sectional study cohort during the ongoing pandemic (August 2020-April 2021) at the university hospitals of Basel and Zurich (Switzerland). The group included patients with different neurological symptom severity (Class I: nearly asymptomatic/mild symptoms, II: moderate symptoms, III: severe symptoms). Thirty-three healthy age and sex-matched subjects that underwent the same MRI protocol served as controls. For each anatomical T1w MPRAGE image, regional cortical gray matter volume, thickness, and surface area were computed with FreeSurfer. Using a linear regression model, cortical measures were compared between groups (patients vs. controls; Class I vs. II-III), with age, sex, MRI magnetic field strength, and total intracranial volume/mean thickness/total surface area as covariates. In a subgroup of patients, the association between cortical features and clinical parameters was assessed using partial correlation adjusting for the same covariates. P-values were corrected using a false discovery rate (FDR). Results: Our findings revealed a lower cortical volume in COVID-19 patients' orbitofrontal, frontal, and cingulate regions than in controls (p < 0.05). Regional gray matter volume and thickness decreases were negatively associated with CSF total protein levels, the CSF/blood-albumin ratio, and CSF EN-RAGE levels. Conclusion: Our data suggest that viral-triggered inflammation leads to neurotoxic damage in some cortical areas during the acute phase of a COVID-19 infection in patients with neurological symptoms.

Severe Neuro-COVID is associated with peripheral immune signatures, autoimmunity and neurodegeneration: a prospective cross-sectional study.

Growing evidence links COVID-19 with acute and long-term neurological dysfunction. However, the pathophysiological mechanisms resulting in central nervous system involvement remain unclear, posing both diagnostic and therapeutic challenges. Here we show outcomes of a cross-sectional clinical study (NCT04472013) including clinical and imaging data and corresponding multidimensional characterization of immune mediators in the cerebrospinal fluid (CSF) and plasma of patients belonging to different Neuro-COVID severity classes. The most prominent signs of severe Neuro-COVID are blood-brain barrier (BBB) impairment, elevated microglia activation markers and a polyclonal B cell response targeting self-antigens and non-self-antigens. COVID-19 patients show decreased regional brain volumes associating with specific CSF parameters, however, COVID-19 patients characterized by plasma cytokine storm are presenting with a non-inflammatory CSF profile. Post-acute COVID-19 syndrome strongly associates with a distinctive set of CSF and plasma mediators. Collectively, we identify several potentially actionable targets to prevent or intervene with the neurological consequences of SARS-CoV-2 infection.

Apolipoprotein E allele 4 effects on Single-Subject Gray Matter Networks in Mild Cognitive Impairment.

There is evidence that gray matter networks are disrupted in Mild Cognitive Impairment (MCI) and associated with cognitive impairment and faster disease progression. However, it remains unknown how these alterations are related to the presence of Apolipoprotein E isoform E4 (ApoE4), the most prominent genetic risk factor for late-onset Alzheimer's disease (AD). To investigate this topic at the individual level, we explore the impact of ApoE4 and the disease progression on the Single-Subject Gray Matter Networks (SSGMNets) using the graph theory approach. Our data sample comprised 200 MCI patients selected from the ADNI database, classified as non-Converters and Converters (will progress into AD). Each group included 50 ApoE4-positive ('Carriers', ApoE4 + ) and 50 ApoE4-negative ('non-Carriers', ApoE4-). The SSGMNets were estimated from structural MRIs at two-time points: baseline and conversion. We investigated whether altered network topological measures at baseline and their rate of change (RoC) between baseline and conversion time points were associated with ApoE4 and disease progression. We also explored the correlation of SSGMNets attributes with general cognition score (MMSE), memory (ADNI-MEM), and CSF-derived biomarkers of AD (Aß42, T-tau, and P-tau). Our results showed that ApoE4 and the disease progression modulated the global topological network properties independently but not in their RoC. MCI converters showed a lower clustering index in several regions associated with neurodegeneration in AD. The SSGMNets' topological organization was revealed to be able to predict cognitive and memory measures. The findings presented here suggest that SSGMNets could indeed be used to identify MCI ApoE4 Carriers with a high risk for AD progression.

Apolipoprotein E4 effects on topological brain network organization in mild cognitive impairment.

The Apolipoprotein E isoform E4 (ApoE4) is consistently associated with an elevated risk of developing late-onset Alzheimer's Disease (AD); however, less is known about the potential genetic modulation of the brain networks organization during prodromal stages like Mild Cognitive Impairment (MCI). To investigate this issue during this critical stage, we used a dataset with a cross-sectional sample of 253 MCI patients divided into ApoE4-positive (?Carriers') and ApoE4-negative ('non-Carriers'). We estimated the cortical thickness (CT) from high-resolution T1-weighted structural magnetic images to calculate the correlation among anatomical regions across subjects and build the CT covariance networks (CT-Nets). The topological properties of CT-Nets were described through the graph theory approach. Specifically, our results showed a significant decrease in characteristic path length, clustering-index, local efficiency, global connectivity, modularity, and increased global efficiency for Carriers compared to non-Carriers. Overall, we found that ApoE4 in MCI shaped the topological organization of CT-Nets. Our results suggest that in the MCI stage, the ApoE4 disrupting the CT correlation between regions may be due to adaptive mechanisms to sustain the information transmission across distant brain regions to maintain the cognitive and behavioral abilities before the occurrence of the most severe symptoms.

Networks of myelin covariance.

Networks of anatomical covariance have been widely used to study connectivity patterns in both normal and pathological brains based on the concurrent changes of morphometric measures (i.e., cortical thickness) between brain structures across subjects (Evans, ). However, the existence of networks of microstructural changes within brain tissue has been largely unexplored so far. In this article, we studied in vivo the concurrent myelination processes among brain anatomical structures that gathered together emerge to form nonrandom networks. We name these "networks of myelin covariance" (Myelin-Nets). The Myelin-Nets were built from quantitative Magnetization Transfer data-an in-vivo magnetic resonance imaging (MRI) marker of myelin content. The synchronicity of the variations in myelin content between anatomical regions was measured by computing the Pearson's correlation coefficient. We were especially interested in elucidating the effect of age on the topological organization of the Myelin-Nets. We therefore selected two age groups: Young-Age (20-31 years old) and Old-Age (60-71 years old) and a pool of participants from 48 to 87 years old for a Myelin-Nets aging trajectory study. We found that the topological organization of the Myelin-Nets is strongly shaped by aging processes. The global myelin correlation strength, between homologous regions and locally in different brain lobes, showed a significant dependence on age. Interestingly, we also showed that the aging process modulates the resilience of the Myelin-Nets to damage of principal network structures. In summary, this work sheds light on the organizational principles driving myelination and myelin degeneration in brain gray matter and how such patterns are modulated by aging.

Subtle alterations in cerebrovascular reactivity in mild cognitive impairment detected by graph theoretical analysis and not by the standard approach.

There is growing support that cerebrovascular reactivity (CVR) in response to a vasodilatory challenge, also defined as the cerebrovascular reserve, is reduced in Alzheimer's disease dementia. However, this is less clear in patients with mild cognitive impairment (MCI). The current standard analysis may not reflect subtle abnormalities in CVR. In this study, we aimed to investigate vasodilatory-induced changes in the topology of the cerebral blood flow correlation (CBFcorr) network to study possible network-related CVR abnormalities in MCI. For this purpose, four CBFcorr networks were constructed: two using CBF SPECT data at baseline and under the vasodilatory challenge of acetazolamide (ACZ), obtained from a group of 26 MCI patients; and two equivalent networks from a group of 26 matched cognitively normal controls. The mean strength of association (SA) and clustering coefficient (C) were used to evaluate ACZ-induced changes on the topology of CBFcorr networks. We found that cognitively normal adults and MCI patients show different patterns of C and SA changes. The observed differences included the medial prefrontal cortices and inferior parietal lobe, which represent areas involved in MCI's cognitive dysfunction. In contrast, no substantial differences were detected by standard CVR analysis. These results suggest that graph theoretical analysis of ACZ-induced changes in the topology of the CBFcorr networks allows the identification of subtle network-related CVR alterations in MCI, which couldn't be detected by the standard approach.

Functional Connectivity and Quantitative EEG in Women with Alcohol Use Disorders: A Resting-State Study.

This study was aimed at exploring the electroencephalographic features associated with alcohol use disorders (AUD) during a resting-state condition, by using quantitative EEG and Functional Connectivity analyses. In addition, we explored whether EEG functional connectivity is associated with trait impulsivity. Absolute and relative powers and Synchronization Likelihood (SL) as a measure of functional connectivity were analyzed in 15 AUD women and fifteen controls matched in age, gender and education. Correlation analysis between self-report impulsivity as measured by the Barratt impulsiveness Scale (BIS-11) and SL values of AUD patients were performed. Our results showed increased absolute and relative beta power in AUD patients compared to matched controls, and reduced functional connectivity in AUD patients predominantly in the beta and alpha bands. Impaired connectivity was distributed at fronto-central and occipito-parietal regions in the alpha band, and over the entire scalp in the beta band. We also found that impaired functional connectivity particularly in alpha band at fronto-central areas was negative correlated with non-planning dimension of impulsivity. These findings suggest that functional brain abnormalities are present in AUD patients and a disruption of resting-state EEG functional connectivity is associated with psychopathological traits of addictive behavior.

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.

Studying the topological organization of the cerebral blood flow fluctuations in resting state.

In this paper the cerebral blood flow (CBF) in resting state obtained from SPECT imaging is employed as a hemodynamics descriptor to study the concurrent changes between brain structures and to build binarized connectivity graphs. The statistical similarity in CBF between pairs of regions was measured by computing the Pearson correlation coefficient across 31 normal subjects. We demonstrated the CBF connectivity matrices follow 'small-world' attributes similar to previous studies using different modalities of neuroimaging data (MRI, fMRI, EEG, MEG). The highest concurrent fluctuations in CBF were detected between homologous cortical regions (homologous callosal connections). It was found that the existence of structural core regions or hubs positioned on a high proportion of shortest paths within the CBF network. These were anatomically distributed in frontal, limbic, occipital and parietal regions that suggest its important role in functional integration. Our findings point to a new possibility of using CBF variable to investigate the brain networks based on graph theory in normal and pathological states. Likewise, it opens a window to future studies to link covariation between morphometric descriptors, axonal connectivity and CBF processes with a potential diagnosis applications.

Surface area and cortical thickness descriptors reveal different attributes of the structural human brain networks.

Recently, a related morphometry-based connection concept has been introduced using local mean cortical thickness and volume to study the underlying complex architecture of the brain networks. In this article, the surface area is employed as a morphometric descriptor to study the concurrent changes between brain structures and to build binarized connectivity graphs. The statistical similarity in surface area between pair of regions was measured by computing the partial correlation coefficient across 186 normal subjects of the Cuban Human Brain Mapping Project. We demonstrated that connectivity matrices obtained follow a small-world behavior for two different parcellations of the brain gray matter. The properties of the connectivity matrices were compared to the matrices obtained using the mean cortical thickness for the same cortical parcellations. The topology of the cortical thickness and surface area networks were statistically different, demonstrating that both capture distinct properties of the interaction or different aspects of the same interaction (mechanical, anatomical, chemical, etc.) between brain structures. This finding could be explained by the fact that each descriptor is driven by distinct cellular mechanisms as result of a distinct genetic origin. To our knowledge, this is the first time that surface area is used to study the morphological connectivity of brain networks.

Changes in reading strategies in school-age children.

Learning to read is one of the most important cognitive milestones in the human social environment. One of the most accepted models explaining such process is the Double-Route Cascaded Model. It suggests the existence of two reading strategies: lexical and sublexical. In the Spanish language there are some contradictions about how these strategies are applied for reading. In addition, there are only a few studies dealing with the analysis of shifts between them, achieving a fluent reading process. In this paper we use a reading task including words and pseudowords for characterizing the cost of shifting between reading strategies in children with developmental dyslexia and normal controls. Our results suggest the presence of both strategies in these two experimental groups. In controls, both strategies become more efficient in correspondence to the increased exposition to written material. However, in children with developmental dyslexia only the lexical strategy exhibits such improvement. Their also point to a low cost for shifting between strategies in controls and a much more significant one in children with developmental dyslexia, differentiating subgroups with distinct shifting patterns.

Changes in Reading Strategies in School-Age Children

Learning to read is one of the most important cognitive milestones in the human social environment.One of the most accepted models explaining such process is the Double-Route Cascaded Model. It suggests the existence of two reading strategies: lexical and sublexical. In the Spanish language there are some contradictions about how these strategies are applied for reading. In addition, there are only a few studies dealing with the analysis of shifts between them, achieving a fluent reading process. In this paper we use a reading task including words and pseudowords for characterizing the cost of shifting between reading strategies in children with developmental dyslexia and normal controls. Our results suggest the presence of both strategies in these two experimental groups. In controls, both strategies become more efficient in correspondence to the increased exposition to written material. However, in children with developmental dyslexia only the lexical strategy exhibits such improvement. Their also point to a low cost for shifting between strategies in controls and a much more significant one in children with developmental dyslexia, differentiating subgroups with distinct shifting patterns (AU)

El aprendizaje de la lectura constituye uno de los hitos cognitivos más importantes del entorno social humano. Uno de los modelos de lectura más aceptados ha sido el Modelo de Doble Ruta en Cascada que sugiere la existencia de dos estrategias de lectura: lexical y sublexical. En el idioma español existen datos contradictorios acerca de cómo se aplican estas estrategias y no hay estudios que describan cómo se realizan los cambios de una a otra para lograr una lectura fluida. En este trabajo utilizamos una tarea de lectura de palabras y pseudopalabras para caracterizar el costo de cambio de una a otra estrategia en niños buenos lectores y niños con dislexia del desarrollo. Nuestros resultados sugieren la presencia de ambas estrategias en los dos grupos. En los niños buenos lectores ambas estrategias se hacen más eficientes con el grado de exposición a la lectura. Sin embargo, en los niños disléxicos esto solo ocurre en la estrategia lexical. Además, indican que los niños buenos lectores desarrollan un bajo costo en el cambio de estrategia de lectura mientras que un subgrupo de niños disléxicos presenta un costo mayor, conformándose subgrupos con patrones diferentes de afectación selectiva (AU)