Enumeration of extracellular vesicles has clinical potential as a biomarker for disease. In biological samples, the smallest and largest vesicles typically differ 25-fold in size, 300,000-fold in ...concentration, 20,000-fold in volume, and 10,000,000-fold in scattered light. Because of this heterogeneity, the currently employed techniques detect concentrations ranging from 10(4) to 10(12) vesicles mL(-1) .
To investigate whether the large variation in the detected concentration of vesicles is caused by the minimum detectable vesicle size of five widely used techniques.
The size and concentration of vesicles and reference beads were measured with transmission electron microscopy (TEM), a conventional flow cytometer, a flow cytometer dedicated to detecting submicrometer particles, nanoparticle tracking analysis (NTA), and resistive pulse sensing (RPS).
Each technique gave a different size distribution and a different concentration for the same vesicle sample.
Differences between the detected vesicle concentrations are primarily caused by differences between the minimum detectable vesicle sizes. The minimum detectable vesicle sizes were 70-90 nm for NTA, 70-100 nm for RPS, 150-190 nm for dedicated flow cytometry, and 270-600 nm for conventional flow cytometry. TEM could detect the smallest vesicles present, albeit after adhesion on a surface. Dedicated flow cytometry was most accurate in determining the size of reference beads, but is expected to be less accurate on vesicles, owing to heterogeneity of the refractive index of vesicles. Nevertheless, dedicated flow cytometry is relatively fast and allows multiplex fluorescence detection, making it most applicable to clinical research.
Cell-derived or extracellular vesicles, including microparticles and exosomes, are abundantly present in body fluids such as blood. Although such vesicles have gained strong clinical and scientific ...interest, their detection is difficult because many vesicles are extremely small with a diameter of less than 100 nm, and, moreover, these vesicles have a low refractive index and are heterogeneous in both size and composition. In this review, we focus on the relatively high throughput detection of vesicles in suspension by flow cytometry, resistive pulse sensing, and nanoparticle tracking analysis, and we will discuss their applicability and limitations. Finally, we discuss four methods that are not commercially available: Raman microspectroscopy, micro nuclear magnetic resonance, small-angle X-ray scattering (SAXS), and anomalous SAXS. These methods are currently being explored to study vesicles and are likely to offer novel information for future developments.
Abstract Our brain is a network. It consists of spatially distributed, but functionally linked regions that continuously share information with each other. Interestingly, recent advances in the ...acquisition and analysis of functional neuroimaging data have catalyzed the exploration of functional connectivity in the human brain. Functional connectivity is defined as the temporal dependency of neuronal activation patterns of anatomically separated brain regions and in the past years an increasing body of neuroimaging studies has started to explore functional connectivity by measuring the level of co-activation of resting-state fMRI time-series between brain regions. These studies have revealed interesting new findings about the functional connections of specific brain regions and local networks, as well as important new insights in the overall organization of functional communication in the brain network. Here we present an overview of these new methods and discuss how they have led to new insights in core aspects of the human brain, providing an overview of these novel imaging techniques and their implication to neuroscience. We discuss the use of spontaneous resting-state fMRI in determining functional connectivity, discuss suggested origins of these signals, how functional connections tend to be related to structural connections in the brain network and how functional brain communication may form a key role in cognitive performance. Furthermore, we will discuss the upcoming field of examining functional connectivity patterns using graph theory, focusing on the overall organization of the functional brain network. Specifically, we will discuss the value of these new functional connectivity tools in examining believed connectivity diseases, like Alzheimer's disease, dementia, schizophrenia and multiple sclerosis.
Despite the multitude of longitudinal neuroimaging studies that have been published, a basic question on the progressive brain loss in schizophrenia remains unaddressed: Does it reflect accelerated ...aging of the brain, or is it caused by a fundamentally different process? The authors used support vector regression, a supervised machine learning technique, to address this question.
In a longitudinal sample of 341 schizophrenia patients and 386 healthy subjects with one or more structural MRI scans (1,197 in total), machine learning algorithms were used to build models to predict the age of the brain and the presence of schizophrenia ("schizophrenia score"), based on the gray matter density maps. Age at baseline ranged from 16 to 67 years, and follow-up scans were acquired between 1 and 13 years after the baseline scan. Differences between brain age and chronological age ("brain age gap") and between schizophrenia score and healthy reference score ("schizophrenia gap") were calculated. Accelerated brain aging was calculated from changes in brain age gap between two consecutive measurements. The age prediction model was validated in an independent sample.
In schizophrenia patients, brain age was significantly greater than chronological age at baseline (+3.36 years) and progressively increased during follow-up (+1.24 years in addition to the baseline gap). The acceleration of brain aging was not constant: it decreased from 2.5 years/year just after illness onset to about the normal rate (1 year/year) approximately 5 years after illness onset. The schizophrenia gap also increased during follow-up, but more pronounced variability in brain abnormalities at follow-up rendered this increase nonsignificant.
The progressive brain loss in schizophrenia appears to reflect two different processes: one relatively homogeneous, reflecting accelerated aging of the brain and related to various measures of outcome, and a more variable one, possibly reflecting individual variation and medication use. Differentiating between these two processes may not only elucidate the various factors influencing brain loss in schizophrenia, but also assist in individualizing treatment.
Our brain is a complex network in which information is continuously processed and transported between spatially distributed but functionally linked regions. Recent studies have shown that the ...functional connections of the brain network are organized in a highly efficient small-world manner, indicating a high level of local neighborhood clustering, together with the existence of more long-distance connections that ensure a high level of global communication efficiency within the overall network. Such an efficient network architecture of our functional brain raises the question of a possible association between how efficiently the regions of our brain are functionally connected and our level of intelligence. Examining the overall organization of the brain network using graph analysis, we show a strong negative association between the normalized characteristic path length lambda of the resting-state brain network and intelligence quotient (IQ). This suggests that human intellectual performance is likely to be related to how efficiently our brain integrates information between multiple brain regions. Most pronounced effects between normalized path length and IQ were found in frontal and parietal regions. Our findings indicate a strong positive association between the global efficiency of functional brain networks and intellectual performance.
The human brain forms a large-scale structural network of regions and interregional pathways. Recent studies have reported the existence of a selective set of highly central and interconnected hub ...regions that may play a crucial role in the brain's integrative processes, together forming a central backbone for global brain communication. Abnormal brain connectivity may have a key role in the pathophysiology of schizophrenia.
To examine the structure of the rich club in schizophrenia and its role in global functional brain dynamics.
Structural diffusion tensor imaging and resting-state functional magnetic resonance imaging were performed in patients with schizophrenia and matched healthy controls.
Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, the Netherlands.
Forty-eight patients and 45 healthy controls participated in the study. An independent replication data set of 41 patients and 51 healthy controls was included to replicate and validate significant findings. MAIN OUTCOME(S) AND MEASURES: Measures of rich club organization, connectivity density of rich club connections and connections linking peripheral regions to brain hubs, measures of global brain network efficiency, and measures of coupling between brain structure and functional dynamics.
Rich club organization between high-degree hub nodes was significantly affected in patients, together with a reduced density of rich club connections predominantly comprising the white matter pathways that link the midline frontal, parietal, and insular hub regions. This reduction in rich club density was found to be associated with lower levels of global communication capacity, a relationship that was absent for other white matter pathways. In addition, patients had an increase in the strength of structural connectivity-functional connectivity coupling.
Our findings provide novel biological evidence that schizophrenia is characterized by a selective disruption of brain connectivity among central hub regions of the brain, potentially leading to reduced communication capacity and altered functional brain dynamics.
Although structural brain alterations in schizophrenia have been demonstrated extensively, their quantitative distribution has not been studied over the last 14 years despite advances in ...neuroimaging. Moreover, a volumetric meta-analysis has not been conducted in antipsychotic-naive patients. Therefore, meta-analysis on cross-sectional volumetric brain alterations in both medicated and antipsychotic-naive patients was conducted. Three hundred seventeen studies published from September 1, 1998 to January 1, 2012 comprising over 9000 patients were selected for meta-analysis, including 33 studies in antipsychotic-naive patients. In addition to effect sizes, potential modifying factors such as duration of illness, sex composition, current antipsychotic dose, and intelligence quotient matching status of participants were extracted where available. In the sample of medicated schizophrenia patients (n = 8327), intracranial and total brain volume was significantly decreased by 2.0% (effect size d = -0.17) and 2.6% (d = -0.30), respectively. Largest effect sizes were observed for gray matter structures, with effect sizes ranging from -0.22 to -0.58. In the sample of antipsychotic-naive patients (n = 771), volume reductions in caudate nucleus (d = -0.38) and thalamus (d = -0.68) were more pronounced than in medicated patients. White matter volume was decreased to a similar extent in both groups, while gray matter loss was less extensive in antipsychotic-naive patients. Gray matter reduction was associated with longer duration of illness and higher dose of antipsychotic medication at time of scanning. Therefore, brain loss in schizophrenia is related to a combination of (early) neurodevelopmental processes-reflected in intracranial volume reduction-as well as illness progression.
Summary Network-based analysis of structural and functional connections has provided a new technique to study the brains of healthy people and patients with neurological and psychiatric disorders. ...Graph theory provides a powerful method to quantitatively describe the topological organisation of brain connectivity. With such a framework, the brain can be depicted as a set of nodes connected by edges. Distinct modifications of network topological organisation in the brain have been identified during development and normal ageing, whereas disrupted functional and structural connectivities have been associated with several neurological and psychiatric disorders, including dementia, amyotrophic lateral sclerosis, multiple sclerosis, and schizophrenia. These assessments have improved understanding of the clinical manifestations noted in these patients, including disability and cognitive impairment. Future network-based research might enable indentification of different stages of disorders, subtypes for cognitive impairment, and connectivity profiles associated with different clinical outcomes.
The brain is a complex dynamic system of functionally connected regions. Graph theory has been successfully used to describe the organization of such dynamic systems. Recent resting-state fMRI ...studies have suggested that inter-regional functional connectivity shows a small-world topology, indicating an organization of the brain in highly clustered sub-networks, combined with a high level of global connectivity. In addition, a few studies have investigated a possible scale-free topology of the human brain, but the results of these studies have been inconclusive. These studies have mainly focused on inter-regional connectivity, representing the brain as a network of brain regions, requiring an arbitrary definition of such regions. However, using a voxel-wise approach allows for the model-free examination of both inter-regional as well as intra-regional connectivity and might reveal new information on network organization. Especially, a voxel-based study could give information about a possible scale-free organization of functional connectivity in the human brain. Resting-state 3 Tesla fMRI recordings of 28 healthy subjects were acquired and individual connectivity graphs were formed out of all cortical and sub-cortical voxels with connections reflecting inter-voxel functional connectivity. Graph characteristics from these connectivity networks were computed. The clustering-coefficient of these networks turned out to be much higher than the clustering-coefficient of comparable random graphs, together with a short average path length, indicating a small-world organization. Furthermore, the connectivity distribution of the number of inter-voxel connections followed a power-law scaling with an exponent close to 2, suggesting a scale-free network topology. Our findings suggest a combined small-world and scale-free organization of the functionally connected human brain. The results are interpreted as evidence for a highly efficient organization of the functionally connected brain, in which voxels are mostly connected with their direct neighbors forming clustered sub-networks, which are held together by a small number of highly connected hub-voxels that ensure a high level of overall connectivity.
Changes in cortical thickness over time have been related to intelligence, but whether changes in cortical surface area are related to general cognitive functioning is unknown. We therefore examined ...the relationship between intelligence quotient (IQ) and changes in cortical thickness and surface over time in 504 healthy subjects. At 10 years of age, more intelligent children have a slightly thinner cortex than children with a lower IQ. This relationship becomes more pronounced with increasing age: with higher IQ, a faster thinning of the cortex is found over time. In the more intelligent young adults, this relationship reverses so that by the age of 42 a thicker cortex is associated with higher intelligence. In contrast, cortical surface is larger in more intelligent children at the age of 10. The cortical surface is still expanding, reaching its maximum area during adolescence. With higher IQ, cortical expansion is completed at a younger age; and once completed, surface area decreases at a higher rate. These findings suggest that intelligence may be more related to the magnitude and timing of changes in brain structure during development than to brain structure per se, and that the cortex is never completed but shows continuing intelligence-dependent development.
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