Hilleke E. Hulshoff Pol

Exploring the brain network: A review on resting-state fMRI functional connectivity

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 Alzheimers disease, dementia, schizophrenia and multiple sclerosis.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 Alzheimers disease, dementia, schizophrenia and multiple sclerosis.

Functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain.

During rest, multiple cortical brain regions are functionally linked forming resting‐state networks. This high level of functional connectivity within resting‐state networks suggests the existence of direct neuroanatomical connections between these functionally linked brain regions to facilitate the ongoing interregional neuronal communication. White matter tracts are the structural highways of our brain, enabling information to travel quickly from one brain region to another region. In this study, we examined both the functional and structural connections of the human brain in a group of 26 healthy subjects, combining 3 Tesla resting‐state functional magnetic resonance imaging time‐series with diffusion tensor imaging scans. Nine consistently found functionally linked resting‐state networks were retrieved from the resting‐state data. The diffusion tensor imaging scans were used to reconstruct the white matter pathways between the functionally linked brain areas of these resting‐state networks. Our results show that well‐known anatomical white matter tracts interconnect at least eight of the nine commonly found resting‐state networks, including the default mode network, the core network, primary motor and visual network, and two lateralized parietal‐frontal networks. Our results suggest that the functionally linked resting‐state networks reflect the underlying structural connectivity architecture of the human brain. Hum Brain Mapp 2009.

Efficiency of Functional Brain Networks and Intellectual Performance

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 λ 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.

Brain volume abnormalities in major depressive disorder: A meta-analysis of magnetic resonance imaging studies

Objective. So far, there have been no attempts to integrate the growing number of all brain volumetric magnetic resonance imaging studies in depression. In this comprehensive meta‐analysis the magnitude and extent of brain volume differences between 2,418 patients with major depressive disorder and 1,974 healthy individuals from 64 studies was determined. Methods. A systematic research was conducted for volumetric magnetic resonance imaging studies of patients with major depressive disorder in relation to healthy control subjects. Studies had to report sufficient data for computation of effect sizes. For each study, the Cohens d was calculated. All analyses were performed using the random effects model. Additionally, meta‐regression analyses were done to explore the effects of potential sources of heterogeneity. Results. Patients showed large volume reductions in frontal regions, especially in the anterior cingulate and orbitofrontal cortex with smaller reductions in the prefrontal cortex. The hippocampus, the putamen and caudate nucleus showed moderate volume reductions. Conclusions. This is the first comprehensive meta‐analysis in major depressive disorder demonstrating structural brain abnormalities, particularly in those brain areas that are involved in emotion processing and stress‐regulation. Hum Brain Mapp, 2009.

Aberrant Frontal and Temporal Complex Network Structure in Schizophrenia: A Graph Theoretical Analysis

Brain regions are not independent. They are interconnected by white matter tracts, together forming one integrative complex network. The topology of this network is crucial for efficient information integration between brain regions. Here, we demonstrate that schizophrenia involves an aberrant topology of the structural infrastructure of the brain network. Using graph theoretical analysis, complex structural brain networks of 40 schizophrenia patients and 40 human healthy controls were examined. Diffusion tensor imaging was used to reconstruct the white matter connections of the brain network, with the strength of the connections defined as the level of myelination of the tracts as measured through means of magnetization transfer ratio magnetic resonance imaging. Patients displayed a preserved overall small-world network organization, but focusing on specific brain regions and their capacity to communicate with other regions of the brain revealed significantly longer node-specific path lengths (higher L) of frontal and temporal regions, especially of bilateral inferior/superior frontal cortex and temporal pole regions. These findings suggest that schizophrenia impacts global network connectivity of frontal and temporal brain regions. Furthermore, frontal hubs of patients showed a significant reduction of betweenness centrality, suggesting a less central hub role of these regions in the overall network structure. Together, our findings suggest that schizophrenia patients have a less strongly globally integrated structural brain network with a reduced central role for key frontal hubs, resulting in a limited structural capacity to integrate information across brain regions.

Anatomical MRI of the developing human brain: what have we learned?

OBJECTIVE To critically review and integrate the existing literature on magnetic resonance imaging (MRI) studies of the normally developing brain in childhood and adolescence and discuss the implications for clinical MRI studies. METHOD Changes in regional brain volume with age and differences between the sexes are summarized from reports in refereed journal articles pertaining to MRI of the developing human brain. RESULTS White matter volume increases with age. Gray matter volumes increase during childhood and then decrease before adulthood. On average, boys have larger brains than girls; after correction for overall brain volume the caudate is relatively larger in girls, and the amygdala is relatively larger in boys. Differences are of clinical interest given gender-related differences in the age of onset, symptomatology, and prevalence noted for nearly all childhood-onset psychiatric disorders. Attention-deficit/hyperactivity disorder is frequently used as an example to demonstrate these points. CONCLUSIONS Understanding the developmental trajectories of normal brain development and differences between the sexes is important for the interpretation of clinical imaging studies.

Brain Volumes in Schizophrenia: A Meta-Analysis in Over 18 000 Subjects

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.

Genetic influences on human brain structure: A review of brain Imaging studies in twins

Twin studies suggest that variation in human brain volume is genetically influenced. The genes involved in human brain volume variation are still largely unknown, but several candidate genes have been suggested. An overview of structural Magnetic Resonance (brain) Imaging studies in twins is presented, which focuses on the influence of genetic factors on variation in healthy human brain volume. Twin studies have shown that genetic effects varied regionally within the brain, with high heritabilities of frontal lobe volumes (90–95%), moderate estimates in the hippocampus (40–69%), and environmental factors influencing several medial brain areas. High heritability estimates of brain structures were revealed for regional amounts of gray matter (density) in medial frontal cortex, Heschls gyrus, and postcentral gyrus. In addition, moderate to high heritabilities for densities of Brocas area, anterior cingulate, hippocampus, amygdala, gray matter of the parahippocampal gyrus, and white matter of the superior occipitofrontal fasciculus were reported. The high heritability for (global) brain volumes, including the intracranium, total brain, cerebral gray, and white matter, seems to be present throughout life. Estimates of genetic and environmental influences on age‐related changes in brain structure in children and adults await further longitudinal twin‐studies. For prefrontal cortex volume, white matter, and hippocampus volumes, a number of candidate genes have been identified, whereas for other brain areas, only a few or even a single candidate gene has been found so far. New techniques such as genome‐wide scans may become helpful in the search for genes that are involved in the regulation of human brain volume throughout life. Hum Brain Mapp, 2007.

Magnetic resonance imaging of boys with attention-deficit/hyperactivity disorder and their unaffected siblings

OBJECTIVE To study the influence of increased familial risk for attention-deficit/hyperactivity disorder (ADHD) on brain morphology. METHOD Volumetric cerebral measures based on whole brain magnetic resonance imaging scans from 30 boys with ADHD, 30 of their unaffected siblings, and 30 matched controls were compared. RESULTS Both subjects with ADHD and their unaffected siblings displayed reductions in right prefrontal gray matter and left occipital gray and white matter of up to 9.1% (p < 0.05). Right cerebellar volume was reduced by 4.9% in subjects with ADHD (p = 0.026) but not in their unaffected siblings (p = 0.308). A 4.0% reduction in intracranial volume was found in subjects with ADHD (p = 0.031), while a trend was observed in their unaffected siblings (p = 0.068). CONCLUSIONS The volumetric reductions in cortical gray and white matter in subjects with ADHD are also present in their unaffected siblings, suggesting that they are related to an increased familial risk for the disorder. In contrast, the cerebellum is unaffected in siblings, suggesting that the reduction in volume observed in subjects with ADHD may be more directly related to the pathophysiology of this disorder.

The association between brain volume and intelligence is of genetic origin

83 TO THE EDITOR—The recent study by Thompson and colleagues1 reported high heritability of gray-matter volume in several cortical regions using voxel-based MRI techniques. Gray matter substantially correlated with general intelligence, or ‘g’. These findings prompt three major questions: (i) is the high heritability specific to gray-matter volume, (ii) is the correlation with g specific to gray-matter volume and (iii) is the correlation between gray-matter volume and g of genetic or environmental origin? We addressed the first question in a large Dutch sample of twins and their siblings (258 Dutch adults from 112 extended twin families)2. We found high heritability for total brain gray-matter volume (Table 1), comparable to the estimate reported by Thompson and colleagues1. In addition, we found high heritability for total brain white-matter volume. As stated in a commentary3 on the recent report in Nature Neuroscience1, high heritability of gray matter implies that interindividual variation in cell-body volume is not modified by experience. Because white matter reflects the degree of interconnection between different neurons, interindividual variance in whitematter volume might be expected to be more under the influence of experience and less under genetic control. Our results clearly suggest otherwise. Either environmental experience barely contributes to interindividual variation in white-matter volume or, alternatively, with the genes that influence g. The extent of the overlap is reflected by the magnitude of the genetic correlation. When the cross-trait/cross-twin correlations are similar for MZ and DZ twins, this suggests that environmental factors contribute to the observed phenotypic correlation between brain volume and g. Given a heritability of 0.85 for brain volume2, a heritability of 0.80 for g (ref. 5) and a correlation between brain volume and g of 0.40 (ref. 7), at least 17 MZ and 17 DZ pairs are needed to detect a genetic correlation with 80% power (and α = 0.05) that explains the observed correlation. In 24 MZ pairs, 31 DZ pairs and 25 additional siblings, we decomposed the correlation between brain volumes and g into genetic and environmental components by using structural equation modeling for a multivariate genetic design (gray matter, white matter and g)6. This showed that the correlation between gray-matter volume and g was due completely to genetic factors and not to environmental factors. We obtained the same result for the correlation between white-matter volume and g. Thus, the answer to the third question is The association between brain volume and intelligence is of genetic origin