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Bird's Eye Views of the Brain Connectivity Maze

3 April 2012. Trillions of synapses in the human brain connect neurons near and far, with information traversing different paths within this maze of connectivity. But don’t be fooled: this elaborate network reflects some pretty basic principles, according to two new studies.

One, a modeling study led by Edward Bullmore of University of Cambridge, United Kingdom, published online March 30 in the Proceedings of the National Academy of Sciences, proposes that the brain’s wiring diagram is generated by two competing processes that, when “detuned,” result in patterns found in schizophrenia. Another study, published March 30 in Science and spearheaded by Van Wedeen of Massachusetts General Hospital in Charlestown, distills the weblike connections visualized by diffusion magnetic resonance imaging (MRI) into an orderly, three-dimensional grid. Together, the studies suggest that grasping the logic behind the tangle of neural connections may make it easier to detect areas of pathology in brain disorders of all kinds, including schizophrenia.

It’s a small world after all
Abnormal brain neurodevelopment has long been suspected in schizophrenia, and genetic risk factors such as DISC1 are implicated in various processes of brain-making, including neural proliferation, migration, and synapse formation (see SRF related news story). But these findings have not yet congealed into an understanding of the core differences in the wiring diagram of individuals with schizophrenia. Brain imaging offers a more top-down approach, and several different aspects of brain organization to measure.

One option is to study the default-mode network, a collection of brain areas that are active when a person is awake but at rest. Functional MRI picks up this resting-state activity, and correlations in its ups and downs between two regions point to functional connections between them, thus capturing the inherent organization of at least some of the brain’s pathways. Aberrations in the default-mode network have been found in schizophrenia, including hyperactive resting-state activity that correlated with worse performance on a working memory task (see SRF related news story). Similarly, a recent study identified differences in resting-state activity that distinguished schizophrenia from bipolar disorder—differences that were shared with unaffected first-degree relatives (Meda et al., 2012). This suggests that focusing on resting-state activity could help find the genetic factors contributing to altered networks in disease.

The study by Bullmore and colleagues used resting-state activity, but went beyond cataloging differences in functional connectivity between particular regions to get at the shape, or “topology,” of the brain’s overall connectivity patterns. This approach focuses on the number of connections emanating from any one region, or “node,” to identify hubs that contact many nodes, and clusters of nodes that communicate largely with each other; the result looks much like an airline’s flight map across the world. Researchers have drawn from graph theory to analyze the network properties of a range of brain data, including gene transcription (see SRF related news story), neuroanatomical structure, and functional connectivity (Bullmore and Sporns, 2009).

Seen this way, the brain looks like a “small world” network in that the path between any two nodes is short compared to a random network. (Social networks are similar, with small degrees of separation between people within it, prompting exclamations of “Small world!” upon discovering a mutual acquaintance.) This small world structure is disrupted in schizophrenia (Liu et al., 2008), with studies finding an increased connection distance and fewer hubs in fragmented networks (Bassett et al., 2008; Lynall et al 2010).

Economical clustering
To address why this might be, first author Petra Vértes and colleagues set out to understand the forces that shape these networks in the first place. The researchers developed a computer model to mimic the network shape that emerged from resting-state fMRI data from 140 cortical areas in a group of 20 healthy study participants. One prevailing idea is that networks organize themselves “economically” so that the high-maintenance long-distance connections are few; however, the researchers found that simulations based solely on this distance penalty—in which the probability of a connection between two regions dropped with the distance between them—did not reproduce the network’s properties. When they added another “clustering” rule by which the probability to form a connection increased between two regions if they were already connected to a common neighbor nearby (akin to a mutual acquaintance), this reproduced the network in terms of clustering, efficiency (related to the path lengths within the network), and modularity (the community structure of nodes densely connected to one another).

They found that this two-rule “economical clustering” process could also account for network properties on which the computer model was not trained, like the distribution of distances between any two nodes in the network. Also, the particular values for the distance penalty and the clustering rule that best fit the first dataset also predicted the network features obtained from a second fMRI dataset from 12 healthy participants. Finally, the researchers found that the model could account for the altered network configurations found in 19 individuals with childhood-onset schizophrenia, which display less modularity and clustering—but only when the model parameters were tweaked away from the values that worked for healthy participants. This suggests that in schizophrenia, the distance penalty rule is still in effect but less strictly enforced—something that could fragment a network by allowing more long-distance connections.

Though this exercise does not prove that the brain actually uses these rules—which have biologically plausible correlates—it does suggest that the complexity of these networks may stem from a small number of factors.

Path neighborhoods
In the Science paper, Wedeen and colleagues used diffusion spectrum MRI (DSI) to visualize the actual trajectories these connections take within the brain. A variant of diffusion-weighted imaging techniques that infers white matter structure from the direction of movement of water molecules, DSI can detect different diffusion patterns within a single voxel, making it sensitive enough to resolve fiber tracts that crisscross each other (Wedeen et al., 2008).

DSI of whole-brain samples from four different nonhuman primate species and of six live human subjects visualized the spaghetti of white matter, and ensuing analysis reconstructed its paths taken throughout the brain. To get at the spatial relations between these different pathways, the researchers pinpointed the main path of a small region, then delineated the “path neighborhood” for it, which consisted of all other paths that shared at least one voxel with the main path. This revealed a grid-like sheet, with pathways running either parallel to or crossing perpendicularly to the main path—much like the fibers within a woven cloth. In a three-dimensional view, these gridlike sheets were stacked on top of each other, and diagonal paths were not observed. For example, analysis of a fiber tract known to split into two seemingly diagonal branches in the frontal lobe of the rhesus monkey revealed that this split actually consisted of one branch turning 90 degrees into a different plane.

Click on the image to launch the video.

The human brain's connections turn out to be an orderly 3D grid structure with no diagonals. 2D sheets of parallel fibers cross at right angles, "like the warp and weft of a fabric." Image credit: Van Wedeen

Though these surfaces were curved and folded to fit within the brain, their basic structure was preserved across different parts of the brain and across species. The researchers hypothesize that this simple structure reflects the three axes of axon-guiding chemical gradients operating during brain development. This new coordinate system may make it easier to pick up on connectivity pathologies in brain disorders like schizophrenia, which has been characterized by white matter abnormalities (Whitford et al., 2011).

Genetic parcellation
With the rise of imaging genetics, research will soon explore the extent to which a particular variant controls these global views of brain organization. Indeed, another study in the same issue of Science finds the fingerprints of the genetic program upon cortical organization (Chen et al., 2012). Using structural MRI to delineate the cortical surface area of 406 twin pairs, the researchers compared monozygotic to dizygotic twin data to identify 12 genetically based subdivisions similar to recognized regions within the brain. This “genetically parceled” brain atlas, and the connectivity grids and topologies observed in the other studies, represent refinements of brain phenotype which may better illuminate the influences of genetic risk factors in disorders like schizophrenia.—Michele Solis.

References:
Vértes PE, Alexander-Bloch AF, Gogtay N, Giedd JN, Rapoport JL, Bullmore ET. Simple models of human brain functional networks. Proc Natl Acad. Sci USA. 2012 Mar 30. Abstract

Wedeen VJ, Rosene DL, Wang R, Dai G, Mortazavi F, Hagmann P, Kaas JH, Tseng WY. The geometric structure of the brain fiber pathways. Science. 2012 Mar 30;335: 1628-1634. Abstract

Comments on Related News


Related News: Default Mode Network Acts Up in Schizophrenia

Comment by:  Vince Calhoun
Submitted 27 January 2009
Posted 27 January 2009

In this work the authors test for differences in the default mode network between healthy controls, patients with schizophrenia, and first degree relatives of the patients. They look at both the degree to which the default mode is modulated by a working memory task and also examine the strength of the functional connectivity. The controls are found to show the most default mode signal decrease during a task, with relatives and patients showing much less. The controls, relatives, and patients show increasing amounts of functional connectivity within the default mode regions. In addition, signal in some of the regions correlated with positive symptoms. The findings in the chronic patients and controls are consistent with our previous work in Garrity et al., 2007, which also showed significantly more functional connectivity in the default mode of schizophrenia patients and significant correlations in certain regions of the default mode with positive symptoms, and in both cases the regions we identified are similar to those shown in the Whitfield-Gabrieli paper. Our work in Kim et al., 2009, was a large multisite study showing significantly fewer default mode signal decreases for the auditory oddball task in chronic schizophrenia patients, again consistent with the Whitfield-Gabrieli paper, but in a different task.

The most interesting contribution of the Whitfield-Gabrieli paper is their inclusion of a first-degree relative group. They found that the first-degree relatives are “in between” the healthy controls and the chronic patients in terms of both the degree to which they modulate the default mode, as well as in their degree of functional connectivity. This has interesting implications in terms of the genetic aspects of the illness and suggests that the default mode may be a potential schizophrenia endophenotype. It will be interesting in future studies to examine both the heritability of the default mode patterns and their genetic underpinnings.

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Related News: Default Mode Network Acts Up in Schizophrenia

Comment by:  Edith Pomarol-Clotet
Submitted 28 January 2009
Posted 28 January 2009

The Default Mode Network and Schizophrenia
For a long time functional imaging research has focused on brain activations. However, since 2001 it has been appreciated that there is also a network of brain regions—which includes particularly two midline regions, the medial prefrontal cortex and the posterior cingulate cortex/precuneous—which deactivates during performance of a wide range of cognitive tasks. Why some brain regions should be active at rest but deactivate when tasks have to be performed is unclear, but there is intense speculation that this network is involved in functions such as self-reflection, self-monitoring, and the maintenance of one’s sense of self.

Could the default mode network be implicated in neuropsychiatric disease states? There is evidence that this is the case in autism, and a handful of studies have been also carried out in schizophrenia. Now, Whitfield-Gabrieli and colleagues report that 13 schizophrenic patients in the early phase of illness showed a failure to deactivate the anterior medial prefrontal node of the default mode network when they performed a working memory task. They also find that failure to deactivate is seen to a lesser but still significant extent in unaffected first-degree relatives of the schizophrenic patients, and that the degree of failure to deactivate is associated with both the severity of positive and negative symptoms in the patients.

Importantly, the findings of Whitfield-Gabrieli and colleagues are closely similar to those of another recent study by our group (Pomarol-Clotet et al., 2008), which found failure to deactivate in the medial prefrontal cortex node of the default mode network in 32 chronic schizophrenic patients. This is a striking convergence in the field of functional imaging studies of schizophrenia, which has previously been marked by diverse and often conflicting findings. Additionally, in both studies the magnitude of the difference between patients and controls was large and visually striking. These findings suggest that we may be dealing with an important abnormality which could be close to the disease process in schizophrenia.

If so, what does dysfunction in the default mode network mean? On the one hand, failure to deactivate part of a network whose activity normally decreases when attention has to be turned to performance of external tasks might be expected to interfere with normal cognitive operations. Consistent with this, cognitive impairment is nowadays accepted as being an important, or even a “core” feature of schizophrenia. Perhaps more importantly, could it be that default mode network dysfunction can help us understand the symptoms of schizophrenia? As Whitfield-Gabrieli and colleagues note, if the default mode network is involved in self-reflection, self-monitoring, and maintenance of one’s sense of self, then failure of deactivation might lead to an exaggerated focus on one’s own thoughts and feelings, excessive self-reference, and/or a breakdown in the boundary between the inner self and the external world. The default mode network may thus have the potential to account for two major realms of clinical abnormality in schizophrenia—its symptoms and the cognitive impairment that is frequently associated with them.

View all comments by Edith Pomarol-Clotet

Related News: Default Mode Network Acts Up in Schizophrenia

Comment by:  Samantha BroydEdmund Sonuga-Barke
Submitted 4 February 2009
Posted 4 February 2009

The surge in scientific interest in patterns of connectivity and activation of resting-state brain function and the default-mode network has recently extended to default-mode brain dysfunction in mental disorders (for a review, please see Broyd et al., 2008). Whitfield-Gabrieli et al. examine resting-state and (working-memory) task-related brain activity in 13 patients with early-phase schizophrenia, 13 unaffected first-degree relatives, and 13 healthy control participants. These authors report hyperconnectivity in the default-mode network in patients and relatives during rest, and note that this enhanced connectivity was correlated with psychopathology. Further, patients and relatives exhibited reduced task-related suppression (hyperactivation) of the medial prefrontal region of the default-mode network relative to the control group, even after controlling for task performance.

The findings from the Whitfield-Gabrieli paper are in accordance with those from a number of other research groups investigating possible default-mode network dysfunction in schizophrenia. For example, in a similar working memory task Pomarol-Clotet and colleagues (2008) have also shown reduced task-related suppression of medial frontal nodes of the default-mode network in 32 patients with chronic schizophrenia. However, the findings are at odds with research reporting widespread reductions in functional connectivity in the resting brain of this clinical group (e.g., Bluhm et al., 2007; Liang et al., 2006). As noted by Whitfield-Gabrieli et al., increased connectivity and reduced task-related suppression of default-mode activity may redirect attentional focus from task-related events to introspective and self-referential thought processes. The reduced anti-correlation between the task-positive and default-mode network in patients further supports and helps biologically ground suggestions of the possibility of an overzealous focus on internal thought. Perhaps even more interestingly, the study by Whitfield-Gabrieli and colleagues suggests that aberrant patterns of activation and connectivity in the default-mode network, and in particular the medial frontal region of this network, may be associated with genetic risk for schizophrenia. Although there are some inconsistencies in the literature regarding the role of the default-mode network in schizophrenia, the work of Whitfield-Gabrieli and others suggests that this network may well contribute to the pathophysiology of this disorder and is relevant to contemporary models of schizophrenia. Indeed, the recent flurry in empirical research investigating the clinical relevance of this network to mental disorder has highlighted a number of possible putative mechanisms that might link the default-mode network to disorder. Firstly, effective transitioning from the resting-state to task-related activity appears to be particularly vulnerable to dysfunction in mental disorders and may be characterized by deficits in attentional control. Sonuga-Barke and Castellanos (2007) have suggested that interference arising from a reduction in the task-related deactivation of the default-mode network may underlie the disruption of attentional control. The default-mode interference hypothesis proposes that spontaneous low-frequency activity in the default-mode network, normally attenuated during goal-directed tasks, can intrude on task-specific activity and create cyclical lapses in attention resulting in increased variability and a decline in task performance (Sonuga-Barke and Castellanos, 2007). Sonuga-Barke and Castellanos (2007) suggest that the efficacious transition from rest to task and the maintenance of task-specific activity may be moderated by trait factors such as disorder. Secondly, the degree of functional connectivity in the default-mode network may highlight problems of reduced connectivity, or excess functional connectivity (e.g., schizophrenia), which suggests a zealous focus on self-referential processing and introspective thought. Thirdly, the strength of the anti-correlation between the default-mode and task-positive networks may also indicate a clinical susceptibility to introspective or extrospective orienting. Finally, future research should continue to examine the etiology of the default-mode network in schizophrenia.

References:

Bluhm, R.L., Miller, J., Lanius, R.A., Osuch, E.A., Boksman, K., Neufeld, R.W.J., Théberge, J., Schaefer, B., & Williamson, P. (2007). Spontaneous low-frequency fluctuations in the BOLD signal in schizophrenic patients: Anomalies in the default network. Schizophrenia Bulletin, 33, 1004-1012. Abstract

Broyd, S.J., Demanuele, D., Debener, S., Helps, S.K., James, C.J., & Sonuga-Barke, E.J.S. (in press). Default-mode brain dysfunction in mental disorders: a systematic review. Neurosci Biobehav Rev. 2008 Sep 9. Abstract

Liang, M., Zhou, Y., Jiang, T., Liu, Z., Tian, L., Liu, H., and Hao, Y. (2006). Widespread functional disconnectivity in schizophrenia with resting-state functional magnetic resonance imaging. NeuroReport, 17, 209-213. Abstract

Pomarol-Clotet, E., Salvador, R., Sarro, S., Gomar, J., Vila, F., Martinez, A., Guerrero, A.,Ortiz-Gil, J., Sans-Sansa, B., Capdevila, A., Cebemanos, J.M., McKenna, P.J., 2008. Failure to deactivate in the prefrontal cortex in schizophrenia: dysfunction of the default-mode network? Psychological Medicine, 38, 1185–1193. Abstract

Sonuga-Barke, E.J.S., Castellanos, F.X., 2007. Spontaneous attentional fluctuations in impaired states and pathological conditions: a neurobiological hypothesis. Neuroscience Biobehavioural Reviews, 31, 977–986. Abstract

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Related News: Default Mode Network Acts Up in Schizophrenia

Comment by:  Yuan ZhouTianzi JiangZhening Liu
Submitted 18 February 2009
Posted 22 February 2009
  I recommend the Primary Papers

The consistent findings on default-mode network in human brain have attracted the researcher’s attention to the task-independent activity. The component regions of the default-mode network, especially medial prefrontal cortex and posterior cingulate cortex/precuneus, are related to self-reflective activities and attention. Both of these functions are observed to be impaired in schizophrenia. And thus the default-mode network has also attracted more and more attention in the schizophrenia research community. The study of Whitfield-Gabrieli et al. shows a further step along this research streamline.

The authors found hyperactivity (reduced task suppression) and hyperconnectivity of the default network in schizophrenia, and found that hyperactivity and hyperconnectivity of the default network are associated with poor work memory performance and greater psychopathology in schizophrenia. And they found less anticorrelation between the medial prefrontal cortex and the right dorsolateral prefrontal cortex, a region showing increased task-related activity in schizophrenia, whether during rest or task. Furthermore, the hyperactivity in medial prefrontal cortex is negatively related to the hyperconnectivity of the default network in schizophrenia.

There are two main contributions in this work. First, they found significant correlation between the abnormalities in the default mode network and impaired cognitive performance and psychopathology in schizophrenia. Thus they propose a new explanation for the impaired working memory and attention in schizophrenia, and propose a possibility that schizophrenic symptoms, such as delusions and hallucinations, may be due to the blurred boundary between internal thoughts and external perceptions. Secondly, they recruited the first-degree relatives of these patients in this study, and found that these healthy relatives showed abnormalities in the default network similar to that of patients but to a lesser extent. This is the first study investigating the default mode network of relatives of individuals with schizophrenia. This finding indicates that the dysfunction in the default mode network is associated with genetic risk for schizophrenia.

The findings in schizophrenia are consistent with our previous work (Zhou et al., 2007), in which we also found hyperconnectivity of the default mode network during rest. Considering the differences in ethnicity of participants (Chinese in our study) and methodology, the consistency in the hyperconnectivity of the default mode network in schizophrenia is exciting, which supports the possibility that abnormality in the default-mode network may be a potential imaging biomarker to assist diagnosis of schizophrenia. However, this needs to be validated in future studies with a large sample size, due to other contradictory findings, for example, the reduced resting-state functional connectivities associated with the posterior cingulate cortex in chronic, medicated schizophrenic patients (Bluhm et al., 2007). In addition, further studies should focus on default-mode function in different clinical subtypes, as schizophrenia is a complicated disorder. Finally, it should be noticed that the hyperconnectivity of the default-mode network is not exclusively contradictory with hyperconnectivity in other regions, as we previously found (Liang et al., 2006). It is possible that hyperconnectivity and hyperconnectivity coexist in the brains of individuals with schizophrenia and together lead to the complicated symptoms and cognitive deficits.

References:

Bluhm, R. L., Miller, J., Lanius, R. A., Osuch, E. A., Boksman, K., Neufeld, R. W., et al., 2007. Spontaneous low-frequency fluctuations in the BOLD signal in schizophrenic patients: anomalies in the default network. Schizophr Bull 33, 1004-1012. Abstract

Liang, M., Zhou, Y., Jiang, T., Liu, Z., Tian, L., Liu, H., et al., 2006. Widespread functional disconnectivity in schizophrenia with resting-state functional magnetic resonance imaging. Neuroreport 17, 209-213. Abstract

Whitfield-Gabrieli, S., Thermenos, H. W., Milanovic, S., Tsuang, M. T., Faraone, S. V., McCarley, R. W., et al., 2009. Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. Proc Natl Acad Sci U S A 106, 1279-1284. Abstract

Zhou, Y., Liang, M., Tian, L., Wang, K., Hao, Y., Liu, H., et al., 2007. Functional disintegration in paranoid schizophrenia using resting-state fMRI. Schizophr Res 97, 194-205. Abstract

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