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ScienceDirect Reconciling abnormalities of brain network structure and function in schizophrenia Alex Fornito1 and Edward T Bullmore2,3 Schizophrenia is widely regarded as a disorder of abnormal brain connectivity. Magnetic resonance imaging (MRI) suggests that patients show robust reductions of structural connectivity. However, corresponding changes in functional connectivity do not always follow, with increased functional connectivity being reported in many cases. Here, we consider different methodological and mechanistic accounts that might reconcile these apparently contradictory findings and argue that increased functional connectivity in schizophrenia likely represents a pathophysiological dysregulation of brain activity arising from abnormal neurodevelopmental wiring of structural connections linking putative hub regions of association cortex to other brain areas. Elucidating the pathophysiological significance of connectivity abnormalities in schizophrenia will be contingent on better understanding how network structure shapes and constrains function. Addresses 1 Monash Clinical and Imaging Neuroscience, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Victoria, Australia 2 Brain Mapping Unit, Department of Psychiatry, and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK 3 Alternative Discovery and Development, GlaxoSmithKline, Cambridge, UK Corresponding author: Fornito, Alex ([email protected])
Current Opinion in Neurobiology 2015, 30:44–50 This review comes from a themed issue on Neuropsychiatry Edited by Steven Hyman and Raquel Gur
http://dx.doi.org/10.1016/j.conb.2014.08.006 0959-4388/# 2014 Elsevier Ltd. All right reserved.
Schizophrenia is thought to arise from abnormal connectivity between spatially distributed neuronal systems [1–3]. Neuroimaging, and magnetic resonance imaging (MRI) in particular, has marshaled a large body of evidence in support of this hypothesis, identifying a plethora of structural and functional brain network changes in patients at all illness stages (reviewed in [4,5,6]). Two broad types of brain connectivity are commonly studied with MRI: structural and functional. Structural connectivity refers to the anatomical (i.e., synaptic, Current Opinion in Neurobiology 2015, 30:44–50
dendritic, axonal) connections between brain regions. It is commonly measured using diffusion-weighted MRI (dwMRI) tractography, which attempts to reconstruct the trajectories of major fiber bundles based on preferred directions of water diffusion in cerebral tissue [7]. Functional connectivity refers to a statistical dependence between neurophysiological recordings acquired in spatially distinct brain regions [8]. It is commonly assessed using functional MRI (fMRI), where covariations (typically correlations) of regional fluctuations of the blood-oxygenation-level-dependent (BOLD) signal are used to define functionally related networks either during task performance [9,10], or task-free, so-called restingstates [11,12]. Electro-encephalographic and magnetoencephalographic signals can also be used to gain greater temporal precision at the expense of spatial resolution in functional connectivity estimates. Structural and functional connectivity have a symbiotic relationship [13–15]. On the one hand, structural connectivity provides an anatomical substrate enabling the precise coordination of inter-regional synchronization dynamics; on the other hand, changes in functional connectivity enable the exploration of alternative network configurations that can, over time, modify network anatomy, as occurs with experience-dependent plasticity. This relationship implies a tight coupling between structure and function, such that increases or decreases in one measure are expected to effect similar changes in the other. In schizophrenia, a large body of studies indicates that patients almost always show impaired structural connectivity [4,5]. In contrast, investigations of functional connectivity have been less consistent (e.g., Figure 1): While functional connectivity reductions are prevalent (reviewed in [4,10,16,17]), as would be predicted from the structural findings, a substantial number of reports describe functional connectivity increases in select neural systems (e.g., [18,19,20]). These functional connectivity increases often correlate with symptom severity [19– 21,22], suggesting that they are intimately related to the clinical expression of the disease. Importantly, even when structure and function have been measured in the same sample, increased functional connectivity has been found to co-occur with reduced structural connectivity [23]. An important question thus emerges: Is this apparent discrepancy a pathophysiologically informative clue yielding insights into how the brain is disrupted by the disease, or is it merely an artifact of the different analytic www.sciencedirect.com
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De-coupling of network structure and function in schizophrenia. (a) Shows an example of a brain-wide map of structural connectivity deficits in patients with schizophrenia, highlighting a relatively diffuse impairment that particularly affects fronto-posterior anatomical connectivity. In this wholebrain analysis, no increases of structural connectivity were found. Letters denote different regions (see below for key). (b) Illustrates frontal regions showing decreased and increased functional connectivity with seed regions in the dorsal (top) and ventral (bottom) caudate nucleus, respectively, in patients with schizophrenia (yellow, blue) and their unaffected, first-degree relatives (magenta, green). Thus, despite a fairly global impairment of structural connectivity (depicted in (a)), systems-specific increases in functional connectivity can be observed (b). (c,d) Brain-wide alterations of structural (c) and functional (d) connectivity in the same sample of patients with schizophrenia. Blue and green depict links where anatomical and functional connectivity, respectively, were reduced in patients; red depicts links where functional connectivity was increased in the patient group. (a) reproduced from [24], (b) from [18], and (c,d) from [23] with permission. Regional abbreviations in (a) are as follow: A. Left Superior Frontal, B. Right Superior Frontal, C. Left Supplementary Motor Area, D. Left Superior Medial Frontal, E. Right Supplementary Motor Area, F. Right Superior Medial Frontal, G. Right Superior Parietal, H. Right Superior Occipital, I. Left Cuneus, J. Left Superior Occipital, K. Left Precuneus, L. Right Precuneus, M. Left Middle Temporal, N. Left Middle Occipital, O. Left Inferior Temporal, P. Left Fusiform, Q. Right Cuneus, R. Left Hippocampus, S. Left Middle Cingulum.
techniques applied to fMRI and dwMRI data when mapping brain connectivity? In this article, we consider some competing accounts of the apparent decoupling of functional from structural connectivity in schizophrenia. First, we focus on a possible methodological explanation, related to the common practice of correcting fMRI time series for a global signal before estimation of inter-regional www.sciencedirect.com
functional connectivity. Then, we consider evidence supporting two alternative, mechanistic explanations: That hyperconnectivity reflects disease processes that result in de-differentiated or compensatory recruitment of select brain networks; or that functional hyperconnectivity is a paradoxical but topologically predictable effect of neurodevelopmentally driven reductions of the anatomical connectivity of network hubs. Current Opinion in Neurobiology 2015, 30:44–50
46 Neuropsychiatry
The global signal, functional connectivity and schizophrenia A common yet controversial preprocessing step in fMRI studies of functional connectivity involves correcting regional signals of interest for covariations with a time course averaged across all brain voxels — the so-called global signal [25]. The global signal is correlated with non-neuronal physiological drivers of BOLD signal fluctuations, particularly low-frequency respiratory fluctuations [26,27]. Global signal correction thus offers an apparently simple method for removing non-neuronal sources of variance from regional time courses without having to acquire measures of peripheral physiology [27,28] and often increases the specificity of functional connectivity patterns in an anatomically plausible manner [11]. Importantly, most studies reporting functional connectivity increases in patients have used global signal correction or a related method; studies that have not employed this step are more likely to report evidence for widespread functional connectivity reductions [4]. Global signal correction is controversial because it alters the distribution of functional connectivity estimates in the data [25]. This shift often leads to the emergence of spurious connectivity values [29] and the distortion of group differences in these estimates in a manner that depends on the noise (e.g., head motion, peripheral physiology) and covariance (intrinsic coupling) structure of the data in each group [30]. Because these factors are rarely quantified, the differential effect of global signal correction in patients and controls is difficult to predict. This problem is further compounded by the fact that the global signal reflects a composite of both signals of interest (i.e., those derived from grey matter) and no interest (e.g., white matter and cerebrospinal fluid signal fluctuations). Correction of this signal may thus remove some of the effects of interest, potentially alter the direction of group differences, or lead to the emergence of differences when in fact there are none [30]. These effects can vary depending on the specific neural system being studied and the metric used to quantify functional connectivity [31,32]. Supporting the idea that the global signal contains pathophysiologically relevant information, one recent study found evidence of increased global signal spectral power and variance in two independent samples of schizophrenia patients compared to healthy controls [31]. Moreover, these increases correlated with symptom severity and were not apparent in bipolar patients, providing some evidence for a diagnostically specific diseaserelated neural phenotype. Critically, patients showed evidence of increased functional connectivity in select prefrontal systems only in uncorrected data; data corrected for global signal fluctuations only revealed evidence of reduced functional connectivity in patients. Computational modeling further suggested that increased Current Opinion in Neurobiology 2015, 30:44–50
functional coupling either within local microcircuits or globally across the entire brain could explain the observed increase in global signal power. Since the model is noisefree, this result suggests that elevated functional connectivity may be an intrinsic feature of schizophrenia pathophysiology that explains altered global signal fluctuations and which is not merely an artifact of global signal correction itself. However, some studies have not replicated these global signal alterations in schizophrenia patients [32], and the findings of this study are somewhat at odds with other work suggesting that increased functional connectivity is more likely to be observed after correction for the global signal (reviewed in [4]). Moreover, while increased functional connectivity is a replicated finding in schizophrenia, decreased functional connectivity is more common [4,5], suggesting a global increase of functional coupling represents an unlikely explanation for the broad spectrum of functional connectivity changes observed in patients. Nonetheless, the modeling results suggest that increased functional coupling in some systems may be a valid feature of disturbed brain dynamics in the disorder. Moreover, the observation that functional connectivity can be increased in the absence of global signal correction indicates that such increases are not solely attributable to this particular preprocessing step.
Plasticity, neurodevelopment and dysconnectivity in schizophrenia If increased functional connectivity is indeed a pathophysiologically informative feature of brain network abnormalities in schizophrenia, how does it arise in the presence of a fairly diffuse impairment of structural connectivity [23,24]? One explanation is that dwMRI offers a relatively indirect measure of the integrity of anatomical connections and should be interpreted with caution [33]. Most dwMRI tractographic methods have difficulties accurately reconstructing fiber pathways in regions of crossing fibers, and many commonly used metrics will give the impression of reduced axonal integrity in such cases. However, neurodevelopmental abnormalities of brain wiring in schizophrenia could plausibly result in disordered white matter architecture and yield a higher frequency of crossing fibers across the brain. Under such circumstances, even areas of increased fiber density may be identified as showing reduced connectivity if these fibers are disordered compared to healthy controls. To our knowledge, no systematic study of the prevalence of crossing fibers in schizophrenia has been conducted. Studies of other neurological disorders may offer further useful insights into mechanistic accounts of brain structure–function decoupling following pathology. Multiple sclerosis (MS) offers a particularly appropriate example, as it is associated with a known white matter lesion — demyelination of axonal connections — that disrupts www.sciencedirect.com
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inter-regional communication and causes a variety of physical, cognitive and psychiatric symptoms. Consistent with this pathology, dwMRI measures of structural connectivity are almost universally reduced in this patient group although, as with schizophrenia, functional connectivity changes are characterized by a combination of both increases and decreases relative to controls ([34] reviewed in [36]). This de-coupling can co-occur in the same neural system; for example, MS patients with optic neuritis, which is associated with inflammatory demyelination of visual tracts, show increased extrastriate functional connectivity [37]. Similarly, patients with relapsing-remitting MS have shown reduced structural and increased functional connectivity of the default mode network [38]. Increased functional connectivity in MS may be more common in earlier illness stages [34,35]. This finding suggests that functional connectivity increases in this disorder may reflect a plastic and possibly compensatory response to axonal degradation and/or inflammation [39]. Similar findings have been proposed to explain
reports of activation and connectivity increases in the early stages of Alzheimer’s disease [40] and Huntington’s disease [41]. However, not all instances of increased functional connectivity may be adaptive — some studies have found that increased functional connectivity in MS is associated with poorer cognitive performance [34]. In such cases, axonal inflammation and demyelination may dysregulate circuit-level excitatory and inhibitory interactions, leading to a de-differentiated state of neural activity characterized by a breakdown of normally segregated neural activity [42]. Whether a similar mechanism explains functional connectivity increases in schizophrenia is unclear, though evidence that such increases correlate with symptom severity in this patient group [19– 21,22] supports a de-differentiation account over a compensatory mechanism. While MS offers useful insights into functional hyperconnectivity in schizophrenia, the pathogenic mechanisms of the two disorders differ in several ways. In particular, abnormal neurodevelopment is thought to play a prominent role in the aetiology of schizophrenia. The
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Characteristics of structural hub impairment in schizophrenia. (a) Depicts putative structural connectivity hubs of the brain (top), as identified using a hub score that counts the number of hub-like topological properties possessed by each region. Hubs have a score 2 and predominantly localize to association cortices. Bottom depicts frontal hub regions where topological centrality was reduced in anatomical networks of patients with schizophrenia. (b) Depicts maturation of hub connections throughout adolescence. In general, this period is associated with an increase of hub connectivity, particularly to frontal regions. (c,d) Illustration of how topological properties of structural paths can affect functional connectivity. Black dotted lines indicate the path of a primary signal traveling from a source to target region. This path is shown to pass through either a topologically peripheral node (c) or a central node with many connections (d). In the latter case, the high connectivity of the central node offers greater opportunity for the target signal to mix with, or be corrupted by, other incoming traffic. Functional connectivity between source and target is thus expected to be higher for (c) rather than (d). In this manner, structural dysconnectivity of hub regions may actually increase functional connectivity between some brain regions. (a) reproduced from [48]; (b) from [49]; and (c) adapted from [50] with permission. www.sciencedirect.com
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dominant model posits that an early genetic or environmental neural insult derails or interacts with normal brain development, leading to aberrant maturation of laterdeveloping systems. This altered maturation causes dysfunction of critical circuits as they come ‘on-line’, thereby giving rise to clinical symptoms in late adolescence and early adulthood [43]. The last regions of the brain to develop are typically areas of association cortex, which are known to act as connectivity hubs that integrate information from diverse network elements (Figure 2a,b; [44,45,46]). Deficient wiring of these areas can dysregulate communication across widespread areas, causing complex changes in brain dynamics involving both abnormal increases and decreases in functional connectivity. In some cases, the increases may be a compensatory response to dysregulated signaling in specific parts of the network; in other cases, abnormal wiring of structural connections may lead to a breakdown of normally segregated systems [47] and a de-differentiation of neural activity. Two lines of evidence support this hypothesis. First, recent work suggests that schizophrenia is associated with a disproportionate compromise of structural connectivity between network connector hubs located in association cortex (Figure 2a; [48,51]). Second, functional connectivity between two regions is strongly related to characteristics of the structural path that interconnects them, such that nodes linked by a path traversing a highly connected hub node will show reduced functional connectivity compared to a path crossing less connected regions [50]. The high connectivity of hub regions increases the likelihood that any passing signal will be mixed with other incoming traffic, adding noise as messages pass between source and target (Figure 2c,d). An impairment of anatomical connectivity of network hubs may reduce this effect, thus offering a potential mechanism through which reduced structural connectivity may give rise to abnormal enhancements of functional connectivity. Accordingly, computational models have confirmed that structural removal of hub nodes in association cortices yields a complex pattern of increases and decreases in functional connectivity [52]. Analysis of how topological characteristics of structural paths predict functional connectivity increases in schizophrenia will be useful for testing this hypothesis.
Conclusions Increased functional connectivity is a replicated finding in schizophrenia. It occurs in the presence of a general impairment of structural connectivity, and it cannot be explained solely by methodological factors. In this review, we have considered how abnormal neurodevelopment of structural connectivity, particularly hub nodes in association cortex, might give rise to apparent functional hyperconnectivity in patients. Naturally, other factors may play a part. For example, antipsychotic treatment can increase Current Opinion in Neurobiology 2015, 30:44–50
functional connectivity in some circuits (reviewed in [18,21,53]). However, evidence for functional connectivity increases in unmedicated high-risk samples [18,21,54] suggests that medication cannot explain all such reports of functional hyperconnectivity. Other factors, such as differences in head motion [55] and other analysis steps specific to fMRI network studies may also play a role (discussed in [56]). These influences notwithstanding, the evidence discussed here indicates that decoupling of brain network structure and function may represent an intrinsic property of how the disease affects the brain, and draws attention to the need for a greater understanding of how anatomical network topology shapes and constrains brain functional connectivity in both health and brain disorders.
Conflict of interest statement ETB is employed half-time by the University of Cambridge and half-time by GlaxoSmithKline; he holds shares in GSK. The Behavioural & Clinical Neuroscience Institute is supported by the Medical Research Council (UK) and the Wellcome Trust. AF is supported by an Australian Research Council Future Fellowship (ID: FT130100589) and National Health and Medical Research Council grants (IDs: 1050504 and 1066779).
Acknowledgements ETB is employed half-time by the University of Cambridge and half-time by GlaxoSmithKline; he holds shares in GSK. The Behavioural & Clinical Neuroscience Institute is supported by the Medical Research Council (UK) (Grant No. G1000183) and the Wellcome Trust (Grant No. 093875/Z/10/Z). AF is supported by an Australian Research Council Future Fellowship (ID: FT130100589) and National Health and Medical Research Council grants (IDs: 1050504 and 1066779).
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38. Zhou F, Zhuang Y, Gong H, Wang B, Wang X, Chen Q, Wu L, Wan H: Altered inter-subregion connectivity of the default mode network in relapsing remitting multiple sclerosis: a functional and structural connectivity study. PLoS ONE 2014, 9:e101198. 39. Schoonheim MM, Filippi M: Functional plasticity in MS: friend or foe? Neurology 2012, 79:1418-1419. Interesting discussion of how plasticity, inflammation and degeneration interact to determine functional changes seen in patients with MS. 40. Sperling R: Functional MRI studies of associative encoding in normal aging, mild cognitive impairment, and Alzheimer’s disease. Ann N Y Acad Sci 2007, 1097:146-155. 41. Papoutsi M, Labuschagne I, Tabrizi SJ, Stout JC: The cognitive burden in Huntington’s disease: pathology, phenotype, and mechanisms of compensation. Mov Disord 2014, 29:673-683. 42. Li SC, Lindenberger U, Sikstro¨m S: Aging cognition: from neuromodulation to representation. Trends Cogn Sci 2001, 5:479-486. 43. Weinberger DR: Implications of normal brain development for the pathogenesis of schizophrenia. Arch Gen Psychiatry 1987, 44:660-669. 44. van den Heuvel MP, Sporns O: Network hubs in the human brain. Trends Cogn Sci 2013, 17:683-696. Excellent overview of the role of connectivity hubs in the structure and function of brain networks, as well as their involvement in disease. 45. Buckner RL, Sepulcre J, Talukdar T, Krienen FM, Liu H, Hedden T, Andrews-Hanna JR, Sperling RA, Johnson KA: Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. J Neurosci 2009, 29:1860-1873. 46. Supekar K, Musen M, Menon V: Development of large-scale functional brain networks in children. PLoS Biol 2009, 7:e1000157. 47. Alexander-Bloch AF, Gogtay N, Meunier D, Birn R, Clasen L, Lalonde F, Lenroot R, Giedd J, Bullmore ET: Disrupted modularity and local connectivity of brain functional networks in childhood-onset schizophrenia. Front Syst Neurosci 2010, 4:147. 48. van den Heuvel MP, Mandl RC, Stam CJ, Kahn RS, Hulshoff Pol HE: Aberrant frontal and temporal complex network structure in schizophrenia: a graph theoretical analysis. J Neurosci 2010, 30:15915-15926.
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49. Hwang K, Hallquist MN, Luna B: The development of hub architecture in the human functional brain network. Cereb Cortex 2013, 23:2380-2393. 50. Gon˜i J, van den Heuvel MP, Avena-Koenigsberger A, Velez de Mendizabal N, Betzel RF, Griffa A, Hagmann P, CorominasMurtra B, Thiran J-P, Sporns O: Resting-brain functional connectivity predicted by analytic measures of network communication. Proc Natl Acad Sci U S A 2014, 111:833-838. Innovative study demonstrating that a simple analytic measures of network information flow computed on dwMRI structural networks predict empirically observed patterns of functional connectivity, thus demonstrating the strong influence of network structural topology on function. 51. van den Heuvel MP, Sporns O, Collin G, Scheewe T, Mandl RC, Cahn W, Goni J, Hulshoff Pol HE, Kahn RS: Abnormal rich club organization and functional brain dynamics in schizophrenia. JAMA Psychiatry 2013, 70:783-792. Provides evidence that structural connectivity impairments in patients with schizophrenia appear to specifically affect links between brain network hubs. 52. Alstott J, Breakspear M, Hagmann P, Cammoun L, Sporns O: Modeling the impact of lesions in the human brain. PLoS Comput Biol 2009, 5:e1000408. 53. Nejad AB, Ebdrup BH, Glenthøj BY, Siebner HR: Brain connectivity studies in schizophrenia: unravelling the effects of antipsychotics. Curr Neuropharmacol 2012, 10:219-230. 54. Anticevic A, Tang Y, Cho YT, Repovs G, Cole MW, Savic A, Wang F, Krystal JH, Xu K: Amygdala connectivity differs among chronic, early course, and individuals at risk for developing schizophrenia. Schizophr Bull 2013 http://dx.doi.org/10.1093/ schbul/sbt165. 55. Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE: Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 2012, 59:2142-2154. 56. Fornito A, Zalesky A, Breakspear M: Graph analysis of the human connectome: promise, progress, and pitfalls. NeuroImage 2013, 80:426-444. Detailed overview of the strengths and limitations of neuroimaging approaches to mapping brain structural and functional connectivity with graph theory.
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ScienceDirect Reconciling abnormalities of brain network structure and function in schizophrenia Alex Fornito1 and Edward T Bullmore2,3 Schizophrenia is widely regarded as a disorder of abnormal brain connectivity. Magnetic resonance imaging (MRI) suggests that patients show robust reductions of structural connectivity. However, corresponding changes in functional connectivity do not always follow, with increased functional connectivity being reported in many cases. Here, we consider different methodological and mechanistic accounts that might reconcile these apparently contradictory findings and argue that increased functional connectivity in schizophrenia likely represents a pathophysiological dysregulation of brain activity arising from abnormal neurodevelopmental wiring of structural connections linking putative hub regions of association cortex to other brain areas. Elucidating the pathophysiological significance of connectivity abnormalities in schizophrenia will be contingent on better understanding how network structure shapes and constrains function. Addresses 1 Monash Clinical and Imaging Neuroscience, School of Psychological Sciences and Monash Biomedical Imaging, Monash University, Victoria, Australia 2 Brain Mapping Unit, Department of Psychiatry, and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK 3 Alternative Discovery and Development, GlaxoSmithKline, Cambridge, UK Corresponding author: Fornito, Alex ([email protected])
Current Opinion in Neurobiology 2015, 30:44–50 This review comes from a themed issue on Neuropsychiatry Edited by Steven Hyman and Raquel Gur
http://dx.doi.org/10.1016/j.conb.2014.08.006 0959-4388/# 2014 Elsevier Ltd. All right reserved.
Schizophrenia is thought to arise from abnormal connectivity between spatially distributed neuronal systems [1–3]. Neuroimaging, and magnetic resonance imaging (MRI) in particular, has marshaled a large body of evidence in support of this hypothesis, identifying a plethora of structural and functional brain network changes in patients at all illness stages (reviewed in [4,5,6]). Two broad types of brain connectivity are commonly studied with MRI: structural and functional. Structural connectivity refers to the anatomical (i.e., synaptic, Current Opinion in Neurobiology 2015, 30:44–50
dendritic, axonal) connections between brain regions. It is commonly measured using diffusion-weighted MRI (dwMRI) tractography, which attempts to reconstruct the trajectories of major fiber bundles based on preferred directions of water diffusion in cerebral tissue [7]. Functional connectivity refers to a statistical dependence between neurophysiological recordings acquired in spatially distinct brain regions [8]. It is commonly assessed using functional MRI (fMRI), where covariations (typically correlations) of regional fluctuations of the blood-oxygenation-level-dependent (BOLD) signal are used to define functionally related networks either during task performance [9,10], or task-free, so-called restingstates [11,12]. Electro-encephalographic and magnetoencephalographic signals can also be used to gain greater temporal precision at the expense of spatial resolution in functional connectivity estimates. Structural and functional connectivity have a symbiotic relationship [13–15]. On the one hand, structural connectivity provides an anatomical substrate enabling the precise coordination of inter-regional synchronization dynamics; on the other hand, changes in functional connectivity enable the exploration of alternative network configurations that can, over time, modify network anatomy, as occurs with experience-dependent plasticity. This relationship implies a tight coupling between structure and function, such that increases or decreases in one measure are expected to effect similar changes in the other. In schizophrenia, a large body of studies indicates that patients almost always show impaired structural connectivity [4,5]. In contrast, investigations of functional connectivity have been less consistent (e.g., Figure 1): While functional connectivity reductions are prevalent (reviewed in [4,10,16,17]), as would be predicted from the structural findings, a substantial number of reports describe functional connectivity increases in select neural systems (e.g., [18,19,20]). These functional connectivity increases often correlate with symptom severity [19– 21,22], suggesting that they are intimately related to the clinical expression of the disease. Importantly, even when structure and function have been measured in the same sample, increased functional connectivity has been found to co-occur with reduced structural connectivity [23]. An important question thus emerges: Is this apparent discrepancy a pathophysiologically informative clue yielding insights into how the brain is disrupted by the disease, or is it merely an artifact of the different analytic www.sciencedirect.com
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De-coupling of network structure and function in schizophrenia. (a) Shows an example of a brain-wide map of structural connectivity deficits in patients with schizophrenia, highlighting a relatively diffuse impairment that particularly affects fronto-posterior anatomical connectivity. In this wholebrain analysis, no increases of structural connectivity were found. Letters denote different regions (see below for key). (b) Illustrates frontal regions showing decreased and increased functional connectivity with seed regions in the dorsal (top) and ventral (bottom) caudate nucleus, respectively, in patients with schizophrenia (yellow, blue) and their unaffected, first-degree relatives (magenta, green). Thus, despite a fairly global impairment of structural connectivity (depicted in (a)), systems-specific increases in functional connectivity can be observed (b). (c,d) Brain-wide alterations of structural (c) and functional (d) connectivity in the same sample of patients with schizophrenia. Blue and green depict links where anatomical and functional connectivity, respectively, were reduced in patients; red depicts links where functional connectivity was increased in the patient group. (a) reproduced from [24], (b) from [18], and (c,d) from [23] with permission. Regional abbreviations in (a) are as follow: A. Left Superior Frontal, B. Right Superior Frontal, C. Left Supplementary Motor Area, D. Left Superior Medial Frontal, E. Right Supplementary Motor Area, F. Right Superior Medial Frontal, G. Right Superior Parietal, H. Right Superior Occipital, I. Left Cuneus, J. Left Superior Occipital, K. Left Precuneus, L. Right Precuneus, M. Left Middle Temporal, N. Left Middle Occipital, O. Left Inferior Temporal, P. Left Fusiform, Q. Right Cuneus, R. Left Hippocampus, S. Left Middle Cingulum.
techniques applied to fMRI and dwMRI data when mapping brain connectivity? In this article, we consider some competing accounts of the apparent decoupling of functional from structural connectivity in schizophrenia. First, we focus on a possible methodological explanation, related to the common practice of correcting fMRI time series for a global signal before estimation of inter-regional www.sciencedirect.com
functional connectivity. Then, we consider evidence supporting two alternative, mechanistic explanations: That hyperconnectivity reflects disease processes that result in de-differentiated or compensatory recruitment of select brain networks; or that functional hyperconnectivity is a paradoxical but topologically predictable effect of neurodevelopmentally driven reductions of the anatomical connectivity of network hubs. Current Opinion in Neurobiology 2015, 30:44–50
46 Neuropsychiatry
The global signal, functional connectivity and schizophrenia A common yet controversial preprocessing step in fMRI studies of functional connectivity involves correcting regional signals of interest for covariations with a time course averaged across all brain voxels — the so-called global signal [25]. The global signal is correlated with non-neuronal physiological drivers of BOLD signal fluctuations, particularly low-frequency respiratory fluctuations [26,27]. Global signal correction thus offers an apparently simple method for removing non-neuronal sources of variance from regional time courses without having to acquire measures of peripheral physiology [27,28] and often increases the specificity of functional connectivity patterns in an anatomically plausible manner [11]. Importantly, most studies reporting functional connectivity increases in patients have used global signal correction or a related method; studies that have not employed this step are more likely to report evidence for widespread functional connectivity reductions [4]. Global signal correction is controversial because it alters the distribution of functional connectivity estimates in the data [25]. This shift often leads to the emergence of spurious connectivity values [29] and the distortion of group differences in these estimates in a manner that depends on the noise (e.g., head motion, peripheral physiology) and covariance (intrinsic coupling) structure of the data in each group [30]. Because these factors are rarely quantified, the differential effect of global signal correction in patients and controls is difficult to predict. This problem is further compounded by the fact that the global signal reflects a composite of both signals of interest (i.e., those derived from grey matter) and no interest (e.g., white matter and cerebrospinal fluid signal fluctuations). Correction of this signal may thus remove some of the effects of interest, potentially alter the direction of group differences, or lead to the emergence of differences when in fact there are none [30]. These effects can vary depending on the specific neural system being studied and the metric used to quantify functional connectivity [31,32]. Supporting the idea that the global signal contains pathophysiologically relevant information, one recent study found evidence of increased global signal spectral power and variance in two independent samples of schizophrenia patients compared to healthy controls [31]. Moreover, these increases correlated with symptom severity and were not apparent in bipolar patients, providing some evidence for a diagnostically specific diseaserelated neural phenotype. Critically, patients showed evidence of increased functional connectivity in select prefrontal systems only in uncorrected data; data corrected for global signal fluctuations only revealed evidence of reduced functional connectivity in patients. Computational modeling further suggested that increased Current Opinion in Neurobiology 2015, 30:44–50
functional coupling either within local microcircuits or globally across the entire brain could explain the observed increase in global signal power. Since the model is noisefree, this result suggests that elevated functional connectivity may be an intrinsic feature of schizophrenia pathophysiology that explains altered global signal fluctuations and which is not merely an artifact of global signal correction itself. However, some studies have not replicated these global signal alterations in schizophrenia patients [32], and the findings of this study are somewhat at odds with other work suggesting that increased functional connectivity is more likely to be observed after correction for the global signal (reviewed in [4]). Moreover, while increased functional connectivity is a replicated finding in schizophrenia, decreased functional connectivity is more common [4,5], suggesting a global increase of functional coupling represents an unlikely explanation for the broad spectrum of functional connectivity changes observed in patients. Nonetheless, the modeling results suggest that increased functional coupling in some systems may be a valid feature of disturbed brain dynamics in the disorder. Moreover, the observation that functional connectivity can be increased in the absence of global signal correction indicates that such increases are not solely attributable to this particular preprocessing step.
Plasticity, neurodevelopment and dysconnectivity in schizophrenia If increased functional connectivity is indeed a pathophysiologically informative feature of brain network abnormalities in schizophrenia, how does it arise in the presence of a fairly diffuse impairment of structural connectivity [23,24]? One explanation is that dwMRI offers a relatively indirect measure of the integrity of anatomical connections and should be interpreted with caution [33]. Most dwMRI tractographic methods have difficulties accurately reconstructing fiber pathways in regions of crossing fibers, and many commonly used metrics will give the impression of reduced axonal integrity in such cases. However, neurodevelopmental abnormalities of brain wiring in schizophrenia could plausibly result in disordered white matter architecture and yield a higher frequency of crossing fibers across the brain. Under such circumstances, even areas of increased fiber density may be identified as showing reduced connectivity if these fibers are disordered compared to healthy controls. To our knowledge, no systematic study of the prevalence of crossing fibers in schizophrenia has been conducted. Studies of other neurological disorders may offer further useful insights into mechanistic accounts of brain structure–function decoupling following pathology. Multiple sclerosis (MS) offers a particularly appropriate example, as it is associated with a known white matter lesion — demyelination of axonal connections — that disrupts www.sciencedirect.com
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inter-regional communication and causes a variety of physical, cognitive and psychiatric symptoms. Consistent with this pathology, dwMRI measures of structural connectivity are almost universally reduced in this patient group although, as with schizophrenia, functional connectivity changes are characterized by a combination of both increases and decreases relative to controls ([34] reviewed in [36]). This de-coupling can co-occur in the same neural system; for example, MS patients with optic neuritis, which is associated with inflammatory demyelination of visual tracts, show increased extrastriate functional connectivity [37]. Similarly, patients with relapsing-remitting MS have shown reduced structural and increased functional connectivity of the default mode network [38]. Increased functional connectivity in MS may be more common in earlier illness stages [34,35]. This finding suggests that functional connectivity increases in this disorder may reflect a plastic and possibly compensatory response to axonal degradation and/or inflammation [39]. Similar findings have been proposed to explain
reports of activation and connectivity increases in the early stages of Alzheimer’s disease [40] and Huntington’s disease [41]. However, not all instances of increased functional connectivity may be adaptive — some studies have found that increased functional connectivity in MS is associated with poorer cognitive performance [34]. In such cases, axonal inflammation and demyelination may dysregulate circuit-level excitatory and inhibitory interactions, leading to a de-differentiated state of neural activity characterized by a breakdown of normally segregated neural activity [42]. Whether a similar mechanism explains functional connectivity increases in schizophrenia is unclear, though evidence that such increases correlate with symptom severity in this patient group [19– 21,22] supports a de-differentiation account over a compensatory mechanism. While MS offers useful insights into functional hyperconnectivity in schizophrenia, the pathogenic mechanisms of the two disorders differ in several ways. In particular, abnormal neurodevelopment is thought to play a prominent role in the aetiology of schizophrenia. The
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Characteristics of structural hub impairment in schizophrenia. (a) Depicts putative structural connectivity hubs of the brain (top), as identified using a hub score that counts the number of hub-like topological properties possessed by each region. Hubs have a score 2 and predominantly localize to association cortices. Bottom depicts frontal hub regions where topological centrality was reduced in anatomical networks of patients with schizophrenia. (b) Depicts maturation of hub connections throughout adolescence. In general, this period is associated with an increase of hub connectivity, particularly to frontal regions. (c,d) Illustration of how topological properties of structural paths can affect functional connectivity. Black dotted lines indicate the path of a primary signal traveling from a source to target region. This path is shown to pass through either a topologically peripheral node (c) or a central node with many connections (d). In the latter case, the high connectivity of the central node offers greater opportunity for the target signal to mix with, or be corrupted by, other incoming traffic. Functional connectivity between source and target is thus expected to be higher for (c) rather than (d). In this manner, structural dysconnectivity of hub regions may actually increase functional connectivity between some brain regions. (a) reproduced from [48]; (b) from [49]; and (c) adapted from [50] with permission. www.sciencedirect.com
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dominant model posits that an early genetic or environmental neural insult derails or interacts with normal brain development, leading to aberrant maturation of laterdeveloping systems. This altered maturation causes dysfunction of critical circuits as they come ‘on-line’, thereby giving rise to clinical symptoms in late adolescence and early adulthood [43]. The last regions of the brain to develop are typically areas of association cortex, which are known to act as connectivity hubs that integrate information from diverse network elements (Figure 2a,b; [44,45,46]). Deficient wiring of these areas can dysregulate communication across widespread areas, causing complex changes in brain dynamics involving both abnormal increases and decreases in functional connectivity. In some cases, the increases may be a compensatory response to dysregulated signaling in specific parts of the network; in other cases, abnormal wiring of structural connections may lead to a breakdown of normally segregated systems [47] and a de-differentiation of neural activity. Two lines of evidence support this hypothesis. First, recent work suggests that schizophrenia is associated with a disproportionate compromise of structural connectivity between network connector hubs located in association cortex (Figure 2a; [48,51]). Second, functional connectivity between two regions is strongly related to characteristics of the structural path that interconnects them, such that nodes linked by a path traversing a highly connected hub node will show reduced functional connectivity compared to a path crossing less connected regions [50]. The high connectivity of hub regions increases the likelihood that any passing signal will be mixed with other incoming traffic, adding noise as messages pass between source and target (Figure 2c,d). An impairment of anatomical connectivity of network hubs may reduce this effect, thus offering a potential mechanism through which reduced structural connectivity may give rise to abnormal enhancements of functional connectivity. Accordingly, computational models have confirmed that structural removal of hub nodes in association cortices yields a complex pattern of increases and decreases in functional connectivity [52]. Analysis of how topological characteristics of structural paths predict functional connectivity increases in schizophrenia will be useful for testing this hypothesis.
Conclusions Increased functional connectivity is a replicated finding in schizophrenia. It occurs in the presence of a general impairment of structural connectivity, and it cannot be explained solely by methodological factors. In this review, we have considered how abnormal neurodevelopment of structural connectivity, particularly hub nodes in association cortex, might give rise to apparent functional hyperconnectivity in patients. Naturally, other factors may play a part. For example, antipsychotic treatment can increase Current Opinion in Neurobiology 2015, 30:44–50
functional connectivity in some circuits (reviewed in [18,21,53]). However, evidence for functional connectivity increases in unmedicated high-risk samples [18,21,54] suggests that medication cannot explain all such reports of functional hyperconnectivity. Other factors, such as differences in head motion [55] and other analysis steps specific to fMRI network studies may also play a role (discussed in [56]). These influences notwithstanding, the evidence discussed here indicates that decoupling of brain network structure and function may represent an intrinsic property of how the disease affects the brain, and draws attention to the need for a greater understanding of how anatomical network topology shapes and constrains brain functional connectivity in both health and brain disorders.
Conflict of interest statement ETB is employed half-time by the University of Cambridge and half-time by GlaxoSmithKline; he holds shares in GSK. The Behavioural & Clinical Neuroscience Institute is supported by the Medical Research Council (UK) and the Wellcome Trust. AF is supported by an Australian Research Council Future Fellowship (ID: FT130100589) and National Health and Medical Research Council grants (IDs: 1050504 and 1066779).
Acknowledgements ETB is employed half-time by the University of Cambridge and half-time by GlaxoSmithKline; he holds shares in GSK. The Behavioural & Clinical Neuroscience Institute is supported by the Medical Research Council (UK) (Grant No. G1000183) and the Wellcome Trust (Grant No. 093875/Z/10/Z). AF is supported by an Australian Research Council Future Fellowship (ID: FT130100589) and National Health and Medical Research Council grants (IDs: 1050504 and 1066779).
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23. Skudlarski P, Jagannathan K, Anderson K, Stevens MC, Calhoun VD, Skudlarska BA, Pearlson G: Brain connectivity is not only lower but different in schizophrenia: a combined anatomical and functional approach. Biol Psychiatry 2010, 68:61-69. First demonstration of increased functional connectivity in the presence of reduced structural connectivity in people with schizophrenia. 24. Zalesky A, Fornito A, Seal ML, Cocchi L, Westin CF, Bullmore ET, Egan GF, Pantelis C: Disrupted axonal fiber connectivity in schizophrenia. Biol Psychiatry 2011, 69:80-89. The first brain-wide dwMRI study of connectivity disturbances in schizophrenia mapped at the level of each pair-wise regional connection. Structural connectivity reductions were widespread. No connectivity increases were found. 25. Fox MD, Zhang D, Snyder AZ, Raichle ME: The global signal and observed anticorrelated resting state brain networks. J Neurophysiol 2009, 101:3270-3283. 26. Wise RG, Ide K, Poulin MJ, Tracey I: Resting fluctuations in arterial carbon dioxide induce significant low frequency variations in BOLD signal. NeuroImage 2004, 21:1652-1664. 27. Birn RM, Diamond JB, Smith MA, Bandettini PA: Separating respiratory-variation-related fluctuations from neuronalactivity-related fluctuations in fMRI. NeuroImage 2006, 31:1536-1548. 28. Van Dijk KRA, Hedden T, Venkataraman A, Evans KC, Lazar SW, Buckner RL: Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. J Neurophysiol 2010, 103:297-321. 29. Murphy K, Birn RM, Handwerker DA, Jones TB, Bandettini PA: The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? NeuroImage 2009, 44:893-905. 30. Saad ZS, Gotts SJ, Murphy K, Chen G, Jo HJ, Martin A, Cox RW: Trouble at rest: how correlation patterns and group differences become distorted after global signal regression. Brain Connect 2012, 2:25-32. Excellent demonstration of how global signal correction can alter group differences in studies of functional connectivity. 31. Yang GJ, Murray JD, Repovs G, Cole MW, Savic A, Glasser MF, Pittenger C, Krystal JH, Wang XJ, Pearlson GD et al.: Altered global brain signal in schizophrenia. Proc Natl Acad Sci U S A 2014, 111:7438-7443. In-depth study of global signal abnormalities in people with schizophrenia and how they impact findings of relative hyper-functional or hypo-functional connectivity. 32. Hahamy A, Calhoun V, Pearlson G, Harel M, Stern N, Atar F, Malach R, Salomon R: Save the global: global signal connectivity as a tool for studying clinical populations with fMRI. Brain Connect 2014 http://dx.doi.org/10.1089/ brain.2014.0244. 33. Jones DK, Knosche TR, Turner R: White matter integrity, fiber count, and other fallacies: the dos and donts of diffusion MRI. NeuroImage 2012 http://dx.doi.org/10.1016/ j.neuroimage.2012.06.081. Excellent review of the limitations associated with dwMRI mapping of axonal tract structure and integrity. 34. Hawellek DJ, Hipp JF, Lewis CM, Corbetta M, Engel AK: Increased functional connectivity indicates the severity of cognitive impairment in multiple sclerosis. Proc Natl Acad Sci U S A 2011, 108:19066-19071. 35. Roosendaal SD, Schoonheim MM, Hulst HE, Sanz Arigita EJ, Smith SM, Geurts JJG, Barkhof F: Resting state networks change in clinically isolated syndrome. Brain 2010, 133:1612-1621. 36. Sacco R, Bonavita S, Esposito F, Tedeschi G, Gallo A: The contribution of resting state networks to the study of cortical reorganization in MS. Multiple Sclerosis Int 2013, 2013:1-7. 37. Gallo A, Esposito F, Sacco R, Docimo R, Bisecco A, Corte Della M, D’Ambrosio A, Corbo D, Rosa N, Lanza M et al.: Visual restingstate network in relapsing-remitting MS with and without previous optic neuritis. Neurology 2012, 79:1458-1465. Demonstration of increased functional connectivity in the visual system of MS patients with optic neuritis. Current Opinion in Neurobiology 2015, 30:44–50
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