Intrinsic Functional Connectivity As a Tool For Human Connectomics: Theory, Properties, and Optimization Koene R. A. Van Dijk,1,2,3 Trey Hedden,1,2 Archana Venkataraman,4 Karleyton C. Evans,5 Sara W. Lazar,5 and Randy L. Buckner1,2,5,6 1Department of Psychology, Center for Brain Science, Harvard University, Cambridge, Massachusetts 2Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology and 5Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts 3Department of Neuropsychology and Psychopharmacology, Faculty of Psychology, Maastricht University, Netherlands 4Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, Massachusetts and 6Howard Hughes Medical Institute, Cambridge, Massachusetts Submitted 24 August 2009 accepted in final form 26 October 2009 Van Dijk KR, Hedden T, Venkataraman A, Evans KC, Lazar SW, Buckner RL. Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. J Neu- rophysiol 103: 000���000, 2010. First published November 4, 2009 doi:10.1152/jn.00783.2009. Resting state functional connectivity MRI (fcMRI) is widely used to investigate brain networks that exhibit correlated fluctuations. While fcMRI does not provide direct measure- ment of anatomic connectivity, accumulating evidence suggests it is sufficiently constrained by anatomy to allow the architecture of dis- tinct brain systems to be characterized. fcMRI is particularly useful for characterizing large-scale systems that span distributed areas (e.g., polysynaptic cortical pathways, cerebro-cerebellar circuits, cortical- thalamic circuits) and has complementary strengths when contrasted with the other major tool available for human connectomics���high angular resolution diffusion imaging (HARDI). We review what is known about fcMRI and then explore fcMRI data reliability, effects of preprocessing, analysis procedures, and effects of different acquisition parameters across six studies (n 98) to provide recommendations for optimization. Run length (2���12 min), run structure (1 12-min run or 2 6-min runs), temporal resolution (2.5 or 5.0 s), spatial resolution (2 or 3 mm), and the task (fixation, eyes closed rest, eyes open rest, continuous word-classification) were varied. Results revealed moder- ate to high test-retest reliability. Run structure, temporal resolution, and spatial resolution minimally influenced fcMRI results while fix- ation and eyes open rest yielded stronger correlations as contrasted to other task conditions. Commonly used preprocessing steps involving regression of nuisance signals minimized nonspecific (noise) correla- tions including those associated with respiration. The most surprising finding was that estimates of correlation strengths stabilized with acquisition times as brief as 5 min. The brevity and robustness of fcMRI positions it as a powerful tool for large-scale explorations of genetic influences on brain architecture. We conclude by discussing the strengths and limitations of fcMRI and how it can be combined with HARDI techniques to support the emerging field of human connectomics. I N T R O D U C T I O N The human brain is organized into parallel, interacting systems of anatomically connected areas. Understanding the functions of these systems and differences associated with atypical development and degenerative processes requires methods to measure connectivity and how it varies from one person to the next. Because of these needs, there has been great interest in developing techniques to measure connectivity in the human brain and to link the measured connectivity patterns to information about cytoarchitectonic boundaries and func- tional response properties. The present paper focuses on one such technique���functional connectivity MRI (fcMRI)���that provides indirect information about structural connectivity pat- terns that define brain systems.1 Expanding on related approaches (e.g., Friston 1994 Friston et al. 1993 Gochin et al. 1991 Horwitz et al. 1984 McIntosh 1999 Nunez et al. 1997), fcMRI is based on the observation that brain regions show slow, spontaneous fluctuations when measured using blood-oxygenation-level-dependent (BOLD) imaging methods (Biswal et al. 1995). Regions within anatom- ically connected brain systems, such as the motor and visual systems, are strongly and selectively correlated, suggesting the potential to use such correlations to infer the anatomic connec- tivity of brain systems. The present paper reviews the theory and methods of fcMRI (including its limitations) and then presents the results of six novel empirical studies that charac- terize parameters for its optimal use. Functional connectivity MRI and its relation to alternative techniques Until recently, the majority of information about the ana- tomic connectivity of the human brain came from studies of non-human primates using invasive tracing techniques (Felle- man and Van Essen 1991 Jones and Powell 1970 Mesulam 2000 Ungerleider and Haxby 1994) and inferences from hu- man brain lesions (e.g., Geschwind 1965). Postmortem tracing techniques in humans are feasible but have met with limited success because they are only able to trace connections span- ning short distances (e.g., Burkhalter et al. 1993). For these Address for reprint requests and other correspondence: R. L. Buckner, Harvard University���Center for Brain Science, Northwest Bldg., Rm. 280.05, 52 Oxford St., Cambridge, MA 02138 (E-mail: randy_buckner@harvard.edu). 1 Functional connectivity is operationally defined as temporally correlated remote neurophysiological events (Friston 1994 see also Horwitz 2003) and does not explicitly require that one event is influencing the other (e.g., 2 physiological events can be correlated because they are triggered by common stimulus or neuromodulatory events). The term effective connectivity is often evoked when the correlated events can be demonstrated to arise from a direct influence. While we adopt the agnostic term functional connectivity MRI (fcMRI) in this paper, accumulating evidence suggests that intrinsic activity correlations observed by fcMRI analysis are constrained by anatomic connec- tivity and may be a viable tool for making inferences about stable organiza- tional properties of neural systems. fcMRI acquired at rest is also sometimes referred to as resting state fMRI (R-fcMRI) or discussed in terms of the networks it identifies���resting state networks (RSNs). J Neurophysiol 103: 000���000, 2010. First published November 4, 2009 doi:10.1152/jn.00783.2009. 1 0022-3077/10 $8.00 Copyright �� 2010 The American Physiological Society www.jn.org AQ: A Fn1 AQ: A tapraid4/z9k-neurop/z9k-neurop/z9k00110/z9k9867d10z xppws S 1 12/1/09 5:05 MS: J00783-9 Ini: lr

reasons, noninvasive human techniques based on MRI have become a focus for development even through significant technical hurdles present limitations on resolution and sensi- tivity. Two main sets of MRI techniques are commonly used for noninvasive mapping of human brain connectivity: diffusion- based methods including diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI) and indi- rect methods based on functional correlations including fcMRI. Additional methods based on measuring the distant effects of neural stimulation have also been applied successfully but are not as common (Pascual-Leone et al. 2000 Paus et al. 1997). Diffusion-based methods exploit the property that water molecules move along white-matter bundles faster than they do against them. By measuring water diffusion in multiple direc- tions, the location and trajectories of axonal bundles can be estimated and the pathways reconstructed using MRI (Basser et al. 2000 Conturo et al. 1999 Le Bihan et al. 2001 Mori et al. 2002). The major strength of diffusion techniques is that they directly measure anatomic structure. A limitation is their in- ability to resolve complex fiber organization, such as crossing fibers. This limitation has been partially overcome by advances in HARDI techniques including diffusion spectrum imaging (DSI) and Q-Ball methods (Tuch et al. 2002 Wedeen et al. 2005) and by advanced tractography algorithms such as those based on probabilistic estimates (Jbabdi et al. 2007). Nonethe- less, currently applied diffusion techniques sometimes fail to detect known pathways, suggesting they are prone to type-II errors (e.g., Sherbondy et al. 2008). Connectivity techniques based on functional correlation offer an orthogonal set of strengths and weaknesses when compared with diffusion techniques, a point we will return to in the discussion. Functional connectivity is based on the observation that distant brain regions often show strong correlations in their activity levels. Originally observed using positron emission tomography (PET) measures of between-subject variation (e.g., Horwitz et al. 1984 see Vogt et al. 2006 for an interesting recent application), functional correlations between widely distributed brain re- gions are consistently observed in analyses of fMRI time series data (see Fox and Raichle 2007 for review). As Friston (1994) noted in his comprehensive exposition of functional connectivity, the many repeated scans that can be acquired in quick succession with fMRI provide a rich source of information about correlated activity fluctuations. Biswal et al. (1995) were the first to demonstrate the potential of fcMRI using intrinsic activity correlations. They showed that the BOLD signal time course from a region in the motor cortex was strongly correlated with the contralat- eral motor region and midline regions within the motor system. The coherent fluctuations were readily observed within individual participants, indicating that the method is highly sensitive and raising the possibility of measuring individual differences. Figure 1 displays a replication of the functional correlations demonstrated by Biswal et al. (1995) in a single participant. A high correlation between left and right motor cortices is evident as is minimal correlation between motor cortex and visual cortex, demonstrating the specificity of functional correlation measures. Unlike earlier approaches to functional connectivity that focused on stim- ulus-evoked modulations, the correlated fluctuations ob- served by Biswal et al. (1995) were manifest while partic- ipants rested passively without any detectable movement, suggesting the fluctuations were driven by intrinsic activity events constrained by anatomy. Reinforcing this possibility, Koch et al. (2002) combined diffusion-based and resting- state functional methods to provide initial evidence that BOLD signal correlations between regions are mediated by direct and indirect anatomic projections. Properties of functional connectivity MRI Since the seminal observation of Biswal and colleagues, multiple functional systems have been demonstrated to exhibit correlated fluctuations at rest including the visual and auditory systems (Cordes et al. 2000 Damoiseaux et al. 2006 De Luca et al. 2006 Hunter et al. 2006 Lowe et al. 1998 Van de Ven et al. 2004), the default network and the medial temporal lobe memory system (Buckner et al. 2008 Fox et al. 2005 Fransson 2005 Fransson and Marrelec 2008 Greicius et al. 2003, 2004 Vincent et al. 2006), the language system (Hampson et al. 2002), the dorsal attention system (Fox et al. 2005, 2006), and the frontoparietal control system (Vincent et al. 2008). Several reports have used data-driven approaches based on indepen- dent component analysis (ICA) to define multiple large-scale systems with a considerable degree of consistency between datasets (Beckmann et al. 2005 Damoiseaux et al. 2006 De Luca et al. 2006 Smith et al. 2009). Analyses targeting limbic and subcortical structures including the cingulate (Margulies et al. 2007), hippocampal formation (Kahn et al. 2008), thalamus (Zhang et al. 2008), striatum (Di Martino et al. 2008), amyg- dala (Roy et al. 2009), and the cerebellum (Habas et al. 2009 Krienen and Buckner 2009 O���Reilly et al. 2009) have dem- onstrated segregated pathways. While the preceding studies were primarily based on corre- lated BOLD fluctuations that emerge spontaneously during awake rest, functional networks also show synchronous fluc- tuations during task paradigms and in varied states of con- sciousness. For instance, the sensory-motor system shows spontaneous correlation during rest (Biswal et al. 1995) and, FIG. 1. The basis of functional connectivity MRI (fcMRI). Low-frequency spontaneous fluctuations in the blood-oxygenation-level-dependent (BOLD) signal are correlated over time between regions within the same brain systems. Examples from a single subject depict correlated spontaneous fluctuations between left and right motor cortex (top) and the absence of correlation between motor and visual regions (bottom). fcMRI methods make use of the selective correlations between regions to map the organization of brain sys- tems. L, left R, right MOT, motor cortex VIS, visual cortex. 2 VAN DIJK, HEDDEN, VENKATARAMAN, EVANS, LAZAR, AND BUCKNER J Neurophysiol ��� VOL 103 ��� JANUARY 2010 ��� www.jn.org F1 C O L O R tapraid4/z9k-neurop/z9k-neurop/z9k00110/z9k9867d10z xppws S 1 12/1/09 5:05 MS: J00783-9 Ini: lr

under certain conditions, increased correlation during finger tapping (Newton et al. 2007 but see Amann et al. 2009). The default network shows strong correlations during task condi- tions but at an attenuated level relative to rest (Fransson 2006). Spontaneous correlations persist during sleep (Fukunaga et al. 2006 Horovitz et al. 2008, 2009 Larson-Prior et al. 2009) and anesthesia (Greicius et al. 2008 Vincent et al. 2007), suggest- ing they reflect, to a large degree, intrinsic processes. Stage of sleep (Horovitz et al. 2009) and level of sedation (Vincent et al. 2007 see their supplementary materials) modulate intrinsic activity correlations, suggesting that state affects functional connectivity results. The common procedure of measuring synchronous fluctuations at rest does not imply that rest states have a special status that maximizes the presence of coherent fluctuations in all systems. However, because spontaneous fluctuations are often measured at rest, the method is frequently referred to as ���resting state��� fcMRI (R-fcMRI) and the iden- tified brain networks as ���resting state networks��� (RSNs). Mea- surement during rest or passive fixation has the advantage of minimizing task-evoked BOLD fluctuations and is quite simple to implement. The observation that spontaneous correlations are present ubiquitously across brain systems and persist in multiple states of consciousness raises the question of their origin and, spe- cifically, whether they provide indirect information about an- atomical connectivity (see Damioseaux and Greicius 2009 for review). Functional correlations between cortical regions might arise from common neuromodulatory input from ascending neu- rotransmitter systems or thalamo-cortical afferents (Friston 1994). Several lines of evidence indicate that intrinsic BOLD fluctu- ations are constrained by anatomic connectivity. First, patterns of spontaneous synchronous fluctuations in the oculomotor system of the macaque monkey show high overlap with both evoked responses during an eye-movement task and with an anatomical network revealed by retrograde tracer injections into the lateral intraparietal area of the oculomotor system (Lewis and Van Essen 2000 Vincent et al. 2007). Margulies et al. (2009) recently demonstrated correspondance between functional connectivity and monkey tracer injections for four distinct pathways. Second, cortico-cortical axonal pathway densities among regions covering the entire cortex (as measured with diffusion-based imaging) show a significant (but not perfect) relationship to the strength of spontaneous functional correlations among those same regions (Hagmann et al. 2008 Honey et al. 2009). Third, in a case study where spontaneous correlations were assessed before and after a child underwent complete resection of the corpus callosum for the treatment of intractable epilepsy, a significant loss of interhemi- spheric BOLD correlations occurred while intrahemispheric cor- relations remained unchanged suggesting white-matter tracts were the conduits for functional correlations (Johnston et al. 2008). However, there is also evidence that functional connectivity is not merely a reflection of direct structural connections and should not be considered an exact proxy for invasive tracing techniques or human diffusion-based methods. First, while fcMRI results are broadly consistent between passive and active task states, perfor- mance of a task can induce regional variation in correlation strengths indicating that functional connectivity can be modulated despite unchanged structural connectivity (Buckner et al. 2009 Fransson 2006 Hampson et al. 2002, 2004 Hasson et al. 2009 Newton et al. 2007 see also Friston 1994 for an early discussion of the importance of modulating functional connectivity). Provid- ing further evidence for state-dependent influences, several studies have modulated intrinsic activity correlations by manipulating task conditions prior to the rest data epoch (Albert et al. 2009 Hasson et al. 2009 Lewis et al. 2009 Waites et al. 1995) suggesting important components of intrinsic activity may be linked to consolidation. Second, functional connectivity exists between regions that do not display direct anatomic connectivity including right and left primary visual cortex (Vincent et al. 2007) and between the hippocampal formation and certain regions of the dorsal medial prefrontal cortex (Fransson and Marrelec 2008 Greicius et al. 2009). These correlations likely reflect polysyn- aptic connections or common feed-forward projections (from the lateral geniculate nucleus in the instance of primary visual cortex). These collective observations place constraints on interpretation of fcMRI data and suggest limitations of the technique. Several recent mathematical models have begun to explore neural dynamics and propagation properties that might form the basis of intrinsic activity correlations at slow time scales (e.g., Deco et al. 2009 Ghosh et al. 2008 Honey et al. 2007). A particularly clear demonstration that functional correla- tions are constrained by anatomy and also that they reflect polysynaptic connections arises from the study of cerebro- cerebellar circuits (Allen et al. 2005 Habas et al. 2009 Krienen and Buckner 2009 O���Reilly et al. 2009). The cerebro- cerebellar system is an excellent target because its long-range polysynaptic connections are characterized by three relevant properties. First, cortical regions project to the contralateral cerebellum via the pons and afferents that cross through the deep cerebellar nuclei and the thalamus. Second, direct projec- tions do not exist between the cerebral cortex and the cerebel- lum they must traverse either one (efferents) or two (afferents) synapses. Finally, cerebro-cerebellar connections are organized as closed, independent circuits. Cortical regions receive input from the same cerebellar regions that they project to (Kelly and Strick 2003 Middleton and Strick 2000). This polysynaptic connectional architecture is thus well suited to test the speci- ficity of fcMRI. Using cerebro-cerebellar circuitry as the target, Krienen and Buckner (2009) observed correlations between motor regions with predicted anterior portions of the cerebellum. Most nota- bly, robust correlations were also observed between anatomi- cally distinct regions of the cerebellum and regions of prefron- tal cortex (see also Habas et al. 2009 O���Reilly et al. 2009). Prefrontal correlations appeared in Crus I and Crus II of the cerebellum (Schmahmann et al. 1999, 2000). Using viral trac- ing studies in the monkey, these cerebellar regions have been shown to project to prefrontal cortex area 46 (Kelly and Strick 2003). The cerebellar connectivity also showed the expected crossed-lateralization in relation to the cerebral cortex with the BOLD fluctuations in the right neocortex preferentially corre- lated with the left cerebellum and vice versa. These results provide strong evidence that spontaneous BOLD fluctuations are constrained by anatomical projections. It is especially compelling in the instance of cerebro-cerebellar circuits as the contralateral connectivity pattern cannot be attributed to arti- facts such as shared vasculature (the cerebellum is supplied by its own major arteries) or head motion. As there are no direct anatomic projections between the cerebral cortex and cerebel- lum, the results indicate that fcMRI reflects polysynaptic ana- 3 INTRINSIC FUNCTIONAL CONNECTIVITY J Neurophysiol ��� VOL 103 ��� JANUARY 2010 ��� www.jn.org tapraid4/z9k-neurop/z9k-neurop/z9k00110/z9k9867d10z xppws S 1 12/1/09 5:05 MS: J00783-9 Ini: lr

tomic connectivity or correlation patterns that emerge from common inputs. Analysis of network properties and graph theory The major use of fcMRI to date has been to identify functional connectivity patterns within and between distinct brain systems. As discussed in the preceding text, tremendous strides have been made through application of fcMRI in this manner. There has also been a recent expansion of fcMRI analysis to include examination of more global properties and metrics of connectivity (see Bullmore and Sporns 2009 for review). Neuroanatomists have long recognized that convergence of information is a particular challenge for neural circuitry be- cause it opposes the pressure to segregate information process- ing across specialized brain systems (Jones and Powell 1970 Mesulam 1998 Pandya and Kuypers 1969). The anatomic connectivity of the cerebral cortex reflects these opposing demands with certain areas processing highly specialized types of information (e.g., the visual and auditory systems) and other heteromodal association areas serving as integration zones. Mesulam (1998), in a detailed analysis of the issue, referred to these convergence zones that link distributed sources as ���hubs��� and ���nexuses.��� A particularly important recent expansion of fcMRI has been to offer insight into the global organizational properties that allow brain networks to efficiently segregate and integrate information processing. The explorations have relied heavily on graph theory. The mathematical field of graph theory allows abstract properties of complex systems, such as brain systems, to be quantitatively characterized and mapped. In doing so, simple metrics can be derived that capture the global tendencies that define normal brain architecture and its variability among subjects. Local (region-by-region) topological properties can also be obtained such as whether individual regions serve as hubs. Within this approach, the organization of the human brain is formally modeled as a complex system with small world properties (Bassett and Bullmore 2009 Bullmore and Sporns 2009 Watts and Strogatz 1998). Functional and struc- tural connectivity between brain regions are examined to de- termine whether there are orderly sets of regions that have particularly high local connectivity (forming families or clus- ters) as well as limited numbers of regions that serve as relay stations or hubs (Sporns et al. 2007). Network properties that include small world features are found in many complex biological and social systems and are believed to increase efficiency of signal propagation and/or communication (for reviews, see Bassett and Bullmore 2006 Bullmore and Sporns 2009 Rubinov and Sporns 2009). For example, recent investigations have applied graph the- ory to fcMRI data by examining functional connectivity be- tween numerous pairs of regions in the cerebral cortex to determine whether there are hubs of connectivity (Achard et al. 2006 Buckner et al. 2009), paralleling earlier analyses of invasive tract tracing (Sporns et al. 2007) and diffusion-based (Hagmann et al. 2008) data. What has emerged from the fcMRI investigations is a map of the heteromodal cortical regions that are nexuses of connectivity defined specifically by their dis- proportionate tendency to have high numbers of widespread cortical connections (Buckner et al. 2009). Hubs may function to minimize wiring and metabolism costs by providing a limited number of distant connections that integrate local networks (Bassett and Bullmore 2006). Relevant here is the potential of graph theoretical analysis to characterize global and local properties of brain networks in ways that are not captured by focusing on individual brain systems (e.g., rest- state networks) or distinct patterns of connectivity. As another example, Fair et al. (2009) combined fcMRI and graph theory to show that functional connectivity within a frontoparietal network is present in early childhood but that connection strength continues to develop with increasing age, thereby indicating that fcMRI may make it possible to track microstructural maturation during development (see also Fair et al. 2008 Kelly et al. 2009). In addition, their results indicate that reductions in short-range connections occur with a con- comitant strengthening in long-range connections, potentially caused by synaptic pruning and increased myelination of long- range fibers (Fair et al. 2007a). One might imagine that disturbances of brain function in neuropsychiatric developmen- tal disorders (e.g., autism and schizophrenia) could arise from general tendencies to over- or under-connect networks throughout the brain in addition to disturbances that affect particular systems. Clinical applications Potential clinical applications of fcMRI emerged shortly after the development of the technique in normal participants (Haughton and Biswal 1998). The most basic idea is to use the strength of correlations between functionally coupled regions as a marker of brain system integrity. This approach has been surprisingly powerful for detecting differences in neurological and psychiatric disorders. In a particularly influential demon- stration, Greicius et al. (2004) showed that functional connec- tivity within the default network is disrupted in patients with Alzheimer���s disease (AD) as compared with normal older controls (see also Sorg et al. 2007 Wang et al. 2007). Con- nectivity disruptions were further detected in mild cognitive impairment (MCI) (Zhou et al. 2008) and in cognitively normal older individuals who harbor the pathology of AD (Hedden et al. 2009 Sheline et al. 2009). These observations indicate that the method is sensitive and of potential diagnostic value. Functional disruption has now been reported for a number of neuropsychiatric disorders including autism (Cherkassky et al. 2006 Kennedy and Courchesne 2008), attention deficit hyper- activity disorder (Uddin et al. 2008), depression (Anand et al. 2005, 2009 Greicius et al. 2007), and schizophrenia (Bluhm et al. 2007 Garrity et al. 2007 Whitfield-Gabrieli et al. 2009 Zhou et al. 2007 see Calhoun et al. 2009 Greicius et al. 2008 for reviews). Typical aging in the absence of disease has also been demonstrated to correlate with changes in functional connectivity (Andrews-Hanna et al. 2007 Damoiseaux et al. 2008 Meunier et al. 2009). On the one hand, the detection of dysfunction across a wide range of disorders and in aging suggests the technique is highly sensitive. On the other hand, the generality of detectable deficits raises the question of whether disruption of large-scale brain systems is a common outcome of many underlying processes and, ultimately, whether fcMRI will be sufficiently specific to be clinically useful. This is an open question. In a provocative recent study, Seeley et al. (2009) showed that distinct degenerative neurological diseases including AD, semantic dementia, and frontotemporal demen- 4 VAN DIJK, HEDDEN, VENKATARAMAN, EVANS, LAZAR, AND BUCKNER J Neurophysiol ��� VOL 103 ��� JANUARY 2010 ��� www.jn.org tapraid4/z9k-neurop/z9k-neurop/z9k00110/z9k9867d10z xppws S 1 12/1/09 5:05 MS: J00783-9 Ini: lr

tia show network disruption that maps to distinct brain systems defined by fcMRI. Another class of clinical application for fcMRI is in presur- gical planning. Treatments for epilepsy and brain tumors may involve the neurosurgical removal of brain tissue. Maps of the locations of functioning brain systems and knowledge about the lateralization of language function in individual patients are critical information that allows the surgeon to maximize the size of the resection while minimizing the damage to eloquent cortex. Two independent groups have recently demonstrated the feasibility of mapping functional systems in preoperative patients using fcMRI (Liu et al. 2009a Shimony et al. 2009). Of importance, presurgical mapping using fcMRI can be per- formed while participants are at rest or under light anesthesia. In addition, given that multiple brain systems have been demonstrated to be lateralized using fcMRI (e.g., Fox et al. 2006 Liu et al. 2009b H. Yan et al. 2009), fcMRI-enabled presurgical mapping has the potential to replace more invasive alternatives for determining language lateralization. Physiological noise, anticorrelations, and optimization There are several methodological issues associated with the application of fcMRI that merit further research and clarifica- tion. For example, despite remarkable stability of functional connectivity estimates across studies, formal tests of reliability of fcMRI measures have been scarce. Shehzad et al. (2009) reported moderately high reliability in a systematic investiga- tion of within- and between-subject reliability. Their results further demonstrated that the strongest correlations were also the most reliable, positive correlations were more reliable than negative correlations, and mean correlations computed at the group level exhibited higher reliability than within-subject correlations consistent with the increased signal-to-noise levels afforded by signal averaging. However, Honey et al. (2009) raised questions about fcMRI reliability by comparing the correlation strengths for large numbers of region pairs between multiple data sets in the same subject. They found low to moderate reliability within subjects (e.g., r 0.38 to r 0.69). In a recent study, Meindl et al. (2009) reported high reproduc- ibility of core components of the default network across three scan sessions but lower reproducibility of correlations for superior frontal gyrus. Moreover they showed that reproduc- ibility between sessions was comparable to scans acquired within the same session. Liu et al. (2009b) observed moderate within-subject correlations across sessions for fcMRI estimates of brain asymmetries (r 0.58 to r 0.79). Zuo et al. (2009) recently showed moderate to high reliability of amplitude measures of spontaneous low-frequency fluctuations on which fcMRI estimates are based. Another issue is that raw BOLD signal time courses are noisy due to scanner artifacts, participant motion, and physio- logical sources such as cardiac and respiratory cycles. As a result of the need to reduce spurious noise, multiple processing steps are typically conducted to increase signal to noise (e.g., spatial smoothing), isolate signal components most relevant to fcMRI (e.g., temporal filtering), and remove signal contribu- tions from motion and physiological noise (e.g., through re- gression of white-matter, ventricle, and whole-brain signals). Each of these steps raises potential interpretative issues and opportunities for methodological optimization. The BOLD fluctuations that most consistently produce cor- relations within functional networks occur within a range of 0.01���0.08 Hz, corresponding to a cycle repetition time of 12.5���100 s (Biswal et al. 1995 Cordes et al. 2000 De Luca et al. 2006 Fransson and Marrelec 2008 Lowe et al. 1998 Wu et al. 2008 Zuo et al. 2009). Therefore the signals of interest are in the low-frequency spectrum and application of a low- pass filter (e.g., retaining frequencies 0.1 Hz) as a prepro- cessing step is aimed at removal of higher frequencies. How- ever, a low-pass filter will not be effective in removing signals faster than the Nyquist frequency (equal to half of the sampling rate) and slower than the band-pass cut-off, which may be aliased into the retained frequency spectrum. In this respect physiological noise, especially low-frequency components re- lated to respiration, is a particular concern (Birn et al. 2006, 2008a,b Chang et al. 2009 Shmueli et al. 2007 Van Buuren et al. 2009 Wise et al. 2004). While breath-to-breath effects occur with a frequency of 0.3 Hz and are possibly removed by the band-pass filter, variations over time in breathing rate typically occur at much slower frequencies ( 0.03 Hz) (Birn et al. 2006). These sources of noise may have a global effect across the brain and inflate estimated correlations between brain regions if not properly addressed. Without addressing global influences, fluctuations across all regions tend to show positive correlation even between regions unlikely to be ana- tomically or functionally connected (e.g., primary visual and primary auditory cortex). Several ways to minimize unwanted physiological variation from BOLD data have been proposed. Some methods utilize fast sampling rates (e.g., TR 250 ms) at the expense of brain coverage when using conventional BOLD sequences (Chuang and Chen 2001). Other methods use postprocessing to isolate cardiac and respiratory signals (simultaneously recorded dur- ing image acquisition) and incorporate these signals as null regressors in fcMRI analytic models (Birn et al. 2008b Chang and Glover 2009a,b Chang et al. 2009 Glover et al. 2000 Lund et al. 2006). Additional commonly used null regressors in fcMRI analyses include signals averaged over the ventricles, the deep cerebral white matter, and the whole brain (global signal). Removal of signal from the ventricles and white matter is motivated by the fact that these regions contain a relatively high proportion of noise caused by the cardiac and respiratory cycles (Dagli et al. 1999 De Munck et al. 2008 Lund et al. 2006 Windischberger et al. 2002). Furthermore, it is assumed that physiological sources will cause the same pattern of activity over time in affected voxels of the brain (although not necessarily at the same magnitude) (Macey et al. 2004). One way to counteract these global effects is by regression of the whole-brain signal, a method also referred to as ���regression of the global signal,��� ���global signal normalization,��� or ���orthogo- nalization of the global signal.��� The use of whole-brain signal regression has presented challenging interpretive issues. The whole-brain signal is de- fined as the time course of the average signal intensity within the brain and is typically removed by regression from each voxel���s time series, after which the residual time series are used for further analysis (Desjardins et al. 2001). This method of removal of the whole-brain signal has recently been the subject of scrutiny because, in addition to its intended purpose of removing noise, whole-brain signal regression is associated 5 INTRINSIC FUNCTIONAL CONNECTIVITY J Neurophysiol ��� VOL 103 ��� JANUARY 2010 ��� www.jn.org tapraid4/z9k-neurop/z9k-neurop/z9k00110/z9k9867d10z xppws S 1 12/1/09 5:05 MS: J00783-9 Ini: lr