Functional magnetic resonance imaging (fMRI) has been used for about 15 years, primarily to extract signal from brain regions which are showing blood oxygen level dependent (BOLD) changes in response to a cognitive task. More recently there has been interest in temporally coherent, but not necessarily task-driven activity, derived from fMRI data. Early studies performed using correlation of a seed voxel in rapidly sampled echo planar imaging (EPI) fMRI data revealed a significant degree of low frequency correlations with contralateral motor regions [Biswal et al., 1995
]. These correlations, also present for visual and auditory cortices, appear to be related to both blood flow and to BOLD activity [Biswal et al., 1997
] mostly at lower frequencies [Cordes et al., 2001
]. Subsequently it was learned that whole brain data temporally sampled at a much lower rate also showed similar temporally coherent regions [Lowe et al., 1998
]. There has been some interest in identifying to what degree these correlations are affected by cognitive tasks and previous work suggests that resting correlation are “not affected by tasks which activate unrelated brain regions” [Arfanakis et al., 2000
] although early on Lowe did note that TCN’s show modulation correlated with behavior in certain brain regions [Lowe et al., 2000
]. More recently, Hampson et al. 
showed that task performance was positively correlated with connection between two brain regions both at rest and during a task. It remains to be seen to what degree a task actually can be considered to activate only an isolated brain region.
Beyond correlation, multivariate methods based upon independent component analysis (ICA) have also been applied to measure functional connectivity, and have the advantage of not requiring a seed voxel or temporal filtering [McKeown et al., 1998
]. ICA was developed to solve problems similar to the “cocktail party” scenario in which individual voices must be resolved from microphone recordings of many people speaking at once [Bell and Sejnowski, 1995
]. The algorithm, as applied to fMRI, assumes a set of spatially independent brain networks, each with associated time courses. The model used constrains the fluctuations of each voxel in a given component to have the same time course and thus each component can be considered to reveal a temporally coherent network (TCN).
Since the original observations, there have been multiple studies including manipulations of tasks versus a resting baseline or evaluating changes in the correlations in clinical groups. There is some evidence that the spatial maps reflecting TCNs may be more robust than those estimated during a standard approach based upon the general linear model [Calhoun, in press
]. ICA has been used to identify several TCNs which are present in healthy subjects either at rest [Beckmann et al., 2005
; Kiviniemi et al., 2003
; Van de Ven et al., 2004
] or during the performance of a task [Calhoun et al., 2001a
; McKeown et al., 1998
]. There has also been interest in using TCNs as biological disease markers, e.g., TCNs have been used to distinguish Alzheimer’s disease from healthy aging [Greicius et al., 2004
]. Two TCNs have been previously studied in schizophrenia [Bluhm et al., 2007
; Calhoun et al., 2004a
; Garrity et al., 2007
]; one includes bilateral temporal lobe regions, which have previously been used to discriminate healthy controls from patients with schizophrenia [Calhoun et al., 2004a
]. A second TCN, one of the most studied, includes regions thought to be engaged when the brain is idle, but whose activity decreases on performance of a cognitive task, and is termed the “default mode network” [McKiernan et al., 2003
; Raichle et al., 2001
The default mode network is believed to participate in an organized, baseline “idling” state of brain function that is diminished during specific goal-directed behaviors [Raichle et al., 2001
]. The default mode network has also been shown to decrease in proportion to task difficulty [McKiernan et al., 2003
]. It is proposed that the default mode is involved in attending to internal versus external stimuli and is associated with the stream of consciousness, comprising a free flow of thought while the brain is not engaged in other tasks [Gusnard et al., 2001
] however there are alternative explanations as well [Hampson et al., 2006
]. We reported recently an approach utilizing both temporal lobe and default mode TCNs to differentiate schizophrenia, bipolar disorder, and healthy controls [Calhoun, in press
]. Other than these two TCNs, others have been consistently identi- fied [Beckmann et al., 2005
] but not studied in detail. For clinical studies, the extraction of TCNs during task performance has been suggested as a way to constrain a participant’s behavior beyond just “resting” and also to stimulate the brain with a task that both patients and controls can perform accurately and which is known to elicit robust brain function differences between the two groups [Calhoun, in press
]. However it remains to be seen whether the presence of a task affects the resting state networks in a more pervasive manner. Collection of data during rest in subjects with neuropsychiatric disorders is a useful approach in several regards. First, ill subjects are often unable to perform tasks consistently in the scanner or to fully understand complex instructions. However, at rest, there are no such “task” demands. Second, abnormal task performance often occurs in schizophrenia, due to the cognitive disability associated with the disorder. This is often inevitably confounded with concomitant abnormal brain activation in a “chicken and egg” manner. At rest, when there is no task, this problem can be resolved. Finally, the occurrence of symptoms in the scanner, (for example auditory hallucinations in schizophrenia), is usually thought of as undesirable “noise” during performance of a cognitive task but at rest may actually be contributing useful diagnostic information.
In this work, we attempt to address three key questions. First, we wanted to study how similar TCNs identified during a task were to those identified from resting state data. Second, for networks identified both during a task and at rest, we were interested in assessing to what degree they are modulated spatially and temporally. Finally, we also incorporated a clinical group (patients with schizophrenia) to evaluate whether the same observations regarding task TCNs and resting TCNs held for both patients and controls.
We used ICA to analyze two data sets, one collected during rest and the second during the performance of an auditory oddball task [Kiehl et al., 2005a
] collected on the same set of healthy controls and schizophrenia patients. The oddball task is one which both patients and controls can perform well. In addition, one of the most robust functional abnormalities in schizophrenia manifests as decrease in the temporal lobe amplitude of the “oddball response” in event-related potential (ERP) data [McCarley et al., 1991
]. Similar findings have been shown for fMRI data as well, again particularly in temporal regions [Kiehl and Liddle, 2001
]. For each condition we identified the TCNs and then defined paired TCNs by using spatial cross correlation to identify TCNs which were present in both experiments. We evaluated spatial and temporal differences due to the experiment (with and without a task) and differences between patients and controls.
To summarize the results, we identified the same TCNs in both tasks, the only difference being one TCN found to be present in the resting state, but not in the auditory oddball data. In addition, the oddball task modulated multiple TCNs spatially and temporally, some positively and some negatively. Finally, interesting patient versus control differences were identified in several of these networks as well.