Elsevier

NeuroImage

Volume 49, Issue 1, 1 January 2010, Pages 401-414
NeuroImage

Sources of group differences in functional connectivity: An investigation applied to autism spectrum disorder

https://doi.org/10.1016/j.neuroimage.2009.07.051Get rights and content

Abstract

An increasing number of fMRI studies are using the correlation of low-frequency fluctuations between brain regions, believed to reflect synchronized variations in neuronal activity, to infer “functional connectivity”. In studies of autism spectrum disorder (ASD), decreases in this measure of connectivity have been found by focusing on the response to task modulation, by using only the rest periods, or by analyzing purely resting-state data. This difference in connectivity, however, could result from a number of different mechanisms — differences in noise, task-related fluctuations, task performance, or spontaneous neuronal activity. In this study, we investigate the difference in functional connectivity between adolescents with high-functioning ASD and typically developing control subjects by examining the residual fluctuations occurring on top of the fMRI response to an overt verbal fluency task. We find decreased correlations of these residuals (a decreased “connectivity”) in ASD subjects. Furthermore, we find that this decrease was not due to task-related effects, block-to-block variations in task performance, or increased noise, and the difference was greatest when primarily rest periods are considered. These findings suggest that the estimate of disrupted functional connectivity in ASD is likely driven by differences in task-unrelated neuronal fluctuations.

Introduction

An increasing number of neuroimaging studies are using functional MRI (fMRI) to investigate not only task-induced neuronal activation, but also the connections between different brain regions. This estimate of “connectivity” is typically derived by measuring the correlation of time series fluctuations between brain areas. Synchronized fluctuations in the fMRI signal intensity time series can, of course, be task-induced, but also have been shown to occur in the absence of an external stimulus or explicit task, particularly at low temporal frequencies (< 0.1 Hz). It is believed that these signal fluctuations reflect synchronized variations in the neuronal activity of a network of regions. These correlations are often referred to as “functional connectivity,” a phenomenon first studied in fMRI by Biswal et al. (1995) in the motor cortex. Since then a number of studies have identified a consistent set of resting-state networks in motor cortex, auditory cortex, visual cortex, attentional areas and the “default mode network” areas (Damoiseaux et al., 2006, De Luca et al., 2006, Greicius et al., 2003, Raichle et al., 2001). The default mode network (DMN) consists of areas that consistently show deactivations (relative to rest) during a wide range of attention-demanding tasks (McKiernan et al., 2003, Raichle et al., 2001). This network, which includes the medial prefrontal cortex, posterior cingulate/precuneus, and angular gyrus, is of particular interest because it is believed to reflect areas that are more active during rest. Since this network is characterized by decreases in activity during many cognitively demanding tasks, it is also referred to as the task negative network (TNN). In contrast, the attention network consisting of the pre-supplementary motor area, intraparietal sulcus, and superior precentral sulcus is sometimes referred to ask the task positive network (TPN) (Fox et al., 2005, Kennedy and Courchesne, 2008, Raichle et al., 2001).

Changes in functional connectivity have been investigated in numerous psychiatric and neurological disorders, including Alzheimer's disease (Li et al., 2002, Wang et al., 2006), multiple sclerosis (Cader et al., 2006, Lowe et al., 2002), epilepsy (Waites et al., 2006, Whalley et al., 2005), schizophrenia (Bluhm et al., 2007, Garrity et al., 2007, Lawrie et al., 2002, Liang et al., 2006, Zhou et al., 2007), attention deficit hyperactivity disorder (Tian et al., 2006), depression (Anand et al., 2005) and autism spectrum disorders (ASD) (Cherkassky et al., 2006, Just et al., 2004, Kennedy and Courchesne, 2008). For example, some studies have observed an increased functional connectivity in the default mode network in schizophrenia (Zhou et al., 2007), while others have observed a widespread (Liang et al., 2006) or frontotemporal (Lawrie et al., 2002) decrease in connectivity in this disorder. Likewise, a general theory of “underconnectivity” in autism has become prevalent in the literature (Brock et al., 2002, Just et al., 2007). It offers a potential explanation for many ASD characteristics and particularly an afflicted individuals' inability to integrate information. Though underconnectivity in autism is not ubiquitous, thalamo-cortical connectivity having been shown to be greater in ASD (Mizuno et al., 2006, Turner, 1999), it has been observed during sentence comprehension, verbal working memory, semantic judgments of sentences, executive processing on the tower of London task, visuomotor coordination, emotion perception and other executive function tasks (Just et al., 2007, Just et al., 2004, Kana et al., 2006, Kleinhans et al., 2005, Koshino et al., 2005, Villalobos et al., 2005, Welchew et al., 2005). Decreased connectivity in ASD has also been found using data from subjects during a resting state (Cherkassky et al., 2006, Kennedy and Courchesne, 2008).

The difficulty with interpreting the results of fMRI studies investigating functional connectivity is that the measures of connectivity are computed in a number of different ways and under a variety of conditions (also, see Horwitz, 2003). The term “functional connectivity” has most generally been defined as the “temporal correlation between remote neurophysiological events” (Friston et al., 1993). This definition, however, has been applied to both resting and task data. Functional connectivity has been computed from data acquired during task performance to determine the synchrony of brain networks while engaged in a task (Bokde et al., 2001, Buchel and Friston, 1997; Bullmore et al., 2000, Hampson et al., 2002). For example, in one of the early investigations of functional connectivity in ASD, Just et al. (2004) focused on the correlation of task effects by only using the task blocks in their analysis. Conversely, Cherkassky et al. (2006) looked at functional connectivity in ASD using only the rest blocks of datasets from six different block-design experiments (including that from Just et al., 2004 aforementioned). More recently, Kennedy and Courchesne (2008), investigated the functional connectivity in ASD using continuous resting-state data. The analysis of functional connectivity is particularly well suited to resting data, where an expected task response is not known (Biswal et al., 1995, Cordes et al., 2000, Lowe et al., 1998). In addition, resting-state designs are attractive for patient studies since they require no task compliance and hence minimal effort by the subject. Another alternative measure of connectivity that has been proposed is to compute the correlation between residual fluctuations in task activation datasets after task effects have been regressed out. This was done by Villalobos et al. (2005), where a box-car nuisance regressor was applied to a dataset with mixed simple (index-only) and complex (pressing fingers in a six-digit sequence) finger tapping tasks. The resulting residual time series may reflect the variability between the two tasks, the trial-to-trial variability within each task, spontaneous neuronal fluctuations, and other sources of noise, making the measured functional connectivity more, but likely not entirely, driven by task-unrelated fluctuations. In another task regression technique, Fair et al. (2007), removed the task response from an event-related design using a deconvolution approach. This technique should model the task response more effectively than a box-car regression, but trial-to-trial task variability would still be present in the data.

Because of the variety of techniques used for measuring functional connectivity, it is often difficult to draw direct comparisons between studies. Which fluctuations in the time series are driving the measure of connectivity? What is the source of the time series fluctuations that result in areas being functionally “connected”? Perhaps more importantly, when a change in functional connectivity is observed in a particular disorder, what exactly is it that changes? For example, a decreased connectivity (or, more precisely, a decrease in the temporal correlation between two or more regions) in a patient group could be due either to increased noise or to decreased “signal” (correlated fluctuations) in that population. Differences in connectivity observed during a task can also be influenced by variations in the task performance, a particular concern for studies involving patients that may have an impaired ability to perform the task. In this case, connectivity (correlation) differences may simply reflect task-induced activation differences.

In the present study, we investigate the difference in functional connectivity between adolescents with high-functioning ASD and typically developing (TD) control subjects during an overt verbal fluency task designed to investigate deficits in language and executive function. Such deficits are pervasive characteristics of autism (Howlin, 2003, Kenworthy et al., 2005, Lord and Paul, 1997, Muller et al., 1998, Tager-Flusberg, 2003, Tager-Flusberg, 2004). The differences in activation observed between ASD and TD subjects performing this task, and the resulting neuroscientific and clinical interpretation, are the focus of a separate study. The focus of this study is to delve more deeply into measures of functional connectivity, particularly from a methodological point of view. We compare functional connectivity measures obtained during task modulation, and those obtained from residual fluctuations occurring on top of the task. The primary goals of this study are: 1) to determine whether differences in functional connectivity in ASD subjects compared to typical controls can be seen in residual fluctuations on top of task modulations; and 2) to determine the sources of these changes in functional connectivity. We investigate the connectivity both between areas active in the fluency task, as well as between regions within the task negative (TNN) and task positive (TPN) networks, which have been implicated in previous studies of functional connectivity in autism (Cherkassky et al., 2006, Kennedy and Courchesne, 2008). Based on previous studies, our hypotheses are that there are task-unrelated fluctuations, occurring on top of the task-induced signal changes, which are correlated between functionally related areas — such as the areas activated during the verbal fluency task and the areas of the task negative (“default mode”) network. Furthermore, based on prior studies of ASD during a resting state and the deficits in the performance of executive function tasks typically observed in ASD (Hill, 2004, Kenworthy et al., 2008, Pennington and Ozonoff, 1996, Sergeant et al., 2002, Turner, 1999), we predict that the correlation of these fluctuations (i.e., the “functional connectivity”) is lower in ASD subjects, and that this difference in connectivity is driven in large part by differences in neuronal activity unrelated to the verbal fluency task performed by the subjects. Although this analysis is applied to a particular disorder, ASD, our techniques to identify the sources of connectivity differences and the insights gained from this investigation should extend more broadly to other studies of functional connectivity.

Section snippets

General scanning information

We scanned a total of 23 adolescent high-functioning ASD male subjects and 20 typically developing (TD) male subjects on a 3 T GE Signa MRI scanner (Waukesha, WI). All ASD subjects met DSM-IV criteria for ASD in the judgment of a clinician or team of clinicians experienced with the assessment and diagnosis of individuals with ASD (American Psychiatric Association, 1994). In addition, all ASD participants also met criteria for an ASD on the Autism Diagnostic Interview — revised (ADI-R, Lord et

Activation results and ROI definitions

The general activation results used to define the ROIs can be seen in Fig. 1. The selectivity (C, L or M) of the active areas are tabulated in Table 2 and can be visualized in Fig. 1. The ROIs for the connectivity analysis were drawn according to the spatial extent of activation seen in Fig. 1.

Connectivity results

Fig. 2 shows the effect of regressing out the task from M1 to M2 and M3. The full BOLD responses to the tasks can be seen in M1, while the residual task-to-task variability in the BOLD responses can be

Discussion

In this study, functional connectivity was computed in a number of different ways. Computing the functional connectivity (correlation) from the signal intensity time course without removing any task-related effects (M1) shows areas with a similar response to the induced task compared to baseline. Differences in correlation of ROI time courses for TD compared to ASD subjects, in this case, could simply reflect the differences in the task-related response. This is similar to a more conventional

Conclusions

In this study, we found strong correlations in the residual fluctuations occurring on top of a task in both TD and ASD subjects. The connectivity maps derived from these residuals are highly similar to maps seen in other resting-state studies. The high correlation of the residuals is likely driven by task-unrelated fluctuations, since the correlation increases when task effects are regressed out and when task blocks are ignored. Furthermore, we find differences in these correlations between two

Acknowledgments

This research was supported by the Intramural Research Program of the National Institute of Mental Health. The authors thank Dr. Gregory Wallace for providing information on clinical assessment and diagnosis.

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