ICA was successfully used to identify resting-state components in healthy controls and patients with schizophrenia and to identify differences in functional network connectivity among these components. We were able to identify several inter-connected networks present during resting and then examine temporal dependencies between them by computing the maximal lagged temporal correlation between the ICA time courses.
The identified resting state networks are included in (a–g). represents the "default-mode" network, which reflects an ensemble of cortical regions typically deactivated during demanding cognitive tasks in fMRI studies (Raichle, et al. 2001
). Using functional connectivity, this network can be conceptualized and studied as a "stand-alone" function or system. Major regions in component A
include the precuneus, anterior cingulate and posterior cingulate gyri. Additional regions include the superior and middle temporal and frontal gyri, the inferior parietal lobule and parahippocampal gyrus. Briefly, these regions are thought to be related to subject’s recognition, social communication, working memory and some auditory demands. The fluctuations in this network have been cited in numerous studies to increase during resting state and suspend during specific goal-directed behaviors (Garrity, et al. 2007
; Raichle, et al. 2001
(in ), in general, represents resting state hemodynamic activity fluctuations in the parietal regions. Along with the parietal lobule, significant regions include postcentral, anterior and posterior cingulate gyri, along with precuneus and middle frontal cortex. In general, the parietal lobule is used during visuo-spatial interaction. Regions present in component C
include major visual cortical areas, such as the occipital gyrus along with activations in other areas such as lingual and fusiform gyri, cuneus and precuneus. Component D
includes regions in frontal, temporal and parietal gyri, while component E
shows covarying regions not only in frontal and temporal gyri, but also in subcortical regions, which include thalamus, caudate, and other basal ganglia regions. The function of the basal ganglia can be described as a brain relay station where commands, such as “stop reading”, get forwarded to the appropriate brain regions for processing. The dominant regions in component D
are related to visual perceptual abnormalities reported in schizophrenia (Levy, et al. 2000
The medial frontal gyrus dominates component F
. Other covarying regions in component F
include anterior cingulate and precentral gyrus as well as superior frontal gyrus. In general, these regions control the brain’s executive functions and have been cited to show abnormalities in schizophrenia brains (Chan, et al. 2006
). Regions in component G
include superior temporal and inferior frontal gyrus, responsible for auditory processing and language comprehension, along with insula which is the affective sensory region. Several studies cite frontal and temporal gyrus as partially responsible for auditory hallucinations found in schizophrenia (Gaser, et al. 2004
Apart from just identifying neural networks present during ‘resting state’, the primary purpose of this paper was to examine functional network connectivity (FNC), or the temporal relationships among the identified components. Connectivity of the ‘default mode’ component (A) with other components was present more consistently in patients than controls. Although we did not explicitly examine connectivity strength in relation to measurements of active psychosis, it is reasonable to speculate that the increased default mode FNC in patients may be the results of distraction due to hallucinatory experiences and/or delusional preoccupations. Increased FNC of the default mode with other components in patients also could indicate greater dependency of brain regions in the default mode network on the function of other neural circuits (or vice versa) during resting state. We also observed directional differences in lag among components (i.e. C→A in patients, while A→C in controls).
There were other group differences in the relationship among component time courses. Although controls showed greater correlations than patients in one functional network (B–G
), more networks existed in which patient correlations were significantly higher than controls. This trend of higher correlation in patients (i.e. ρcontrols
) might be related to the attentional deficits in schizophrenia (Jorm, et al. 2005
). Controls, on the other hand, may have a better ability to persist in a single mental state with patients more variable. This is also consistent with our recent findings showing more rapid fluctuations in the default mode network in patients verses controls (Garrity, et al. 2007
). This idea is indirectly supported by the significant connection B→C
in only controls because the brain regions in component B
have been linked to focused attention and decision making (Paulus, et al. 2002
). In addition, reduced fluctuations in frontal and parietal regions have been attributed as a possible concomitant of deficit symptoms in schizophrenia (Pearlson 2000
). In contrast, brain regions identified in component C
assist in visual image processing and recognition (Kim, et al. 2005
). In particular, fusiform gyrus and other object/face recognition areas are abnormal in schizophrenia (Dickey, etal. 2003
). Therefore, the absence of functional connection between B
may hint higher order control deficits over sensory association process in patients that appears intact in healthy controls, however this would need to be directly tested in a future study.
Furthermore, the B–G
connectivity shows higher correlation in controls than patients, which agrees with previous studies since these components are related to mental timekeeping and self-ordered behavior, commonly disturbed in schizophrenia (Ganzevles and Haenen 1995
). Two of the 4 connections among components in which patients show higher correlation than controls relate to component E
. This is also consistent with previous studies finding abnormal function of basal ganglia regions in schizophrenia minds show dysfunction and decreased activations in the basal ganglia (Gaser, et al.2004
; Menon, et al. 2001
). The increased connectivity of these networks with that depicted in component E
may suggest a need for dependency on other components to make up for the lack of function in component E
The validation of our results through analysis of subsets of data lends additional support to our various conclusions
. The large-scale (54 subjects) and small-scale (15 subjects/trial for 20 trials) analysis of functional connectivity in controls showed very consistent results. The five connectivity networks found in the large-scale analysis of correlation group difference (A
) also manifested significantly in the small-scale analysis of multiple trials; however, the smaller-scale model of the study showed patients connectivity to be more scattered, with more connections occurring between 3 and 18 times, than never or always occurring (0–2 or 19–20 times), while control connectivity was more consistent, with most occurrences either less than 2 times, or greater than 19 times. We had anticipated this greater variance in patterns in schizophrenic subjects on account of the previously reported differences in patterns of brain activity in patients with different symptom profiles (Liddle 1992
). Regardless of the few differences in the large-scale and small-scale analysis of correlation, the consistent similarities suggest this technique may identify important differences between patients and controls, which may also be useful for classification (although further work is needed to test this). Furthermore, the repeatability of results between two diagnostic groups also implies robustness of results, regardless of gender, age or education. Further validation of our results using the resampling technique confirmed that the mean correlation differences between patients and controls were indeed significantly different.
Group difference results for lag calculations () were not analyzed in detail because the lag difference is only meaningful if the correlations between the processes are also significant (i.e. if the shape of the two time courses is similar). Since the combinations with significant lag do not also have significant correlations for either controls or patients (i.e. connections D–E and D–F are not present in or ), the lags differences are not reported.
In summary, we describe a general method for studying network connectivity, which is demonstrated in a study of patients with schizophrenia and healthy controls. In agreement with our hypotheses, patients showed slightly greater number of significant correlation connectivity as controls in 54 subject analysis, as well as in individual runs for the 20 trial validation
Furthermore, patients had higher correlation values than controls in most of the significant functional networks. We also found increased functional network connectivity in patients versus controls with respect to the default mode which has been described as involved in “internal” versus “external” focus (Raichle, et al. 2001
). The increased connectivity of other networks to the default mode network may be related to hallucinations, although future work is needed to confirm this speculation.
An advantage of our approach to study the dependencies between functional networks was that it allowed us to examine weak, but significant, connectivity among strongly connected networks. We plan to explore the covariation between the symptom expression and the outcome measures within the schizophrenia group in future work. Although we used a correlational approach in this paper, one could also use other dependency measures, such as mutual information or Granger causality, to study the differences in FNC in healthy controls versus patients with schizophrenia. Furthermore, structural equation modeling techniques will be considered in future work to study multiple dependencies among networks. We also hope to apply this method to ICA results from EEG data and incorporate the newly derived FNC diagrams with the ones found through fMRI analysis (Calhoun, et al. 2006b
; Liu and Calhoun 2007
). Covariations between ICA components has previously been studied using EEG in the context of a visual task, although in this case short-term covariation was studied (Makeig, et al. 2004
). In summary, we propose a general method for studying functional network connectivity (weak, but significant temporal dependencies between temporally coherent networks) and demonstrate it in a study of patients with schizophrenia and healthy controls. Our approach revealed several novel findings, and may help improve our understanding of schizophrenia as well as other mental disorders.