Using ICA, we were able to filter out noise/artifactual components of the fMRI signal to identify the anatomical components of putative brain networks involved in WM based on their synchronous activation. We were also able to examine differences in the functioning of these networks in patients with schizophrenia compared to healthy controls. Our results confirmed our hypotheses, as we found strong differences in C23, a network of WM regions (DLPFC & IPL) and C4 (cerebellum), engaging motor-related regions. Significant differences were also seen in networks (C15, C18, C20, and C24) spanning brain regions known to be associated with the DMN. The number of networks shown to have significant between-group differences suggests that the cognitive pathophysiology of schizophrenia is widespread. Furthermore, this dysfunction extends to the DMN and the number of networks that span its associated regions provide some support for the idea that this network is not singular, but a conglomeration of multiple subnetworks that work in conjunction with one another (Uddin, et al. 2009
In regards to the manipulation and storage of WM items, C23 is considerably significant since it engages both the DLPFC and IPL. Studies have shown that the DLPFC is tightly linked to the ‘on-line’ maintenance of WM items and that dysfunction in this area is prominent in patients with schizophrenia (Goldman-Rakic 1994
) and their first degree relatives (Seidman, et al. 2006
). The IPL has been known to be intricately connected to the DLPFC (Cole and Schneider 2007
) and several studies have focused on the relationship between these two regions in modulating working memory processes (Barch and Csernansky 2007
; Calhoun, et al. 2006a
; Kim, et al. 2003
). Other regions were implicated as well, including the medial frontal gyrus and some portion of the anterior cingulate. These regions are known to be implicated in high-level executive functions and decision-related processes (Talati and Hirsch 2005
), and might assist in the storage and retrieval of WM items. The network of regions associated with C4 was predominantly cerebellar. The cerebellum is known to assist in the smooth coordination of complex motor-activity and its activation could be a result of the button presses associated with the retrieval and recognition of WM items. More subtle differences in this brain region have been posited (Schlosser, et al. 2003
), where schizophrenia patients showed reduced connectivity between the prefrontal and cerebellar pathways during a WM task. This finding adds some support to the notion that schizophrenia patients might suffer from a dysfunction in the connectivity of these two networks during WM processes.
Our 3-Way ANCOVA showed that C23 was affected by both G×P and G×L interactions, but not the G×L×P interactions. The lack of significance for the G×L×P could suggest that differences in schizophrenia patients which stem from the encoding and probing of WM items might not be significantly related to the modulation of WM processes as the number of items increases. The G×P interactions were driven almost entirely by the probe phase, which was also the case for any component that had significant interactions of this sort. Whether this is partially due to the fact that the probe phase was considerably longer is difficult to tell. However, the DLPFC and IPL have been consistently shown to be linked to WM dysfunction in schizophrenia and our results suggest that this relationships may be stronger during the recognition and retrieval of WM items, which is associated with the probe phase of the paradigm. For the G×L interaction, the medium WM load was found to be the most significant, counter to the idea that higher WM loads would better extract these differences. However, our findings are consistent with a recent schizophrenia WM study (Potkin, et al. 2008
), which utilized data from the same fBIRN collaboration and found that the medium WM load was most responsible for significant between-group differences in the DLPFC. Their conclusion was that these differences are more strongly related to the “inefficiency” of this brain region that might not be directly caused by increases in WM load.
A significant number of fMRI studies in schizophrenia have now focused on the DMN and its potential dysfunction in schizophrenia (Calhoun, et al. 2006b
; Garrity, et al. 2007
; Pomarol-Clotet, et al. 2008
; Whitfield-Gabrieli, et al. 2009
). Our current results support this claim as the four negatively modulated networks we found were comprised entirely of regions hypothesized to be engaged in the DMN. These included the medial prefrontal cortex (C18 & C24), ventral anterior cingulate (C18), parahippocampus (C20), posterior cingulate (C15, C18, C20, & C24) extending to the precuneus (C15), and some regions of the lateral parietal cortex (C24) (Buckner, et al. 2008
). The fact that these networks were not conglomerated into a single network using ICA suggests that the DMN might consist of multiple networks that work in conjunction with one another to perform complex tasks such as introspection, goal-planning, and general non-task oriented activities. Furthermore, these four networks have a single common region in the PCC, which Buckner et. al suggested could modulate different subsystems within the DMN. However, further analyses would be needed in order to determine if these networks have specific connectivity relationships with one another, which our current study cannot provide.
Our 3-Way ANCOVA of the four networks associated with the DMN showed some interesting trends that may pertain to the dysfunction of WM processes in schizophrenia. The DMN, more than other temporally coherent resting state networks, has been shown to share a continuous competitive relationship with networks necessary for task completion (Broyd, et al. 2008
). Further, the degree of deactivation that occurs in this network is influenced by task type (Tomasi, et al. 2006
), task load (McKiernan, et al. 2003
), and schizophrenia diagnosis (Harrison, et al. 2007
). Load sensitivity in the current WM study was evident for C18, where increasing WM load was associated with increased deactivation of the component timecourse. For C18, controls deactivated more than patients across the low and high WM loads for both phases, while patients deactivated slightly more than controls for the medium WM load. However, when covarying for accuracy, G×L interactions were no longer significant; suggesting that significant between-group differences associated with WM load might be task-dependent. For C24, an interesting trend was seen where patients were consistently deactivated more than controls across all WM loads for both phases. However, G×P interactions were only found to be significant for this network and this significance no longer existed when covarying for accuracy. In this context, C18 and C24 seem to represent differences associated to some degree with poor WM performance, which has been shown to be a prominent marker for this illness. It can be hypothesized then that these two networks might play a significant role in WM processes and a dysfunction in the normal deactivation of these networks could impair such processes.
As for C15 and C20, G×P interactions remained significant when covarying for accuracy, indicating that these networks may might represent a more stable functional marker of schizophrenia. Supporting evidence for this comes from a recent study using an identical ICA analysis approach on datasets from the same fBIRN collaboration, which found highly significant differences in schizophrenia patients in an ICA network nearly identical to C15 during the completion of an auditory oddball paradigm (Kim, et al. 2009
). It is also worth noting that these G×P interactions reflect an interesting reversal of brain deactivation between patients and controls during the encode and probe phases. Patients show a greater deactivation for both networks during encoding of WM items, but this is reversed during the probe phase, where controls show a significantly greater deactivation. A similar trend was seen in C4, but in the positive direction where controls were greater during the encode phase, but patients were greater during the probe phase. This phenomenon might be related to an inefficiency in the modulation of task-oriented networks that need to be activated sufficiently in order for proper WM processes to occur. Recent studies have suggested that a dysfunction in the intricate interplay between task-positive and task-negative networks might be associated with schizophrenia (Jafri, et al. 2008
) and our results provide some support for those ideas. These inefficiences might be found in other modalities as well (i.e, MEG, EEG) and future work in this direction could further elucidate the pathophsyiology of schizophrenia (Calhoun, et al. 2008b
There are several limitations to the present study. Schizophrenia is a heterogeneous disorder and biomarkers that identify subgroups or regions with high inter-subject morphologic or functional variability may be obscured by group averaging (Manoach 2003
). Though we attempted to account for the effect of site by including it as a covariate in our ANCOVA models, it is still possible that this factor increased the variability of the data and obscured findings of interest. Another concern is the possibility that patients with schizophrenia may be characterized by reduced attentional control or reduced motivation, and that these attentional or motivational differences may have resulted in the observed deficits in WM performance and functional activation. We attempted to control for this concern by motivating all participants with a monetary reward for each correct trial. Nonetheless, it is possible that remaining differences in attention or motivation could have been responsible for some group differences noted. Intelligence measures were also different for the fBIRN and MCIC collaborations, utilizing the NART and WRAT measurements respectively, and thus we omitted the reporting of these measurements. Finally, we normalized our datasets to an MNI template that might not be sensitive to volumetric differences found in patients with schizophrenia. An averaged brain template, reflective of our population, could allow for a more accurate assessment of the anatomical locations of these functional connectivity differences.