In humans, parietal cortex has traditionally been linked to processing mechanisms involving attention (Corbetta et al., 1998
; Corbetta and Shulman, 2002
; Dosenbach et al., 2007
; Dosenbach et al., 2006
; Rushworth et al., 2001
; Yantis et al., 2002
). Other accounts of parietal cortex function, particularly focused on the left hemisphere, have examined its role in reading (Turkeltaub et al., 2002
), as well as numerosity judgments and arithmetic (Göbel and Rushworth, 2004
; Hubbard et al., 2005
). More recently, there has been a surge in research devoted to understanding the contributions of left lateral parietal cortex (LLPC) to memory retrieval (for review, see Wagner et al., 2005
). The multitude of processing descriptions arising from studies in these domains suggests that distinct regions in parietal cortex might subserve unique functional contributions. As Devlin and Poldrack (2007)
have argued, the success of functional neuroimaging is contingent on the ability to accurately and precisely map function to underlying neuroanatomy. The primary goal of the current study is to better define divisions that exist within LLPC. As a means of identifying potential distinctions, we use a combined resting state functional connectivity MRI (rs-fcMRI) and functional MRI (fMRI) approach. The fMRI data includes studies related to memory retrieval, which will be used to leverage the distinctions seen with rs-fcMRI-based analyses and to better understand how specific LLPC regions contribute to this domain.
Determining the functional-neuroanatomical correlates of memory retrieval has driven a considerable amount of work in cognitive neuroscience. In particular, a great deal of research has been aimed at understanding how humans distinguish between previously experienced information (“old”) and that which is novel (“new”) (Henson et al., 2000
; Konishi et al., 2000
; McDermott et al., 2000
; Wheeler and Buckner, 2003
). Using fMRI, researchers have classified a set of regions in the brain that tend to be more active when human subjects correctly identify an item as old (“hit”) than when a given item is correctly identified as being new (“correct rejection”). This difference in activation has come to be called the “retrieval success effect” and is present in a highly distributed set of brain regions. The most common regions showing retrieval success effects are in lateral parietal cortex (Simons, 2008
), and though this differential activation is typically bilateral, the most robust effects include a large expanse of LLPC (McDermott, 2009
A number of laboratories (Cabeza et al., 2008
; Ciaramelli et al., 2008
; Vilberg and Rugg, 2008
) have begun exploring the role of LLPC contributions to memory retrieval by performing meta-analyses in which data from large numbers of studies involving a variety of different retrieval tasks are analyzed in a common stereotactic space. The anatomical location of foci within LLPC can be differentiated on the basis of responses in paradigms contrasting recollection and familiarity, or source and item memory, as well as a number of other tasks in which old and new information is embedded. A consistent finding across these studies is the presence of a dorsal-ventral distinction in LLPC that appears to dissociate regions near intraparietal sulcus (IPS) involved in familiarity judgments (dorsal) and regions near angular gyrus involved in recollection (ventral) (Henson et al., 1999
; Wheeler and Buckner, 2004
Mapping estimates of recollection and familiarity onto distinct regions of LLPC represents one way in which the functional neuroanatomy underlying separate types of processing can be distinguished. However, even within the domain of memory retrieval, this distinction does not represent an extensive view of possible parcellations of LLPC. Further, a failure to incorporate “non-retrieval” related accounts of parietal mechanisms could result in an incomplete description of boundaries that exist within LLPC. A deeper understanding of the processing roles of LLPC is likely to be bolstered by the ability to apply objective parcellation schemes using a variety of analysis techniques.
One such technique, rs-fcMRI boundary mapping, is based on the observation that rs-fcMRI can dissociate regions within the cortex using edge detection algorithms (Cohen et al., 2008
). This procedure uses a seed-based approach (Biswal et al., 1995
; Fox et al., 2005
) to identify regions whose rs-fcMRI-derived timecourse is significantly correlated with the timecourse of the seed location. The result is a whole-brain correlation map that indicates the degree of correlation between the seed and all other voxels in the brain. Specifically, the technique developed in Cohen et al. (2008)
detects transitions between whole-brain correlation maps of nearby cortical seeds, and translates these abrupt transitions into boundaries between cortical regions. A key concept underlying this approach that will be underscored by the present report is that global brain relationships
can facilitate the parcellation of anatomically adjacent pieces of cortex.
The current study begins with such rs-fcMRI boundary mapping to identify “correlationally” distinct regions in LLPC. These regions then serve as a launching point from which to probe a set of recognition memory fMRI studies for distinctions within LLPC. We find that LLPC regions divide along anatomical lines into an anterior group that does not display retrieval success effects, and a posterior group that does. These findings are augmented with large-scale network analysis of rs-fcMRI signal correlations between LLPC regions and regions located outside of LLPC using tools from graph theory. This analysis confirms the anterior/posterior distinction and divides the retrieval success regions into four groups embedded in distinct whole-brain rs-fcMRI networks. This final LLPC parcellation scheme is then corroborated by demonstrating that within the distinct whole-brain networks, the task-evoked signals shown by LLPC regions are shared by distinct sets of regions outside of LLPC.