Despite the promise of hr-fMRI for informing mechanistic and representational accounts of MTL substructure function, high-resolution functional imaging is not without its challenges. For example, the SNR of fMRI data diminishes proportionally to decreasing voxel size; thus, even with the gains afforded by reduced partial voluming (Bellgowan et al., 2006
), high-resolution studies must often include more trials per condition and/or more participants to obtain statistically significant effects. In addition, the anterior extent of the MTL suffers from susceptibility artifacts due to proximity to bone and air-filled sinuses, leading to fMRI signal dropout in anterior parts of PRC and ERC, and, to a lesser extent, anterior hippocampus (Ojemann et al., 1997
; Olman et al., 2009
; Schacter and Wagner, 1999
). Thus, although these anterior-most regions can be differentiated anatomically, obtaining reliable functional signal from and observing differences between them can prove difficult. Fortunately, several approaches to this challenge exist, including (a) running an MTL-targeted high-order shim prior to collecting functional data to reduce B0 heterogeneity, (b) adopting pulse sequences that optimize signal acquisition from susceptibility-sensitive structures, such as spiral in/out protocols (e.g., Olsen et al., 2009
; Preston et al., 2009
) and (c) further increasing spatial resolution (e.g., by increasing the through-plane resolution to approach that of the in-plane resolution), which serves to decrease intra-voxel spin de-phasing in susceptibility-sensitive regions (e.g., collecting isotropic voxels; Bakker et al., 2008
; Hassabis et al., 2009
; Kirwan and Stark, 2007
) though perhaps at an SNR cost for non-susceptible regions.
Unfortunately, the physiological factors that give rise to signal dropout also result in signal displacement in the phase-encoding direction (Ojemann et al., 1997
; Olman et al., 2009
)—in the case of most high-resolution studies acquiring coronal images perpendicular to the long axis of the hippocampus, in the superior-inferior direction or vice-versa. The magnitude and form of displacements again depend on the particular functional sequence adopted, with EPI protocols being particularly prone to displacement. Although not always implemented in hr-fMRI studies, such displacement can be attenuated by collecting B0 field maps and subsequently correcting for the measured displacement. Several analysis software packages now include standardized tools for unwarping EPI images (e.g., FSL: http://www.fmrib.ox.ac.uk/fsl/fugue/feat_fieldmap.html
; SPM: http://www.fil.ion.ucl.ac.uk/spm/software/spm8/
), enabling future studies to more easily implement such corrections and benefit from increased precision in functional localization. Finally, signal displacement can be reduced by increasing image acquisition speed or reducing field-of-view in the phase-encoding direction, though at a cost of reduced SNR (Olman et al., 2009
With respect to ROI analyses, a non-trivial factor in evaluating subfield function is the time required to manually trace multiple ROIs in both hemispheres. More importantly, issues pertaining to observer bias must also be considered, with the same observer ideally tracing all ROIs in a given study to maintain consistency across participants. Recent advances in automated segmentation of MTL subfields (e.g., Van Leemput et al., 2009
) may reduce the need for manual demarcation of subfields, and thus offer an associated reduction in both the time requirements and observer biases related to analyzing hr-fMRI datasets.
With respect to testing theories of hippocampal subfield function arising from computational models and animal studies, a major limitation of current hr-fMRI methodology is the difficulty in anatomically discriminating DG from CA2/3
. Despite the gains in resolution afforded by high-resolution imaging, segmentation of DG from CA2/3
is difficult due to the relative absence of anatomical landmarks, particularly in anterior portions of the hippocampus. Recent advances in structural imaging techniques allow for segmentation of DG from CA2/3
in a small number of slices (Ekstrom et al., 2009b
). However, given that DG comprises a very small number of structural voxels, it is likely that, at the resolution of the functional images (often acquired at approximately three times lower resolution than that of structural data), many functional voxels will contain data from both DG and CA2/3
. To this end, further gains in functional resolution may be required for these regions to be reliably differentiated.
Further increases in functional resolution likely will facilitate other efforts to examine whether human MTL demonstrates functional heterogeneity within its subfields similar to that observed in the animal literature, such as the presence of parallel streams supporting the processing of spatial information (postrhinal, medial ERC, proximal CA1, distal subiculum) and non-spatial information (PRC, lateral ERC, distal CA1, proximal subiculum) (for a review, see Knierim et al., 2006
). Given the rapid advances in parallel imaging technology, as well as the increasing availability of 7T scanners, there is reason to be optimistic that hr-fMRI at 3T and higher fields will ultimately achieve the necessary enhanced functional resolution required to more fully integrate the human and animal literatures on MTL subfield function. It bears noting, however, that with gains in sensitivity afforded by higher field strength come increased dropout and distortion in anterior regions of the MTL (e.g., Krasnow et al., 2003
; Kruger et al., 2001
). Moreover, the effective resolution of hr-fMRI data ultimately will be constrained by the point-spread function of the BOLD response (Logothetis and Wandell, 2004
A further challenge for relating hr-fMRI MTL subfield findings and recording data in animals is that, unlike direct single or multi-unit recordings from the animal brain, the BOLD signal is thought to primarily reflect input and local neuronal processing from many thousands of neurons, rather than spiking/output activity (Angenstein et al., 2009
; Logothetis et al., 2001
; Logothetis and Wandell, 2004
). Thus, hr-fMRI of the MTL may be too coarse to detect certain sparse coding properties of MTL neurons (e.g., grid cell responses in medial ERC), being better suited to comparisons with animal studies examining local field potentials (LFPs) or other measures of population activity. Furthermore, because fMRI is an indirect measure of neuronal function in which the BOLD signal peaks several seconds after neural response onset, the temporal resolution afforded is modest. As a result, hypotheses requiring knowledge of millisecond timing differences between regions, such as those regarding the order in which information flows through cortical and hippocampal circuitry, cannot be feasibly evaluated using fMRI alone. It is partly for this reason that a number of laboratories have begun to combine the high spatial resolution of structural and functional imaging with the high spatial and temporal resolution of direct intracranial recordings in human patients undergoing pre-surgical mapping for pharmacologically resistant temporal lobe epilepsy. While intracranial electroencephalography in patients is not widely available and the placement of electrode contacts is solely based on clinical necessity, extant data indicate that patients typically have multiple contacts throughout the MTL that can be effectively localized to specific subfields using high-resolution imaging (Ekstrom et al., 2008
). In addition to addressing MTL function, efforts to integrate hr-fMRI, single-unit, and LFP data from patients can advance understanding of the relationship between MTL BOLD activity and direct neural responses (Ekstrom et al., 2009a
Finally, a trade-off exists in collecting high-resolution images of the MTL in that only a limited number of high-resolution slices can be prescribed without exceeding a reasonable per-volume acquisition time (e.g., repetition times of 2–4 s). The tradeoff between volume coverage, spatial resolution, and temporal resolution typically results in slice prescriptions that exclude many brain areas outside the MTL and its immediate surroundings. This restricted coverage prevents progress in understanding the functional connectivity between the MTL and regions outside the slice prescription. Replication of hr-fMRI experiments at standard resolution offer one means of assessing how extra-MTL regions, such as structures in prefrontal and parietal cortex, interact with the MTL.
Despite these challenges, hr-fMRI of the MTL is the first widely accessible technique to offer a window into the functional organization of the human hippocampus and surrounding cortices at the individual subfield level. Progress to date suggests that this approach may ultimately fulfill its promise to bridge the gap between functional neuroimaging in humans and electrophysiological, gene knock-out, and lesion studies in animals, as well as computational theories of the MTL. The extant hr-fMRI literature provides preliminary support for functional heterogeneity among human MTL subfields, and offers a springboard from which future studies can address specific hypotheses of subfield function motivated by the animal and computational literatures, as well as subfield involvement in psychological processes that are ideally measured in humans, such as autobiographical recollection (e.g., Cabeza and St Jacques, 2007
; Maguire, 2001
) and future thinking (e.g., Schacter and Addis, 2009
). High-resolution fMRI also offers an exciting means of evaluating MTL subfield activity in populations exhibiting memory impairments or structural changes in the MTL, such as older adults and individuals with dementia (Small et al., 2000a
; Small et al., 2000b
), those with genetic risk for Alzheimer's disease (Suthana et al., in press
), and patients with schizophrenia (Gaisler-Salomon et al., 2009
; Schobel et al., 2009) or depression.
High-resolution fMRI also offers an exciting means of evaluating MTL subfield activity in populations exhibiting memory impairments, such as older adults and individuals with dementia (Small et al., 2000a
; Small et al., 2000b
), depression, or schizophrenia (Schobel et al., 2009
). As researchers increasingly turn to hr-fMRI to advance understanding of human MTL function, the coming decade promises to bring substantial progress in specifying how our mnemonic lives depend on representations and computations within the MTL.