Integrating new findings and examining clinical applications of pattern separation

Abstract

Pattern separation, the ability to independently represent and store similar experiences, is a crucial facet of episodic memory. Growing evidence suggests that the hippocampus possesses unique circuitry that is computationally capable of resolving mnemonic interference by using pattern separation. In this Review, we discuss recent advances in the understanding of this process and evaluate the caveats and limitations of linking across animal and human studies. We summarize clinical and translational studies using methods that are sensitive to pattern separation impairments, an approach that stems from the fact that the hippocampus is a major site of disruption in many brain disorders. We critically evaluate the assumptions that guide fundamental and translational studies in this area. Finally, we suggest guidelines for future research and offer ways to overcome potential interpretational challenges to increase the utility of pattern separation as a construct that can further understanding of both memory processes and brain disease.

Fig. 1. Circuitry and computational properties of the hippocampus

a, This simplified circuitry of the hippocampus consists of the DG, CA3, CA1 and subiculum (Sub). The EC is the major input into the hippocampus and consists of the LEC and MEC. Layer II of the EC is the main input to DG and CA3 subregions (via the perforant pathway), whereas layers IV-V are the main output from the CA1 and Sub of the hippocampus to other cortical regions. The DG projects to the CA3 via mossy fibers. The CA3’s largest projection is onto itself via recurrent collaterals. The CA3 projects to the CA1 via Schaffer collaterals. The LEC mainly receives input from PrC (postrhinal in rodents), and the MEC mainly receives input from the PhC, although there is crosstalk between PrC and PhC, as well as between LEC and MEC. The PrC-LEC pathway is largely involved in content, object and local processing (orange), whereas the PhC-MEC pathway is largely involved in context, spatial and global processing (red). The DG is capable of performing pattern separation (blue), whereas the CA3 can perform pattern separation and pattern completion, depending on the input. b, The x axis shows interference levels (maximum, high, low and no interference), and the y axis shows the difference in neural signals (for example, BOLD fMRI contrast, decorrelation in IEG population activity or single-unit firing, or any other indicator of neural change in output). The DG shows a sharp increase in signal even with high levels of interference (pattern separation), whereas the CA3 shows lower neural signals at higher levels of interference and higher neural signals at lower levels of interference and is capable of performing pattern completion and separation. The CA1 shows a linear response function, with greater neural signals with lower levels of interference.

Fig. 2. Exponential increase in articles on pattern separation

The first articles on pattern separation were published in the 1970s, but it wasn’t until ~2010 that a marked increase in pattern separation publications occurred.

Fig. 3. Medial temporal lobe circuitry alterations in disease

a, In aging and AD, there is a reduction of the perforant path, hyperactivity in CA3, reduced inhibition of CA3, hypoactivity in the EC, reduced reelin and tau deposition in the LEC, decreased EC thickness, and impaired object versus spatial processing depending on PrC. b, In depression, there is a retraction of the CA3 dendrites, decreased DG neurogenesis and decreased DG/CA3 BOLD activity. In late-life depression, there is altered DG/CA3 activity and LEC hyperactivity. c, In schizophrenia, there is reduced DG and mossy fiber glutamate transmission, as well as increased CA3 and CA1 activity. d, DG neurogenesis increases with exercise, environmental enrichment and SSRI treatment. Antiepileptic treatment in MCI patients reduces CA3 activity and increases the level of LEC activity.

Fig. 4. Behavioral and neural predictions of discrimination performance and pattern separation

a, The x axis shows the interference level, from maximum interference to no interference, and the y axis shows discrimination performance (typically measured as a lure-discrimination index). The typically observed pattern of discrimination performance as a function of decreasing interference is largely linear (pattern 1). In clinical conditions, one might expect to see variations in this pattern, such that for certain interference conditions discrimination performance is lower or higher than that expected on the basis of control performance. Deviations from this linear pattern may be suggestive of impairments in pattern separation; however, the exact pattern of deviation will dictate whether certain conclusions can be drawn. For example, a clinical sample may show worse discrimination than that in healthy controls at high or low interference, but performance may not differ from that of controls in discrimination of targets or foils (pattern 2). This would suggest an impairment that is selective to items with interference and perhaps some specificity to pattern separation. This type of behavioral deficit has been characterized as a form of mnemonic rigidity. A selective deficit on the high, but not the low, interference stimuli could be stronger evidence in favor of this argument. In contrast, a more generalized impairment that includes even the items that induce little or no interference cannot be interpreted as selective to pattern separation (pattern 3). In some cases, it is possible to observe an enhanced discrimination profile that is selective to items with interference (pattern 4). b, Difference in neural signals (on the y axis) is more sharply tuned, such that normative samples typically show a curvilinear pattern (pattern 1). In this case, an impairment profile could appear linear (pattern 2). A linear ‘flattening’ of the neural tuning function can be interpreted as impairment in pattern separation. A more generalized impairment even on the items without interference (pattern 3) would be more suggestive of a general memory impairment that is not specific to pattern separation. Finally, enhanced tuning of the input/output neural transformation function is also possible (pattern 4) and would be expected under conditions that enhance memory, although evidence for this type of tuning remains scarce, with exercise studies possibly being an exception. Note the correspondence between numbers 1-4 in a and b, which indicates that overall curves in a are detuned versions of the curves in b, probably as a result of nonhippocampal influences, which introduce additional variability that influences the decision-making process. These hypothetical curves are based on a combination of observations from extant data and computational predictions.