Altered Human Memory Modification in the Presence of Normal Consolidation

Abstract

Following initial learning, the memory is stabilized by consolidation mechanisms, and subsequent modification of memory strength occurs via reconsolidation. Yet, it is not clear whether consolidation and memory modification are the same or different systems-level processes. Here, we report disrupted memory modification in the presence of normal consolidation of human motor memories, which relate to differences in lesioned brain structure after stroke. Furthermore, this behavioral dissociation was associated with macrostructural network architecture revealed by a graph-theoretical approach, and with white-matter microstructural integrity measured by diffusion-weighted MRI. Altered macrostructural network architecture and microstructural integrity of white-matter underlying critical nodes of the related network predicted disrupted memory modification. To the best of our knowledge, this provides the first evidence of mechanistic differences between consolidation, and subsequent memory modification through reconsolidation, in human procedural learning. These findings enable better understanding of these memory processes, which may guide interventional strategies to enhance brain function and resulting behavior.

Keywords: DWI; MRI; diffusion; procedural memory consolidation; reconsolidation; sequence learning.

Figure 1.

Individual patient MRI data. (A) Sagittal, coronal, and axial views of individual patients' T1-weighted MRI scans with segmented brain lesions. Slices for each view are shown at the center-of-gravity location for the lesion. (B) Group lesion probability maps displayed in MNI152 space (slices through MNI coordinate: X = 38, Y =− 5, Z = 15). Lesion aligned to the left hemisphere. The map shows voxels where at least 1 patient has a lesion (red), and up to 4 (yellow).

Figure 2.

Experimental design and behavioral results. (A) Participants were trained to tap a 5-digit sequence. Intact consolidation of the memory was assessed on Day 2 as the offline memory performance gains from the end of the Day 1 session. Memory modification was assessed on Day 3 as the offline memory gains from Day 2. (B) Patients' (n = 10) and healthy aged-matched controls' (n = 10) learning curve across days, when the memory was reactivated on Day 2 and when it was not. (C) Healthy controls showed intact consolidation and memory modification. Patients showed intact consolidation but impaired memory modification, which was significantly different from their consolidation and from healthy controls' memory modification. There were significant differences in performance between Day 1 and Day 3 (Total Gains) with no reactivation in the patient group, indicating stable consolidation following a delay that was comparable with healthy controls. In the healthy control group, improvements between Day 1 and Day 3 with Day 2 reactivation were significantly greater than those without Day 2 reactivation. Thus, Day 2 reactivation enabled efficient reconsolidation that mediated memory strengthening, resulting in offline improvements in performance. Contrary to healthy controls, in the patient group, improvements between Day 1 and Day 3 with Day 2 reactivation were not significantly different from without Day 2 reactivation. Repeated-measures ANOVA and t-tests were used. Error bars express standard errors. **P < 0.005; *P < 0.05.

Figure 3.

Structural network architecture. (A) WM fiber tract lesion damage. The most severe damage (marked with red and yellow) affected WM pathways involving the precentral (PrCG), the inferior (IFG), and the middle (MFG) frontal gyri network nodes. Network pathways representing ipsilesional hemisphere short and long association fibers are located in the top-left quadrant (with the black background). Transcallosal fiber pathways are located in lower-left and upper-right quadrants. Contralesional hemisphere short and long association fiber pathways between contralesional nodes directly connected to ipsilesional nodes through transcallosal pathways are located in the lower-right quadrant. Circle diameters denote standard deviation of the mean. (B) Contribution of each nodal brain region in the lesioned and normal networks to global structural network integration, nodal betweenness centrality. Gray bars denote the normal network. Red circles denote the mean lesioned network (diameter representing the standard deviation of the mean), with individual patient scores represented by small gray circles.

Figure 4.

Relation of memory modification to structural network architecture. The difference in nodal betweenness centrality between patients' and normals' networks only in the contralesional MFG correlated positively with memory modification scores. This finding suggests that the contralesional MFG, interconnected with areas related to motor learning, is an important hub integrating information for efficient memory modification.

Figure 5.

Memory modification correlated with WM FA in 3 contralesional WM regions, in the vicinity of the (A) precentral gyrus, (B) middle-frontal gyrus (MFG), and (C) anterior intraparietal area (AIP). Correlation clusters are shown in yellow. MNI coordinates indicate the center-of-mass of each cluster. Scatter plots depict the correlation for each cluster. Voxel-wise statistical images were thresholded at P < 0.05 (FDR corrected, see Methods). (D) Nodal betweenness centrality in the contralesional MFG correlated with FA in the same region.