Exploring Information Flow From Posteromedial Cortex During Visuospatial Working Memory: A Magnetoencephalography Study Part 3

Last, we took the average information flow between each pair of ROIs across three functionally relevant epochs, corresponding to the encoding, maintenance, and retrieval task phases, and asked whether the intersubject variability in information flow was correlated with variability in task performance. 

In modern society, the amount of information we receive is unprecedentedly huge. We are surrounded by massive amounts of information every day, from the messages we receive on our mobile phones to the news and advertisements we see on social media. most of our time and attention. In this case, it is easy for us to feel tired, but the average information flow can help us improve our memory.

First, more information flow allows us to better exercise our brains. When we continue to receive new information and knowledge, our brains need to constantly process and store this information, thus increasing the burden on the brain. This work is an exercise for brain function, which can help us improve our ability to think and analyze, to better deal with complex situations. Just like the body needs exercise to stay healthy, the brain needs constant challenges to improve its abilities.

Second, more information flow can help us expand our horizons. If we only accept limited information, our vision will be limited to a small area. And when we can obtain more information, we can learn about a wider range of topics and things. This can help us see the world more fully and improve our insight and judgment. This helps us better understand different cultures and perspectives, and thus better adapt and integrate into complex social environments.

Finally, average information flow can also stimulate our curiosity. As we continue to receive new information, we find ourselves interested in more topics and areas. This can stimulate our curiosity and desire for knowledge, thus making us more motivated to explore more knowledge. It can be seen that average information flow can also enhance our self-drive and enthusiasm.

In short, average information flow can help us improve brain function, expand our horizons, and stimulate our curiosity. When we have better memories, we will learn new knowledge and skills more easily and cope with complex challenges better. Therefore, we should actively accept more information and make ourselves a smarter and more versatile person. It can be seen that we need to improve memory, and Cistanche deserticola can significantly improve memory because Cistanche deserticola is a traditional Chinese medicinal material that has many unique effects, one of which is to improve memory. The efficacy of minced meat comes from the various active ingredients it contains, including acid, polysaccharides, flavonoids, etc. These ingredients can promote brain health in various ways.

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The "encoding" epoch included the first 180 ms after the onset of each of the four encoding stimuli onset, and the "maintenance" epoch included the maintenance period from 180 ms after the onset of the fourth encoding stimulus until the earliest onset of the retrieval stimulus, and the "retrieval" epoch corresponded to the onset of the retrieval stimulus until the end of the trial. 

For the participant with the shortest encoding stimulus duration (180 ms), these three epochs covered the first 3440 ms from the trial onset; while for the participant with the longest encoding stimulus duration (320 ms), the final epoch finished at 4000 ms. 

Across participants, the median reaction time was 1.99 s after the onset of the retrieval stimulus (lower to upper quartiles: 1.69–2.27 s), meaning that the "retrieval" epoch covered approximately the time until the participant's response. 

Across each of these epochs, for each pair of ROIs, we averaged data from time points for which there was at least moderate evidence of information flow (i.e., BF . 3, q, 0.05) in at least one direction between the pair of ROIs. We excluded data from remaining time points to minimize the contribution of data from times when there was weak or no evidence of information flow between the ROIs.

In each case, we report the average difference in information flow (DiffA.B), and the linear (Pearson's) correlation (r) between participants' average behavioral performance during the MEG session and the average difference in the information flow DiffA.B during the relevant epoch. We repeated this analysis (average DiffA.B and its correlation with behavior) for each of the 10,000 samples of the bootstrapped null data, resulting in a distribution of 10,000 null means and r values. 

As for other results, we report the central 95% of these null values, and we used this null distribution to define p values, in this case defining the p-value as the proportion of null means or r values with a greater absolute value than the observed value (two-sided test). Finally, we applied FDR correction to these p values to correct for multiple comparisons across epochs and calculated the BF for each mean and correlation.

Data availability. Data from MEG experiments are freely available online from the Open Science Framework (https://doi.org/10.17605/OSF. IO/MW3J2). This online repository includes de-identified raw data from the MEG experiments, details of the stimulus timing for each participant, and the MATLAB code used to perform the analyses reported here.

Results

Classification analyses

We used a series of classification analyses to test for times at which the MEG signals in OC, VTC, PMC, and PFC contained information about the location and image of each stimulus during visuospatial working memory performance. 

The decoding of the location in each stimulus is shown in Figure 3, and the decoding of the image at each location is shown in Figure 4. Decoding of stimulus location was higher than decoding of stimulus identity; but for decoding of both location and identity, there was evidence of periods of above-chance decoding of each stimulus in all ROIs.

For decoding stimulus location, we found periods of above-chance decoding of location and identity for each of the four encoding stimuli as well as the retrieval stimulus based on signals from OC, VTC, PMC, or PFC [q, 0.05, with FDR correction for multiple comparisons across time bins, including moderate (BF . 3) and strong (BF . 10) effects]. For decoding of stimulus image identity (Fig. 4), we found weaker classifier performance overall, with the proportion correct an order of magnitude lower than for decoding location. Despite the lower overall accuracy, statistical analyses showed that decoding of each stimulus identity was robustly above chance for some time points in all ROIs.

Across all ROIs, the decoding of stimulus location and identity followed a similar evolution over time, with an initial peak, then decay, consistent with signals primarily driven by the visual response to the stimulus. We expected stimulus information in OC to precede that in other ROIs, but the early peaks in decoding in OC, VTC, and PMC occurred at approximately the same time. This suggests that there may be some "signal leakage" between ROIs in our source localization (we return to this question when considering the results of the IFA, below). Nonetheless, the differences between ROIs suggest that these analyses have also captured signals that are nonoverlapping across ROIs. For instance, for decoding stimulus location, the relative accuracy at;100 ms versus;300 ms after the onset of the relevant stimulus varies across ROIs.

In Figures 3A–D and 4A–D, there tends to be a brief period of above-chance decoding during the processing of the retrieval stimulus. This may reflect some neural process comparing the retrieval stimulus with the remembered locations and identities, especially where this persists to later (e.g., 300 ms) after the onset of the retrieval stimulus. 

However, we cannot rule out that decoding at these times could be driven by the neural response to the retrieval stimulus if there is a slight imbalance in the counterbalancing of trials. For instance, brief periods of above-chance decoding before the onset of the relevant stimulus (e.g., around the time of the first encoding stimulus in Figs. 3B–E and 4B–E) clearly cannot be driven by a response to the decoded stimulus. 

This may have arisen since each trial was defined to include four different locations and four different identities during encoding; so, for example, decoding Location 1 versus Location 3 for encoding Stimulus 2 is equivalent to decoding "not Location 1" versus "not Location 3" for other encoding stimuli. Either way, our results suggest that signals based on remembered stimulus locations or identities were very weak compared with the robust signals comprising the visually driven response.

IF

We next considered Granger-causal interactions between each pair of ROIs using an IFA. Even when classifier performance is low, overall there may be small, genuine signals that drive differences in the pattern of classifier performance across the many different pairwise classifications for each time bin. However, to ensure our measures of information flow were not driven by any spurious effects, we performed this analysis using classifications of only the most recent stimulus for each trial epoch (see Materials and Methods). 

IFA specifically tests for differences between ROIs and the temporal structure of these differences, rendering it potentially more sensitive than average classifier performance for detecting differences between ROIs. The results of our IFAs between PMC and all other ROIs are shown in Figure 5, with interactions between the remaining ROIs shown in Figure 6.

The IFAs revealed periods of significant Granger-causal interactions between each pair of ROIs [q, 0.05, with FDR correction, including times of moderate (BF . 3) and strong (BF . 10) effects] across all trial phases (encoding, maintenance, and retrieval). 

The information flows (uppermost plots in each case for Figs. 5 and 6) were strongest in each direction near periods of higher classifier performance; but within each pair of ROIs, the relative strength of each ROI in influencing the other varied over time, as reflected in the difference plots. When OC was paired with any other ROI, information flow from OC tended to be higher than information flow to OC immediately after each stimulus onset, consistent with a stimulus-driven response. 

This was particularly pronounced for the encoding epoch, where the average difference in information flow between OC and any other ROI (avg DiffOC.
) varied from 0.094 to 0.127, and the BF indicated a series of strong effects in favor of greater information flow from OC to the other area (q, 0.05, BF . 10 in each case). There was also a moderate effect of information flow from OC to PMC exceeding the reverse direction during maintenance (avg DiffOC.PMC = 0.042, q, 0.05, BF = 3.19).

We were particularly interested in whether there was evidence of information flow from PMC shaping responses in other areas. The strongest evidence for this was between PMC and PFC. Average information flow from PMC to PFC tended to exceed the reverse direction over all epochs, with a strong effect during encoding (avg DiffPMC.PFC = 0.056, q, 0.05, BF = 18.70), and a moderate effect during maintenance (avg DiffPMC.PFC = 0.025, q, 0.05, BF = 7.33), but this bias did not reach significance during retrieval (avg DiffPMC.PFC = 0.014, q . 0.05, BF = 0.77). The time courses in Figure 5C show that, across all trial epochs, information flow from PMC to PFC was strongest around the time of the early, stimulus-driven response to each stimulus. With OC, PMC showed evidence of greater information flow in the latter part of the retrieval epoch (Fig. 5A). Averaged across the retrieval epoch, this difference was biased toward PMC driving OC, but this was a small effect (avg DiffPMC.PFC = 0.029, q, 0.05, BF = 1.59).

Across the remaining ROI pairs, the only cases with a moderate or greater effect (BF . 3) were between VT and PFC (Fig. 6C), during encoding (avg DiffVT.PFC = 0.057, q, 0.05, BF = 4.00) and retrieval (avg DiffVT.PFC = 0.040, q, 0.05, BF = 57.79). As for OC, the times where VT dominated information flow with PFC were consistent with the early stimulus-driven response (e.g., compare times of dominance of VT in Fig. 6C and OC in Fig. 6B). Overall, there was little evidence of PFC dominating information flow to other areas.

Last, for each trial epoch, we correlated the average information flow between each pair of ROIs with behavioral accuracy across participants. None of these correlations reached statistical significance once corrected for multiple comparisons, and no correlation reached the level of a moderate effect (BF . 3). This suggests that our relatively small sample size was insufficient to detect any predictive power of information flow for task performance. However, we believe such correlations could be a fruitful direction for future research adopting these methods, so we include these preliminary results as a reference for future work.

Discussion

Despite the demonstrated importance of the PMC during memory retrieval, the causal influence of PMC over other regions has remained unclear. Specifically, the direction and content of information exchange between PMC and other brain regions during memory processes have not been tested. Here, we used MEG recordings from MRI-defined ROIs to evaluate information flow between PMC and other regions during a visuospatial working memory task. Results suggest that PMC object representations show Granger-causal influence on stimulus information in other regions, most notably in its influence on PFC across all task phases. There was also the suggestion that PMC shapes responses in OC during retrieval.

PMC shapes stimulus representations in prefrontal areas

Our most striking finding concerns the influence of PMC on remembered stimulus information in PFC. Across all task phases, information flow from PMC to PFC tended to be more dominant than the reverse direction. Since our measure of information flow is based on classification performance, rather than response amplitude, this finding suggests that the stimulus-related information in PMC was predictive of stimulus representations that were about to emerge in PFC. This was most evident in the encoding and maintenance phases; however, PMC was also found to shape responses in PFC during the retrieval phase. The fact that this effect occurred shortly after the retrieval stimulus onset resonates with PMC relaying information on which the participant's decision is based, to PFC to support successful recall.

Prefrontal regions are reliably implicated in attentional control and the flexible coding of task-relevant information (Duncan, 2010; Freedman and Assad, 2016). Patterns of anatomic connectivity between the hippocampus and frontal visual-oculomotor systems (i.e., dorsolateral PFC and frontal eye fields) (Shen et al., 2016) suggest that frontal regions are particularly well situated to integrate visual memory information to guide behavior (Conti and Irish, 2021). The frontoparietal regions implicated in attentional control are also involved in working memory, especially during encoding and maintenance periods (Gazzaley and Nobre, 2012). Additionally, prefrontal regions are known to drive changes in the visual cortex; for instance, microstimulation of the frontal eye fields produces changes in the visual cortex consistent with shifts of attention (Moore et al., 2003; Premereur et al., 2013).

Our results suggest that PMC shapes prefrontal representations of remembered visuospatial information throughout task performance and including the beginning of the retrieval period. Previous fMRI work has demonstrated increased frontoparietal activation during encoding and maintenance whether retrieval was required or not, while the posterior cingulate cortex showed response patterns consistent with a role in retrieval (Rahm et al., 2014). Functional connectivity between PMC and prefrontal regions, such as ventromedial PFC, lends further support to the importance of frontoparietal coupling during episodic retrieval (for review, see Andrews-Hanna, 2012; Ritchey and Cooper, 2020). Our findings extend previous work by suggesting that this observed pattern of connectivity includes PMC influencing task-relevant information in prefrontal networks.

As with any correlation, our partial correlations in the IFA may reflect associations with another (untested) area. Another possibility is that our ROIs were not fully isolated during source reconstruction and included signals from nearby regions. If PMC signals were present in the PFC ROI or vice versa, this would increase the shared variance between the ROIs. Shared variance might reduce the extent to which signals in one ROI contribute information above that is already present in the other ROI, which could decrease the measured information flow.

PMC influences the early sensory cortex during retrieval

Our results further suggest that, during retrieval, PMC shapes responses in the sensory cortex. Across all task phases, and for each ROI pairing, the OC tended to dominate information flow, influencing other regions more than it was influenced by these regions, consistent with the visual nature of the task. However, the dominance of OC in our measures of information flow may have been amplified by the generally transient classifier performance observed. Across all ROIs, above-chance decoding was largely restricted to brief epochs after the stimulus presentations, suggesting that any neural representation of the maintained information made minimal contribution to classifier performance. Notably, we found some evidence of significant information flow from PMC to OC in the latter part of the retrieval period (BF = 1.59). Whether this information flow is functionally relevant remains unclear. Any functional influence of PMC on OC must be mediated by indirect connections since there are no direct projections between PMC and primary sensorimotor regions (Parvizi et al., 2006; Leech and Smallwood, 2019). Candidate indirect pathways by which PMC could influence occipital regions include the parietal-medial temporal pathway, which is proposed to contribute to visuospatial processing (Kravitz et al., 2011).

Implications for understanding PMC function

Overall, our results suggest that the PMC has a specific role in relaying stimulus-related information to other regions. This then begs the question of what exactly the PMC is doing. While our results suggest a directionality of PMC influence on other brain regions, the precise function of the PMC in this context remains unclear. The PMC represents one of the major hubs of the brain's default mode network, defined by its "task-negative" response profile (Buckner et al., 2005). However, we demonstrate here that PMC does not show a simple task-negative contribution to visuospatial memory. PMC engagement during encoding and maintenance is associated with poorer task performance (Piccoli et al., 2015; Santangelo and Bordier, 2019), yet the PMC "encoding/retrieval flip" suggests that increased activity during retrieval is associated with better recall (Daselaar et al., 2004, 2009). While activity alone may reflect content-unspecific contributions, our measures of stimulus representations, using classifier performance, suggest that PMC actively shapes representations of stimulus-related information in other regions during task performance. Accordingly, PMC is not engaged in the processing of purely external sensory stimuli but is actively involved in the representation of internal content, including remembered information (Leech and Smallwood, 2019). Our study, however, was underpowered to delineate how information flow between PMC and other brain regions is predictive of behavioral accuracy, and we suggest this will be a critical avenue to explore in future work.

Future directions

As the first study, to our knowledge, to explore the exchange of stimulus-related information between PMC and other regions, our study raises several directions for future research. While we focused here on four a priori ROIs and used a relatively small sample size, it will be important to comprehensively map the flow of information between PMC and other brain regions during visuospatial memory with larger cohorts of participants. Time-resolved methods, such as MEG/EEG, could also be complemented with imaging methods, such as fMRI, to test finer parcellations within these regions.

We had low overall decoding during maintenance, and so, a limited ability to detect information flow during this task phase. We chose visually similar stimuli to increase task difficulty and reduce verbal labeling. However, this may have decreased classifier performance for task identity. Decoding was higher for stimulus location, where the retinotopic organization of visual cortical areas would yield larger-spatial scale response differences, yet even for locations, beyond the stimulus-induced response, we found little evidence of decoding based on remembered values. Conversely, in decoding EEG signals during a simpler visual memory task, Bocincova and Johnson (2019) report above-chance decoding of stimuli during a delay period, albeit weaker than during encoding. Thus, while high task difficulty appears necessary to isolate the specific role of the PMC (Kochan et al., 2011; Leech et al., 2011; Vannini et al., 2011), a less demanding task might yield better classifier performance, to better detect information flows across all task phases.

Efforts to delineate the functional relevance of PMC subregions may also offer crucial insights into the early and accurate diagnosis of Alzheimer's disease (Buckner et al., 2005; Xia et al., 2014; Wu et al., 2016; Khan et al., 2020). Emerging evidence suggests that healthy young adults carrying the APOE-«4 allele exhibit inefficiencies modulating PMC activity during scene (but not face and object) working memory and perception (e.g., Shine et al., 2015), while functional deactivation of PMC during visuospatial working memory performance is predictive of subsequent cognitive decline in older adults with mild cognitive impairment (Kochan et al., 2011). 

Given that visuospatial dysfunction because of PMC dysfunction has been proposed as an early harbinger of Alzheimer's disease (Pihlajamäki et al., 2010; Irish et al., 2012; Salimi et al., 2018), future studies investigating functional changes in the PMC may improve the early identification of individuals at risk of dementia.

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