There is substantial interest in treating memory disorders via brain stimulation (Suthana and Fried, 2014; Kim et al., 2016; Sreekumar et al., 2017). Episodic memory depends on the hippocampus (Scoville and Milner, 1957; Squire, 1992) as well as on the distributed set of regions that form a hippocampal-cortical network (Buckner et al., 2008; Rugg and Vilberg, 2013; Eichenbaum, 2000; Ranganath et al., 2005; Ranganath and Ritchey, 2012) comprising distinct anterior-temporal and posterior-medial components (Ranganath and Ritchey, 2012; Libby et al., 2012). The goal of this study was to determine whether noninvasive stimulation targeting the hippocampal-cortical network can enhance network connectivity measured during memory processing, and whether such enhancement is related to episodic memory improvement. We targeted specific portions of the posterior-medial network (PMN) and therefore further hypothesized that stimulation would disproportionately impact the PMN rather than the anterior-temporal network (ATN). This question is of substantial mechanistic and practical significance, given that increased connectivity due to stimulation should manifest primarily during memory processing that depends on the network, and such task-dependent modulation would be essential for any effective intervention to improve memory ability.

Invasive stimulation of the hippocampus or its direct mesial-temporal afferents has primarily been associated with memory disruption (Jacobs et al., 2016; Goyal et al., 2018; Kucewicz et al., 2018a; Mohan, 2019). However, hippocampal network regions can serve as a target for memory improvement (Mohan, 2019; Ezzyat et al., 2018; Kucewicz et al., 2018b; Miller et al., 2015; Shirvalkar et al., 2010). Noninvasive transcranial electromagnetic stimulation (TMS) targeting cortical PMN locations has been shown to improve memory (Tambini et al., 2018; Hermiller et al., 2019; Kim et al., 2018; Wang et al., 2014; Nilakantan et al., 2019; Nilakantan et al., 2017) and alter hippocampal-cortical network fMRI activity (Tambini et al., 2018; Hermiller et al., 2019; Kim et al., 2018; Wang et al., 2014; Nilakantan et al., 2019), especially within the PMN (Kim et al., 2018; Wang et al., 2014; Nilakantan et al., 2019), for durations that substantially outlast the stimulation period. Because PMN-targeted stimulation increases connectivity among PMN regions and relatively low hippocampal-cortical network connectivity is associated with poor episodic memory (Beason-Held, 2011; Grady et al., 2006; Lustig et al., 2003; Damoiseaux et al., 2008; Froudist-Walsh et al., 2018; Henson et al., 2016), it is tempting to hypothesize that memory improvements due to stimulation occur via overall increased connectivity of hippocampal-cortical networks. However, successful memory typically relies on dynamic reconfiguration of fMRI connectivity within the PMN and ATN in response to memory demands (Warren et al., 2018; Gratton et al., 2016; Kim and Voss, 2019; Westphal et al., 2017; King et al., 2015; Geib et al., 2017). Thus, stimulation might need to produce task-dependent and location-specific, rather than nonspecific, increases in hippocampal-cortical network connectivity in order to benefit memory.

Several lines of evidence suggest that memory enhancement should require that hippocampal-cortical network fMRI connectivity increases occur in response to memory processing demands. For instance, effective memory encoding and retrieval is associated with location-specific and task-dependent increases of stimulus-evoked fMRI activity within the network (Paller and Wagner, 2002; Paller, 1997; Wagner et al., 1998; Buckner et al., 2001; Kirchhoff et al., 2000). Correspondingly, better memory is predicted by fMRI connectivity related to specific memory processes, such as recollection (Kim and Voss, 2019; King et al., 2015). In rodents, theta-gamma synchrony in the hippocampus is observed during memory processing, indicating connectivity changes modulated by memory demands (Shirvalkar et al., 2010). Pharmacological disruption of hippocampal theta-gamma synchrony and memory can be rescued by theta-burst stimulation of the fornix, indicating that this marker of connectivity is critical for memory (Shirvalkar et al., 2010). Furthermore, amnestic states, such as those caused by hippocampal lesions, are associated not only with reduced hippocampal-cortical connectivity, but also with increased connectivity between the hippocampal network and other networks (Froudist-Walsh et al., 2018; Henson et al., 2016), suggesting that nonspecific increases in connectivity can be problematic. Demonstration that noninvasive stimulation can affect memory task-dependent changes in connectivity within specific targeted portions of the hippocampal-cortical network therefore is crucial for its evaluation as an effective memory intervention. This question has not been addressed, as the current standard for assessing the effects of brain stimulation on networks is via fMRI connectivity measured during the resting state (Wang et al., 2014; Canals et al., 2009; Eldaief et al., 2011; Fox et al., 2012), which by definition is insensitive to task-dependent connectivity.

We evaluated whether stimulation can alter task-dependent fMRI connectivity within the PMN and ATN. One group of participants (n = 16) received multi-session, high-frequency TMS targeting the PMN via parietal cortex, which is a robust component of the PMN (Ranganath and Ritchey, 2012; Libby et al., 2012). A separate control group (n = 16) received the same TMS regimen targeting a control prefrontal cortex (PFC) location that is not robustly part of the PMN. Each group also received site-specific sham-control stimulation, delivered at a subthreshold intensity which should not influence brain function, administered in counterbalanced order with real stimulation (Figure 1). Because both real and sham stimulation were delivered in different groups (targeting the PMN versus the control PFC location), this design guarded against influences from any nonspecific or subjective differences between real and sham stimulation, which would be similar for both stimulation locations. We compared the effects of stimulation on fMRI connectivity within the PMN, ATN, and whole-brain (via an exploratory analysis) measured 24 hr later during a memory retrieval task versus during the resting state. The memory retrieval task involved extended periods of autobiographical memory retrieval, which has been shown to cause robust hippocampal-cortical network connectivity changes primarily in PMN regions compared to the resting state (Warren et al., 2018). Thus, we were able to test for task-dependent (autobiographical retrieval versus rest) and network-specific (PMN-targeted versus PFC control) effects of stimulation on fMRI connectivity.

Figure 1

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We hypothesized that PMN-targeted stimulation would increase fMRI connectivity during memory retrieval as compared to the resting state relative to sham-control stimulation, whereas out-of-network PFC-targeted stimulation would not. We additionally hypothesized that these connectivity changes would be specific to the PMN, as it (i) was targeted, (ii) contributes to autobiographical memory retrieval (Ranganath and Ritchey, 2012), and (iii) responds to the stimulation regimen that was used (Kim et al., 2018; Wang et al., 2014; Nilakantan et al., 2019). We therefore assessed whether the task-dependent modulation of connectivity due to PMN-targeted stimulation occurred specifically for the PMN versus the ATN, using a priori defined regions of interest for each network (Libby et al., 2012). Furthermore, we tested whether the positive influence of PMN-targeted stimulation on the up-regulation of fMRI connectivity during memory retrieval would predict episodic memory improvement measured in an independent task. This allowed us to address the hypothesis that selective increases in fMRI connectivity during memory retrieval serves as an indicator of effective hippocampal network function that can be modulated by PMN-targeted TMS.

These findings demonstrate memory task-dependent increases in the expression of fMRI connectivity changes caused by PMN-targeted stimulation. These effects were selective to a priori defined regions of the PMN relative to the ATN, which was confirmed via exploratory whole-brain analysis. Task-dependent connectivity increases in these regions were also specific to the PMN-targeted stimulation condition, relative to PFC-targeted control stimulation. Furthermore, retrieval-related connectivity increases in left medial temporal cortex and hippocampus due to stimulation predicted context recollection improvement measured in a separate task. Thus, enhancing hippocampal connectivity during memory processing is functionally critical and is achievable via noninvasive stimulation. Furthermore, these task-dependent effects were measured ~ 24 hr after stimulation, indicating that stimulation led to upregulation of connectivity during a subsequent memory task administered long after, and without any specific relationship to, stimulation delivery.

The PMN and ATN are functionally distinct components of the larger hippocampal-cortical network and are thought to differentially support memory processing related to context recollection versus item recognition, respectively (Ranganath and Ritchey, 2012; Libby et al., 2012). Here, we report that increased connectivity due to stimulation was selective for PMN regions and for the autobiographical memory retrieval task. This selectivity is consistent with the role of the PMN in autobiographical memory retrieval (Ranganath and Ritchey, 2012) as well as with the stimulation location, which was within the PMN (Libby et al., 2012). These findings are consonant with our previous report that the same stimulation protocol increased stimulus-evoked activity during the encoding of item-context pairings selectively within the PMN (Kim et al., 2018; Nilakantan et al., 2019), and further supports hypothesized functional distinctions of the PMN and ATN. Future studies could seek stronger evidence for functional distinction by attempting to target the ATN with the goal of identifying crossover interaction of the effects of ATN-targeted versus PMN-targeted stimulation.

Connectivity changes due to stimulation were measured during a memory retrieval task (autobiographical retrieval task) versus rest, yet they predicted the effects of stimulation on memory ability measured using an independent item/source episodic memory test. Although autobiographical retrieval and episodic memory are superficially distinct, there is accumulating evidence that they are supported by similar cognitive operations and brain regions (McCormick et al., 2015; Andrews-Hanna et al., 2014; Burianova et al., 2010; Daselaar et al., 2008). Our findings of a relationship of stimulation effects on connectivity during autobiographical retrieval with episodic memory performance underscores this similarity. Indeed, we have previously found that the PMN-targeted stimulation parameters used here improve a variety of episodic memory tasks of different formats, including face-word paired associates (Wang et al., 2014), item-scene paired associates (Kim et al., 2018; Nilakantan et al., 2019), item-location associations (Kim et al., 2018; Nilakantan et al., 2019), and precise spatial recall (Nilakantan et al., 2017). Although we did not assess stimulation effects on the success of autobiographical memory retrieval, the current findings emphasize that stimulation targeting the PMN improves a variety of memory measures. Though these memory tasks are typically considered distinct (paired associate versus spatial recall, episodic versus autobiographical), they all have been associated with the PMN (Buckner et al., 2008; Svoboda et al., 2006; McCormick et al., 2015; Andrews-Hanna et al., 2014; Burianova et al., 2010; Daselaar et al., 2008) and therefore all likely respond to PMN-targeted stimulation.

Although resting-state fMRI connectivity has proven useful in characterizing the effects of stimulation on brain network function, including to understand memory improvements due to stimulation (Tambini et al., 2018; Hermiller et al., 2019; Wang et al., 2014; Kim and Voss, 2019), the current findings highlight one of the many limitations of resting-state fMRI for this purpose. That is, relationships between resting-state connectivity and network function are not well specified. Here, there was strong modulation of the effects of stimulation on connectivity based on whether it was measured during a memory retrieval task versus during rest, indicating that resting-state fMRI alone was an incomplete assay for stimulation effects. Furthermore, the functional relevance of stimulation for memory performance was related to its effect on the task-dependent change in connectivity, indicating that resting-state fMRI alone would not have identified functionally relevant effects of stimulation on connectivity.

It is likely that individuals engage in a variety of uncontrolled and typically unmeasured cognitive operations during resting-state fMRI and that these at least partially drive resting-state fMRI connectivity outcomes (Van Calster et al., 2017; Chou et al., 2017; Kucyi et al., 2018; Hurlburt et al., 2015). Our finding that stimulation effects on connectivity are particularly strong during a memory retrieval task is especially problematic for resting-state fMRI as an outcome for memory interventions given that subjects frequently and variably retrieve memories during resting-state fMRI (Van Calster et al., 2017; Chou et al., 2017; Kucyi et al., 2018; Hurlburt et al., 2015). Variable effects of stimulation on fMRI connectivity at rest would therefore be expected based on the content and quality of memory retrieval subjects experience during scanning. The current experiment accounted for these challenges by giving an explicit retrieval task during connectivity measurement, which permitted differentiation of the PMN-targeted versus PFC-targeted stimulation conditions and identified functionally relevant effects of stimulation on task-dependent connectivity related to memory performance.

As our main comparison was between autobiographical memory retrieval and rest, our interpretation is limited without a different cognitive task control. It is therefore possible that similar effects of stimulation on task-dependent connectivity could result using tasks other than those that involve memory. However, different cognitive demands have been strongly associated with connectivity of distinct functional networks (Cole et al., 2014; Rissman et al., 2004; Mennes et al., 2013), with PMN connectivity primarily associated with episodic and autobiographical memory (Buckner et al., 2008; Svoboda et al., 2006; McCormick et al., 2015; Andrews-Hanna et al., 2014; Burianova et al., 2010; Daselaar et al., 2008). It is therefore reasonable to propose that task-dependent effects of stimulation on PMN connectivity would likely be specific for those that involve memory demands, including but not limited to autobiographical retrieval. Notably, whereas PMN-targeted stimulation increased PMN connectivity during retrieval more so than during rest, PFC-targeted stimulation demonstrated the opposite pattern (rest > retrieval). Because the PMN but not the dorsolateral prefrontal cortex has generally been associated with vivid autobiographical memory retrieval (Svoboda et al., 2006; St Jacques et al., 2011), this finding suggests that task-dependent connectivity increases occurred for the task that matched the function of the stimulated network. Future studies should directly investigate task-specificity using multiple task conditions, with an emphasis on determining whether memory demands are required to generate results such as those reported here.

We used an area of dorsolateral prefrontal cortex as a control stimulation location as it is distinct from the PMN. Indeed, counter to the effects of PMN-targeted stimulation, PFC-targeted stimulation caused nonsignificant reductions of PMN task-dependent connectivity (Figure 2A) and significant reductions of task-dependent connectivity in several distributed regions independent from the PMN (Figure 4B). These findings are consistent with our previous report showing reductions in task-evoked activity during memory encoding in prefrontal cortex due to the PFC-targeted stimulation protocol used here (Kim et al., 2018). Although resting-state connectivity of dorsolateral prefrontal cortex with the hippocampus is not robust (Kahn et al., 2008; Yeo et al., 2011), some analyses incorporating task-based connectivity have identified areas of dorsolateral prefrontal cortex that align with the ATN (Libby et al., 2012). However, these areas are distinct from the dorsolateral prefrontal location that we stimulated and, indeed, PFC-targeted stimulation caused no significant change in ATN task-dependent connectivity. Thus, PFC-targeted stimulation was a suitable control for PMN-targeted stimulation, based on a priori considerations and on the distinct pattern of findings that was identified for PFC-targeted stimulation versus PMN-targeted stimulation. An important direction for future research is to determine whether the multi-day stimulation regimen used in the current experiment applied to dorsolateral prefrontal cortex can improve cognitive functions thought to depend on this area.

Although stimulation that influenced task-dependent PMN connectivity (PMN-targeted stimulation) was delivered via lateral parietal cortex, the most robust effects on the PMN occurred in medial occipital-parietal and hippocampal portions of the PMN. This is consistent with our previous findings obtained with the current multi-day stimulation protocol, whereby the most robust effects on connectivity and memory-related activity due to lateral parietal stimulation occurred for the hippocampus and medial occipital-parietal regions rather than the stimulation site (Kim et al., 2018; Wang et al., 2014; Nilakantan et al., 2019). One possibility is that although lateral parietal cortex provides anatomical projections to the mesial temporal lobe (Mesulam et al., 1977; Pandya and Seltzer, 1982) by which its stimulation can influence hippocampal activity, it has relatively weak functional connectivity relative to medial occipital-parietal portions of the network (Kahn et al., 2008; Yeo et al., 2011) and therefore is less influenced by the influence of stimulation on hippocampal neuroplasticity. Indeed, we have previously found that the degree to which a region’s connectivity increases due to stimulation is predicted by its baseline connectivity with the hippocampus (Wang et al., 2014), and this relationship has been replicated in an independent sample (Freedberg et al., 2019).

An alternative possibility is that the effects of brain stimulation are locally disruptive at the stimulated location (Jacobs et al., 2016; Goyal et al., 2018; Kucewicz et al., 2018a; Mohan, 2019) but can generate facilitation in connected regions (Mohan, 2019; Ezzyat et al., 2018; Kucewicz et al., 2018b; Miller et al., 2015; Shirvalkar et al., 2010). Indeed, like PMN-targeted stimulation, the effects of PFC-targeted stimulation on task-dependent connectivity also occurred for distributed regions other than the stimulated location (Figure 4B), but without involvement of the hippocampus. Future research will be needed to adjudicate among alternative possibilities for how network-targeted brain stimulation influences the hippocampal network as well as other neurocognitive networks.

To summarize, our findings suggest that PMN-targeted brain stimulation increases activity coupling among PMN regions when these regions are engaged by memory processing demands, rather than nonspecifically engaged during rest. Furthermore, task-dependent connectivity increases in the medial temporal lobe predicted improvement in a separate memory task. Thus, memory enhancement by brain stimulation relies on dynamic (i.e., activity-dependent) rather than static changes in connectivity among select portions of the hippocampal-cortical network, thereby reflecting a form of effective rather than functional connectivity (Friston, 2011). The relationship between task-dependent connectivity and memory improvement was robust for the left hippocampus and surrounding medial temporal cortex, consistent with its established role in memory encoding and retrieval (Scoville and Milner, 1957; Squire, 1992; Ranganath and Ritchey, 2012). This finding is important given that nonspecific increases in connectivity could be detrimental to memory as well as other cognitive abilities, as this would entail less-selective participation of the hippocampal-cortical network in memory (Shirvalkar et al., 2010; Froudist-Walsh et al., 2018; Henson et al., 2016; Warren et al., 2018; Gratton et al., 2016; Kim and Voss, 2019; Westphal et al., 2017; King et al., 2015; Geib et al., 2017). Stimulation-based interventions for memory disorders should therefore strive to achieve memory task-dependent functional engagement, as identified here using PMN-targeted noninvasive stimulation.

Raw data are available on the Northwestern University Neuroimaging Data Archive (https://nunda.northwestern.edu/). Access to NUNDA requires creating a login with a valid email address - registration is free and unvetted. Once a login has been created, you can find a project with ID "DERVISH". From there you can find the following subject IDs which were used in the current study. PMN-Stim, post stimulation: 01DY_A2, 02DY_A5, 03DY_A5, 04DY_A2, 05aDY_A2, 06DY_A5, 07aDY_A2, 08DY_A5, 09DY_A2, 10DY_A5, 11aDY_A2, 12aDY_A5, 13DY_A2, 14DY_A5, 15DY_A52 16DY_A25 PMN-Stim, post sham: 01DY_A5, 02DY_A2, 03DY_A2, 04DY_A5, 05aDY_A5, 06DY_A2, 07aDY_A5, 08DY_A2, 09DY_A5, 10DY_A2, 11aDY_A5, 12aDY_A2, 13DY_A5, 14DY_A2, 15DY_A5, 16DY_A2. PFC-Stim, post stimulation: 17DY_A2, 18DY_A5, 19DY_A2, 20DY_A5, 21DY_A2, 22aDY_A5, 23DY_A2, 24DY_A5, 25DY_A2, 26DY_A5, 27DY_A2, 28DY_A5, 29DY_A2, 30DY_A5, 31bDY_A2, 32aDY_A5. PFC-Stim, post sham: 17DY_A5, 18DY_A2, 19DY_A5, 20DY_A2, 21DY_A5, 22aDY_A2, 23DY_A5, 24DY_A2, 25DY_A5, 26DY_A2, 27DY_A5, 28DY_A2, 29DY_A5, 30DY_A2, 31bDY_A5, 32aDY_A2.

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Author details

1. Kristen N Warren

   Interdepartmental Neuroscience Program, Department of Medical Social Sciences, Ken and Ruth Davee Department of Neurology, and Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, United States

   Contribution

   Data curation, Formal analysis, Investigation, Visualization, Writing—original draft, Writing—review and editing

   For correspondence

   kristenwarren2015@u.northwestern.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6960-8466

2. Molly S Hermiller

   Interdepartmental Neuroscience Program, Department of Medical Social Sciences, Ken and Ruth Davee Department of Neurology, and Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, United States

   Contribution

   Validation, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7234-4695

3. Aneesha S Nilakantan

   Interdepartmental Neuroscience Program, Department of Medical Social Sciences, Ken and Ruth Davee Department of Neurology, and Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, United States

   Contribution

   Validation, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

4. Joel L Voss

   Interdepartmental Neuroscience Program, Department of Medical Social Sciences, Ken and Ruth Davee Department of Neurology, and Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, United States

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Validation, Methodology, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

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