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

Download asset Open asset

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.

1. 1. Andrews-Hanna JR
   2. Saxe R
   3. Yarkoni T

   (2014) Contributions of episodic retrieval and mentalizing to autobiographical thought: evidence from functional neuroimaging, resting-state connectivity, and fMRI meta-analyses

   NeuroImage 91:324–335.

   https://doi.org/10.1016/j.neuroimage.2014.01.032

   • PubMed
   • Google Scholar
2. 1. Beason-Held LL

   (2011) Dementia and the default mode

   Current Alzheimer Research 8:361–365.

   https://doi.org/10.2174/156720511795745294

   • PubMed
   • Google Scholar
3. 1. Bellana B
   2. Liu ZX
   3. Diamond NB
   4. Grady CL
   5. Moscovitch M

   (2017) Similarities and differences in the default mode network across rest, retrieval, and future imagining

   Human Brain Mapping 38:1155–1171.

   https://doi.org/10.1002/hbm.23445

   • PubMed
   • Google Scholar
4. 1. Buckner RL
   2. Wheeler ME
   3. Sheridan MA

   (2001) Encoding processes during retrieval tasks

   Journal of Cognitive Neuroscience 13:406–415.

   https://doi.org/10.1162/08989290151137430

   • PubMed
   • Google Scholar
5. 1. Buckner RL
   2. Andrews-Hanna JR
   3. Schacter DL

   (2008) The brain's default network: anatomy, function, and relevance to disease

   Annals of the New York Academy of Sciences 1124:1–38.

   https://doi.org/10.1196/annals.1440.011

   • PubMed
   • Google Scholar
6. 1. Burianova H
   2. McIntosh AR
   3. Grady CL

   (2010) A common functional brain network for autobiographical, episodic, and semantic memory retrieval

   NeuroImage 49:865–874.

   https://doi.org/10.1016/j.neuroimage.2009.08.066

   • PubMed
   • Google Scholar
7. 1. Canals S
   2. Beyerlein M
   3. Merkle H
   4. Logothetis NK

   (2009) Functional MRI evidence for LTP-induced neural network reorganization

   Current Biology 19:398–403.

   https://doi.org/10.1016/j.cub.2009.01.037

   • PubMed
   • Google Scholar
8. Preprint

   1. Chen G

   (2018) A tail of two sides: artificially doubled false positive rates in neuroimaging due to the sidedness choice with t-tests

   bioRxiv.

   https://doi.org/10.1101/328567

   • Google Scholar
9. 1. Chou YH
   2. Sundman M
   3. Whitson HE
   4. Gaur P
   5. Chu ML
   6. Weingarten CP
   7. Madden DJ
   8. Wang L
   9. Kirste I
   10. Joliot M
   11. Diaz MT
   12. Li YJ
   13. Song AW
   14. Chen NK

   (2017) Maintenance and representation of mind wandering during Resting-State fMRI

   Scientific Reports 7:40722.

   https://doi.org/10.1038/srep40722

   • PubMed
   • Google Scholar
10. 1. Cole MW
    2. Pathak S
    3. Schneider W

    (2010) Identifying the brain's most globally connected regions

    NeuroImage 49:3132–3148.

    https://doi.org/10.1016/j.neuroimage.2009.11.001

    • PubMed
    • Google Scholar
11. 1. Cole MW
    2. Bassett DS
    3. Power JD
    4. Braver TS
    5. Petersen SE

    (2014) Intrinsic and task-evoked network architectures of the human brain

    Neuron 83:238–251.

    https://doi.org/10.1016/j.neuron.2014.05.014

    • PubMed
    • Google Scholar
12. 1. Cox RW
    2. Chen G
    3. Glen DR
    4. Reynolds RC
    5. Taylor PA

    (2017) FMRI Clustering in AFNI: False-Positive Rates Redux

    Brain Connectivity 7:152–171.

    https://doi.org/10.1089/brain.2016.0475

    • PubMed
    • Google Scholar
13. 1. Damoiseaux JS
    2. Beckmann CF
    3. Arigita EJ
    4. Barkhof F
    5. Scheltens P
    6. Stam CJ
    7. Smith SM
    8. Rombouts SA

    (2008) Reduced resting-state brain activity in the "default network" in normal aging

    Cerebral Cortex 18:1856–1864.

    https://doi.org/10.1093/cercor/bhm207

    • PubMed
    • Google Scholar
14. 1. Daselaar SM
    2. Rice HJ
    3. Greenberg DL
    4. Cabeza R
    5. LaBar KS
    6. Rubin DC

    (2008) The spatiotemporal dynamics of autobiographical memory: neural correlates of recall, emotional intensity, and reliving

    Cerebral Cortex 18:217–229.

    https://doi.org/10.1093/cercor/bhm048

    • PubMed
    • Google Scholar
15. 1. Eichenbaum H

    (2000) A cortical-hippocampal system for declarative memory

    Nature Reviews Neuroscience 1:41–50.

    https://doi.org/10.1038/35036213

    • PubMed
    • Google Scholar
16. 1. Eklund A
    2. Nichols TE
    3. Knutsson H

    (2016) Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates

    PNAS 113:7900–7905.

    https://doi.org/10.1073/pnas.1602413113

    • PubMed
    • Google Scholar
17. 1. Eldaief MC
    2. Halko MA
    3. Buckner RL
    4. Pascual-Leone A

    (2011) Transcranial magnetic stimulation modulates the brain's intrinsic activity in a frequency-dependent manner

    PNAS 108:21229–21234.

    https://doi.org/10.1073/pnas.1113103109

    • PubMed
    • Google Scholar
18. 1. Ezzyat Y
    2. Wanda PA
    3. Levy DF
    4. Kadel A
    5. Aka A
    6. Pedisich I
    7. Sperling MR
    8. Sharan AD
    9. Lega BC
    10. Burks A
    11. Gross RE
    12. Inman CS
    13. Jobst BC
    14. Gorenstein MA
    15. Davis KA
    16. Worrell GA
    17. Kucewicz MT
    18. Stein JM
    19. Gorniak R
    20. Das SR
    21. Rizzuto DS
    22. Kahana MJ

    (2018) Closed-loop stimulation of temporal cortex rescues functional networks and improves memory

    Nature Communications 9:365.

    https://doi.org/10.1038/s41467-017-02753-0

    • PubMed
    • Google Scholar
19. 1. Fox MD
    2. Halko MA
    3. Eldaief MC
    4. Pascual-Leone A

    (2012) Measuring and manipulating brain connectivity with resting state functional connectivity magnetic resonance imaging (fcMRI) and transcranial magnetic stimulation (TMS)

    NeuroImage 62:2232–2243.

    https://doi.org/10.1016/j.neuroimage.2012.03.035

    • PubMed
    • Google Scholar
20. 1. Freedberg M
    2. Reeves JA
    3. Toader AC
    4. Hermiller MS
    5. Voss JL
    6. Wassermann EM

    (2019) Persistent Enhancement of Hippocampal Network Connectivity by Parietal rTMS Is Reproducible

    Eneuro 6:ENEURO.0129-19.2019.

    https://doi.org/10.1523/ENEURO.0129-19.2019

    • PubMed
    • Google Scholar
21. 1. Friston KJ

    (2011) Functional and effective connectivity: a review

    Brain Connectivity 1:13–36.

    https://doi.org/10.1089/brain.2011.0008

    • PubMed
    • Google Scholar
22. 1. Froudist-Walsh S
    2. Browning PG
    3. Young JJ
    4. Murphy KL
    5. Mars RB
    6. Fleysher L
    7. Croxson PL

    (2018) Macro-connectomics and microstructure predict dynamic plasticity patterns in the non-human primate brain

    eLife 7:e34354.

    https://doi.org/10.7554/eLife.34354

    • PubMed
    • Google Scholar
23. 1. Geib BR
    2. Stanley ML
    3. Dennis NA
    4. Woldorff MG
    5. Cabeza R

    (2017) From hippocampus to whole-brain: The role of integrative processing in episodic memory retrieval

    Human Brain Mapping 38:2242–2259.

    https://doi.org/10.1002/hbm.23518

    • PubMed
    • Google Scholar
24. 1. Gotts SJ
    2. Simmons WK
    3. Milbury LA
    4. Wallace GL
    5. Cox RW
    6. Martin A

    (2012) Fractionation of social brain circuits in autism spectrum disorders

    Brain 135:2711–2725.

    https://doi.org/10.1093/brain/aws160

    • PubMed
    • Google Scholar
25. 1. Goyal A
    2. Miller J
    3. Watrous AJ
    4. Lee SA
    5. Coffey T
    6. Sperling MR
    7. Sharan A
    8. Worrell G
    9. Berry B
    10. Lega B
    11. Jobst BC
    12. Davis KA
    13. Inman C
    14. Sheth SA
    15. Wanda PA
    16. Ezzyat Y
    17. Das SR
    18. Stein J
    19. Gorniak R
    20. Jacobs J

    (2018) Electrical Stimulation in Hippocampus and Entorhinal Cortex Impairs Spatial and Temporal Memory

    The Journal of Neuroscience 38:4471–4481.

    https://doi.org/10.1523/JNEUROSCI.3049-17.2018

    • PubMed
    • Google Scholar
26. 1. Grady CL
    2. Springer MV
    3. Hongwanishkul D
    4. McIntosh AR
    5. Winocur G

    (2006) Age-related changes in brain activity across the adult lifespan

    Journal of Cognitive Neuroscience 18:227–241.

    https://doi.org/10.1162/jocn.2006.18.2.227

    • PubMed
    • Google Scholar
27. 1. Gratton C
    2. Laumann TO
    3. Gordon EM
    4. Adeyemo B
    5. Petersen SE

    (2016) Evidence for Two Independent Factors that Modify Brain Networks to Meet Task Goals

    Cell Reports 17:1276–1288.

    https://doi.org/10.1016/j.celrep.2016.10.002

    • PubMed
    • Google Scholar
28. 1. Henson RN
    2. Greve A
    3. Cooper E
    4. Gregori M
    5. Simons JS
    6. Geerligs L
    7. Erzinçlioğlu S
    8. Kapur N
    9. Browne G

    (2016) The effects of hippocampal lesions on MRI measures of structural and functional connectivity

    Hippocampus 26:1447–1463.

    https://doi.org/10.1002/hipo.22621

    • PubMed
    • Google Scholar
29. 1. Hermiller MS
    2. VanHaerents S
    3. Raij T
    4. Voss JL

    (2019) Frequency-specific noninvasive modulation of memory retrieval and its relationship with hippocampal network connectivity

    Hippocampus 29:595–609.

    https://doi.org/10.1002/hipo.23054

    • PubMed
    • Google Scholar
30. 1. Hurlburt RT
    2. Alderson-Day B
    3. Fernyhough C
    4. Kühn S

    (2015) What goes on in the resting-state? A qualitative glimpse into resting-state experience in the scanner

    Frontiers in Psychology 6:1535.

    https://doi.org/10.3389/fpsyg.2015.01535

    • PubMed
    • Google Scholar
31. 1. Jacobs J
    2. Miller J
    3. Lee SA
    4. Coffey T
    5. Watrous AJ
    6. Sperling MR
    7. Sharan A
    8. Worrell G
    9. Berry B
    10. Lega B
    11. Jobst BC
    12. Davis K
    13. Gross RE
    14. Sheth SA
    15. Ezzyat Y
    16. Das SR
    17. Stein J
    18. Gorniak R
    19. Kahana MJ
    20. Rizzuto DS

    (2016) Direct Electrical Stimulation of the Human Entorhinal Region and Hippocampus Impairs Memory

    Neuron 92:983–990.

    https://doi.org/10.1016/j.neuron.2016.10.062

    • PubMed
    • Google Scholar
32. 1. Kahn I
    2. Andrews-Hanna JR
    3. Vincent JL
    4. Snyder AZ
    5. Buckner RL

    (2008) Distinct cortical anatomy linked to subregions of the medial temporal lobe revealed by intrinsic functional connectivity

    Journal of Neurophysiology 100:129–139.

    https://doi.org/10.1152/jn.00077.2008

    • PubMed
    • Google Scholar
33. 1. Kim K
    2. Ekstrom AD
    3. Tandon N

    (2016) A network approach for modulating memory processes via direct and indirect brain stimulation: toward a causal approach for the neural basis of memory

    Neurobiology of Learning and Memory 134:162–177.

    https://doi.org/10.1016/j.nlm.2016.04.001

    • Google Scholar
34. 1. Kim S
    2. Nilakantan AS
    3. Hermiller MS
    4. Palumbo RT
    5. VanHaerents S
    6. Voss JL

    (2018) Selective and coherent activity increases due to stimulation indicate functional distinctions between episodic memory networks

    Science Advances 4:1–9.

    https://doi.org/10.1126/sciadv.aar2768

    • Google Scholar
35. 1. Kim S
    2. Voss JL

    (2019) Large-scale network interactions supporting item-context memory formation

    PLOS ONE 14:e0210167.

    https://doi.org/10.1371/journal.pone.0210167

    • PubMed
    • Google Scholar
36. 1. King DR
    2. de Chastelaine M
    3. Elward RL
    4. Wang TH
    5. Rugg MD

    (2015) Recollection-related increases in functional connectivity predict individual differences in memory accuracy

    Journal of Neuroscience 35:1763–1772.

    https://doi.org/10.1523/JNEUROSCI.3219-14.2015

    • PubMed
    • Google Scholar
37. 1. Kirchhoff BA
    2. Wagner AD
    3. Maril A
    4. Stern CE

    (2000) Prefrontal-temporal circuitry for episodic encoding and subsequent memory

    The Journal of Neuroscience 20:6173–6180.

    https://doi.org/10.1523/JNEUROSCI.20-16-06173.2000

    • PubMed
    • Google Scholar
38. 1. Kucewicz MT
    2. Berry BM
    3. Kremen V
    4. Miller LR
    5. Khadjevand F
    6. Ezzyat Y
    7. Stein JM
    8. Wanda P
    9. Sperling MR
    10. Gorniak R
    11. Davis KA
    12. Jobst BC
    13. Gross RE
    14. Lega B
    15. Stead SM
    16. Rizzuto DS
    17. Kahana MJ
    18. Worrell GA

    (2018a) Electrical Stimulation Modulates High γ Activity and Human Memory Performance

    Eneuro 5:ENEURO.0369-17.2018.

    https://doi.org/10.1523/ENEURO.0369-17.2018

    • PubMed
    • Google Scholar
39. 1. Kucewicz MT
    2. Berry BM
    3. Miller LR
    4. Khadjevand F
    5. Ezzyat Y
    6. Stein JM
    7. Kremen V
    8. Brinkmann BH
    9. Wanda P
    10. Sperling MR
    11. Gorniak R
    12. Davis KA
    13. Jobst BC
    14. Gross RE
    15. Lega B
    16. Van Gompel J
    17. Stead SM
    18. Rizzuto DS
    19. Kahana MJ
    20. Worrell GA

    (2018b) Evidence for verbal memory enhancement with electrical brain stimulation in the lateral temporal cortex

    Brain 141:971–978.

    https://doi.org/10.1093/brain/awx373

    • PubMed
    • Google Scholar
40. 1. Kucyi A
    2. Tambini A
    3. Sadaghiani S
    4. Keilholz S
    5. Cohen JR

    (2018) Spontaneous cognitive processes and the behavioral validation of time-varying brain connectivity

    Network Neuroscience 2:397–417.

    https://doi.org/10.1162/netn_a_00037

    • PubMed
    • Google Scholar
41. 1. Libby LA
    2. Ekstrom AD
    3. Ragland JD
    4. Ranganath C

    (2012) Differential connectivity of perirhinal and parahippocampal cortices within human hippocampal subregions revealed by high-resolution functional imaging

    Journal of Neuroscience 32:6550–6560.

    https://doi.org/10.1523/JNEUROSCI.3711-11.2012

    • PubMed
    • Google Scholar
42. 1. Lustig C
    2. Snyder AZ
    3. Bhakta M
    4. O'Brien KC
    5. McAvoy M
    6. Raichle ME
    7. Morris JC
    8. Buckner RL

    (2003) Functional deactivations: change with age and dementia of the Alzheimer type

    PNAS 100:14504–14509.

    https://doi.org/10.1073/pnas.2235925100

    • PubMed
    • Google Scholar
43. 1. McCormick C
    2. St-Laurent M
    3. Ty A
    4. Valiante TA
    5. McAndrews MP

    (2015) Functional and effective hippocampal-neocortical connectivity during construction and elaboration of autobiographical memory retrieval

    Cerebral Cortex 25:1297–1305.

    https://doi.org/10.1093/cercor/bht324

    • PubMed
    • Google Scholar
44. 1. Mennes M
    2. Kelly C
    3. Colcombe S
    4. Castellanos FX
    5. Milham MP

    (2013) The extrinsic and intrinsic functional architectures of the human brain are not equivalent

    Cerebral Cortex 23:223–229.

    https://doi.org/10.1093/cercor/bhs010

    • PubMed
    • Google Scholar
45. 1. Mesulam MM
    2. Van Hoesen GW
    3. Pandya DN
    4. Geschwind N

    (1977) Limbic and sensory connections of the inferior parietal lobule (area PG) in the rhesus monkey: a study with a new method for horseradish peroxidase histochemistry

    Brain Research 136:393–414.

    https://doi.org/10.1016/0006-8993(77)90066-X

    • PubMed
    • Google Scholar
46. 1. Miller JP
    2. Sweet JA
    3. Bailey CM
    4. Munyon CN
    5. Luders HO
    6. Fastenau PS

    (2015) Visual-spatial memory may be enhanced with theta burst deep brain stimulation of the fornix: a preliminary investigation with four cases

    Brain 138:1833–1842.

    https://doi.org/10.1093/brain/awv095

    • PubMed
    • Google Scholar
47. Preprint

    1. Mohan UR

    (2019) The effects of direct brain stimulation in humans depend on frequency, amplitude, and white-matter proximity

    bioRxiv.

    https://doi.org/10.1101/746834

    • Google Scholar
48. 1. Nilakantan AS
    2. Bridge DJ
    3. Gagnon EP
    4. VanHaerents SA
    5. Voss JL

    (2017) Stimulation of the Posterior Cortical-Hippocampal Network Enhances Precision of Memory Recollection

    Current Biology 27:465–470.

    https://doi.org/10.1016/j.cub.2016.12.042

    • PubMed
    • Google Scholar
49. 1. Nilakantan AS
    2. Mesulam MM
    3. Weintraub S
    4. Karp EL
    5. VanHaerents S
    6. Voss JL

    (2019) Network-targeted stimulation engages neurobehavioral hallmarks of age-related memory decline

    Neurology 92:e2349–e2354.

    https://doi.org/10.1212/WNL.0000000000007502

    • PubMed
    • Google Scholar
50. 1. Paller KA

    (1997) Consolidating dispersed neocortical memories: the missing link in amnesia

    Memory 5:73–88.

    https://doi.org/10.1080/741941150

    • PubMed
    • Google Scholar
51. 1. Paller KA
    2. Wagner AD

    (2002) Observing the transformation of experience into memory

    Trends in Cognitive Sciences 6:93–102.

    https://doi.org/10.1016/S1364-6613(00)01845-3

    • PubMed
    • Google Scholar
52. 1. Pandya DN
    2. Seltzer B

    (1982) Intrinsic connections and architectonics of posterior parietal cortex in the rhesus monkey

    The Journal of Comparative Neurology 204:196–210.

    https://doi.org/10.1002/cne.902040208

    • PubMed
    • Google Scholar
53. 1. Rabin JS
    2. Gilboa A
    3. Stuss DT
    4. Mar RA
    5. Rosenbaum RS

    (2010) Common and unique neural correlates of autobiographical memory and theory of mind

    Journal of Cognitive Neuroscience 22:1095–1111.

    https://doi.org/10.1162/jocn.2009.21344

    • PubMed
    • Google Scholar
54. 1. Ranganath C
    2. Heller A
    3. Cohen MX
    4. Brozinsky CJ
    5. Rissman J

    (2005) Functional connectivity with the hippocampus during successful memory formation

    Hippocampus 15:997–1005.

    https://doi.org/10.1002/hipo.20141

    • PubMed
    • Google Scholar
55. 1. Ranganath C
    2. Ritchey M

    (2012) Two cortical systems for memory-guided behaviour

    Nature Reviews Neuroscience 13:713–726.

    https://doi.org/10.1038/nrn3338

    • PubMed
    • Google Scholar
56. 1. Rissman J
    2. Gazzaley A
    3. D'Esposito M

    (2004) Measuring functional connectivity during distinct stages of a cognitive task

    NeuroImage 23:752–763.

    https://doi.org/10.1016/j.neuroimage.2004.06.035

    • PubMed
    • Google Scholar
57. 1. Ritchey M
    2. Yonelinas AP
    3. Ranganath C

    (2014) Functional connectivity relationships predict similarities in task activation and pattern information during associative memory encoding

    Journal of Cognitive Neuroscience 26:1085–1099.

    https://doi.org/10.1162/jocn_a_00533

    • PubMed
    • Google Scholar
58. 1. Rugg MD
    2. Vilberg KL

    (2013) Brain networks underlying episodic memory retrieval

    Current Opinion in Neurobiology 23:255–260.

    https://doi.org/10.1016/j.conb.2012.11.005

    • PubMed
    • Google Scholar
59. 1. Scoville WB
    2. Milner B

    (1957) LOSS OF RECENT MEMORY AFTER BILATERAL HIPPOCAMPAL LESIONS

    Journal of Neurology, Neurosurgery & Psychiatry 20:11–21.

    https://doi.org/10.1136/jnnp.20.1.11

    • Google Scholar
60. 1. Shirer WR
    2. Ryali S
    3. Rykhlevskaia E
    4. Menon V
    5. Greicius MD

    (2012) Decoding subject-driven cognitive states with whole-brain connectivity patterns

    Cerebral Cortex 22:158–165.

    https://doi.org/10.1093/cercor/bhr099

    • PubMed
    • Google Scholar
61. 1. Shirvalkar PR
    2. Rapp PR
    3. Shapiro ML

    (2010) Bidirectional changes to hippocampal theta-gamma comodulation predict memory for recent spatial episodes

    PNAS 107:7054–7059.

    https://doi.org/10.1073/pnas.0911184107

    • PubMed
    • Google Scholar
62. 1. Squire LR

    (1992) Memory and the hippocampus: a synthesis from findings with rats, monkeys, and humans

    Psychological Review 99:195–231.

    https://doi.org/10.1037/0033-295X.99.2.195

    • PubMed
    • Google Scholar
63. 1. Sreekumar V
    2. Wittig JH
    3. Sheehan TC
    4. Zaghloul KA

    (2017) Principled Approaches to Direct Brain Stimulation for Cognitive Enhancement

    Frontiers in Neuroscience 11:650.

    https://doi.org/10.3389/fnins.2017.00650

    • PubMed
    • Google Scholar
64. 1. St Jacques PL
    2. Kragel PA
    3. Rubin DC

    (2011) Dynamic neural networks supporting memory retrieval

    NeuroImage 57:608–616.

    https://doi.org/10.1016/j.neuroimage.2011.04.039

    • PubMed
    • Google Scholar
65. 1. Suthana N
    2. Fried I

    (2014) Deep brain stimulation for enhancement of learning and memory

    NeuroImage 85 Pt 3:996–1002.

    https://doi.org/10.1016/j.neuroimage.2013.07.066

    • PubMed
    • Google Scholar
66. 1. Svoboda E
    2. McKinnon MC
    3. Levine B

    (2006) The functional neuroanatomy of autobiographical memory: a meta-analysis

    Neuropsychologia 44:2189–2208.

    https://doi.org/10.1016/j.neuropsychologia.2006.05.023

    • PubMed
    • Google Scholar
67. 1. Tambini A
    2. Ketz N
    3. Davachi L

    (2010) Enhanced brain correlations during rest are related to memory for recent experiences

    Neuron 65:280–290.

    https://doi.org/10.1016/j.neuron.2010.01.001

    • PubMed
    • Google Scholar
68. 1. Tambini A
    2. Nee DE
    3. D'Esposito M

    (2018) Hippocampal-targeted Theta-burst Stimulation Enhances Associative Memory Formation

    Journal of Cognitive Neuroscience 30:1452–1472.

    https://doi.org/10.1162/jocn_a_01300

    • PubMed
    • Google Scholar
69. 1. Van Calster L
    2. D'Argembeau A
    3. Salmon E
    4. Peters F
    5. Majerus S

    (2017) Fluctuations of Attentional Networks and Default Mode Network during the Resting State Reflect Variations in Cognitive States: Evidence from a Novel Resting-state Experience Sampling Method

    Journal of Cognitive Neuroscience 29:95–113.

    https://doi.org/10.1162/jocn_a_01025

    • PubMed
    • Google Scholar
70. 1. Wagner AD
    2. Schacter DL
    3. Rotte M
    4. Koutstaal W
    5. Maril A
    6. Dale AM
    7. Rosen BR
    8. Buckner RL

    (1998) Building memories: remembering and forgetting of verbal experiences as predicted by brain activity

    Science 281:1188–1191.

    https://doi.org/10.1126/science.281.5380.1188

    • PubMed
    • Google Scholar
71. 1. Wang JX
    2. Rogers LM
    3. Gross EZ
    4. Ryals AJ
    5. Dokucu ME
    6. Brandstatt KL
    7. Hermiller MS
    8. Voss JL

    (2014) Targeted enhancement of cortical-hippocampal brain networks and associative memory

    Science 345:1054–1057.

    https://doi.org/10.1126/science.1252900

    • PubMed
    • Google Scholar
72. 1. Warren KN
    2. Hermiller MS
    3. Nilakantan AS
    4. O'Neil J
    5. Palumbo RT
    6. Voss JL

    (2018) Increased fMRI activity correlations in autobiographical memory versus resting states

    Human Brain Mapping 39:4312–4321.

    https://doi.org/10.1002/hbm.24248

    • PubMed
    • Google Scholar
73. 1. Westphal AJ
    2. Wang S
    3. Rissman J

    (2017) Episodic Memory Retrieval Benefits from a Less Modular Brain Network Organization

    The Journal of Neuroscience 37:3523–3531.

    https://doi.org/10.1523/JNEUROSCI.2509-16.2017

    • PubMed
    • Google Scholar
74. 1. Yarkoni T
    2. Poldrack RA
    3. Nichols TE
    4. Van Essen DC
    5. Wager TD

    (2011) Large-scale automated synthesis of human functional neuroimaging data

    Nature Methods 8:665–670.

    https://doi.org/10.1038/nmeth.1635

    • PubMed
    • Google Scholar
75. 1. Yeo BT
    2. Krienen FM
    3. Sepulcre J
    4. Sabuncu MR
    5. Lashkari D
    6. Hollinshead M
    7. Roffman JL
    8. Smoller JW
    9. Zöllei L
    10. Polimeni JR
    11. Fischl B
    12. Liu H
    13. Buckner RL

    (2011) The organization of the human cerebral cortex estimated by intrinsic functional connectivity

    Journal of Neurophysiology 106:1125–1165.

    https://doi.org/10.1152/jn.00338.2011

    • PubMed
    • Google Scholar

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

• 2,455

  views

• 266

  downloads

• 35

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.