People often forget information because they fail to effectively encode it. Here, we test the hypothesis that targeted electrical stimulation can modulate neural encoding states and subsequent memory outcomes. Using recordings from neurosurgical epilepsy patients with intracranially implanted electrodes, we trained multivariate classifiers to discriminate spectral activity during learning that predicted remembering from forgetting, then decoded neural activity in later sessions in which we applied stimulation during learning. Stimulation increased encoding-state estimates and recall if delivered when the classifier indicated low encoding efficiency but had the reverse effect if stimulation was delivered when the classifier indicated high encoding efficiency. Higher encoding-state estimates from stimulation were associated with greater evidence of neural activity linked to contextual memory encoding. In identifying the conditions under which stimulation modulates memory, the data suggest strategies for therapeutically treating memory dysfunction.

We addressed this problem by using each subject’s classifier trained on record-only sessions to decode patterns of neural activity during later stimulation sessions. The classifier served as a model that allowed us to assess evidence for the presence of good encoding states before and after stimulation and control periods. The estimates from the classifier integrate information across electrodes and frequencies, which we predicted would account for heterogeneity in stimulation’s physiological effects across people. We targeted stimulation to electrodes placed in nodes of the memory network: if available within the electrode montage, we stimulated a single MTL structure (hippocampus or entorhinal, perirhinal, or parahippocampal cortex) in a given session. For subjects without MTL contacts, we stimulated other structures linked to memory encoding, such as the prefrontal and parietal cortex [], which we selected by identifying the contact that showed the largest subsequent memory effect in the high-frequency range (70–200 Hz), a marker of successful memory encoding that correlates with multi-unit neural firing [].

Using multivariate classification was advantageous in another way. Direct electrical stimulation has multifaceted effects on neural activity that are local and remote relative to the site of stimulation [], and that depend on the baseline excitability of the targeted neural population at the time stimulation is delivered []. This poses a challenge when trying to predict the brain structures stimulation is likely to excite or inhibit and its consequent effects on behavior. Stimulating hippocampal and MTL cortical targets in humans, for example, leads to inconsistent and modest effects on memory, with some studies suggesting memory facilitation [] and others showing memory disruption [].

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

Responses of single neurons to electrical stimulation of the surface of the visual cortex.

Long-term potentiation in the dentate gyrus is induced preferentially on the positive phase of theta-rhythm.

Stimulation in hippocampal region CA1 in behaving rats yields long-term potentiation when delivered to the peak of theta and long-term depression when delivered to the trough.

To do so, we simultaneously measured neural activity across the brain. We recorded intracranial electroencephalography (iEEG) signals from subdural and depth electrodes implanted in patients with medically refractory epilepsy undergoing clinical monitoring to determine seizure onset foci. Subjects performed free recall, a memory task sensitive to many types of neurological dysfunction [] and whose cognitive basis has been modeled by multiple computational mechanisms []. We then used multivariate classification to test whether a classifier could predict the probability of recall success from patterns of neural activity recorded across the brain during encoding. In this way, we took advantage of our access to many recording channels to derive a subject-specific model that could differentiate encoding states likely to lead to remembering from states likely to lead to forgetting.

To use stimulation to modulate encoding states, we first needed to reliably identify neural activity conducive to successful memory. There is evidence that theta activity in the hippocampus and MTL cortex prior to stimulus presentation predicts memory [], and pre-stimulus theta activity has been used to trigger learning trials and improve performance in animal models of classical conditioning []. However, similar approaches in humans using MTL activity in the form of intracranial theta [] have not reliably modulated memory performance. We hypothesized that we could derive a more sensitive index of memory function by estimating encoding states that reflect global memory function, as opposed to specific operations carried out in focal brain areas.

We predicted that stimulation’s effects on memory would depend on the brain’s encoding state at the time it is delivered. If the memory network is operating efficiently, stimulation should interfere with the encoding process and thus later memory. However, if the memory network is not operating efficiently, we predicted that stimulation should disrupt dysfunctional encoding activity and therefore facilitate memory. A mechanism whereby stimulation disrupts dysfunctional brain networks is thought to explain the success in using deep brain stimulation (DBS) of thalamocortical circuits in treating motor dysfunction in Parkinson’s disease [].

If variability in distributed neural network activity reflects fluctuation of encoding states that leads to differences in memory performance, then it should be possible to modulate memory by perturbing the brain’s encoding state directly []. We test this hypothesis using electrical stimulation delivered through electrodes implanted in the brains of epilepsy patients. Direct electrical stimulation allows for targeting focal brain structures in order to modulate activity in complex neural networks [] and can be precisely timed to target specific encoding events, offering some advantages over non-invasive methods [].

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

Memory depends on encoding processes that lay down neural representations of experiences for long-term storage []. Recordings taken during laboratory memory tasks demonstrate that neural activity in the hippocampus, medial temporal lobe (MTL) cortex, frontal lobe, and parietal lobe [] differentiates learned information that is likely to be remembered from information likely to be forgotten. These effects extend to other brain areas [] and exist both during and prior to when a to-be-remembered stimulus is present []. This suggests that coordinated activity in a distributed neural network generates states that are responsible for effective memory encoding.

When stimulation was delivered in low states, both recall performance and classifier evidence increased. We next asked how increased classifier evidence relates to stimulation-evoked changes in neural activity across the brain. To measure stimulation’s effect on neural activity, we used an index of the spectral tilt, which is characterized by increased high-frequency power simultaneous with widespread decreases in low-frequency power. These spectral modulations are thought to reflect both local increases in multi-unit firing [] and decreased long-range low-frequency synchrony []. Evidence for these patterns correlates with the fMRI blood-oxygen-level-dependent (BOLD) signal [], predicts successful memory encoding [], and is related to core memory processes such as item-context binding []. We found that the change in classifier output after stimulation was related to how much stimulation evoked the tilt pattern (r(37) = 0.54, p < 0.001; Figure 5 ), suggesting that stimulation increased classifier evidence by modulating a neural marker that has been linked to contextual memory encoding.

Different frequencies for different scales of cortical integration: from local gamma to long range alpha/theta synchronization.

Stimulation enhanced recall performance when delivered just after low encoding states (t(38) = 2.26, p < 0.03) but decreased performance when delivered just after high encoding states (t(38) = −2.09, p < 0.05; low-high difference t(38) = 3.32, p < 0.003; Figure 4 C). We compared the classifier estimates of the brain’s encoding state post- and pre-stimulation, which showed that low-state stimulation increased evidence for good encoding (p < 0.02; Figure 4 D) whereas high-state stimulation decreased evidence for good encoding (p < 0.001; Figure 4 E). The data suggest that stimulation influences memory function by perturbing the brain’s encoding state relative to its status at the time of delivery.

Although stimulation had inconsistent effects overall, we predicted the pre-stimulation encoding state would account for some of this variability. In subjects who showed above-chance classifier generalization (N = 39 datasets from 27 subjects), we applied the classifiers to intervals just prior to each stimulation train ( Figure 4 A) and split the resulting distribution of classifier outputs into low and high bins, based on the optimal classification threshold from the previous record-only sessions. Low pre-stimulation encoding states were associated with decreased high-frequency power in widespread brain areas that predicted memory performance during word encoding, including the frontal, temporal, and parietal cortex ( Figure 4 B).

(C) Recall performance increased if stimulation was delivered when the brain was in a low encoding state (p < 0.03) and decreased if delivered in a high encoding state (p < 0.05). The difference between low and high stimulation was also significant (p < 0.003). Red bars show mean SE of the difference.

(B) Spectral power prior to stimulation onset was significantly lower at high frequencies in frontal, temporal, parietal, and occipital cortex (FDR corrected at q = 0.05).

We next asked whether encoding stimulation tended to facilitate or disrupt recall performance. Within-subject, stimulation significantly increased recall performance in two subjects and decreased recall performance in six subjects (χtest, p < 0.05). Across the group, stimulation reliably decreased memory performance (Δ normalized recall −6.8% ± 3.2%, p < 0.04; Figure 3 B), but there was considerable variability in stimulation’s effects across individuals, ranging from large memory disruption to facilitation (SD = 22.5%). This heterogeneity is consistent with past work [], and meant that the small difference in overall recall performance was not accompanied by specific group-level differences in recall organization, measured using two traditional assays of human memory performance, the serial position curve and lag conditional response probability curve ( Figures 3 C and 3D).

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

Having established that classification discriminates encoding states, we asked whether stimulation modulates these states in a way that influences memory performance. On stimulation (Stim) lists, we applied 50-Hz trains across a single pair of electrodes at parameters previously used to modulate spatial memory in humans []. We then used each subject’s record-only classifier to decode neural activity during stimulation sessions (N = 52 stimulation datasets from 36 subjects). We first tested classifier generalization to the stimulation sessions. Our experimental design included lists without stimulation (NoStim lists) to serve as a baseline for behavioral performance and for testing between-session classifier generalization ( Figure 3 A). The classifier significantly discriminated encoding activity for recalled and forgotten words (mean AUC on NoStim lists 0.61 ± 0.01, t(51) = 10.4, p < 10), even though recall performance was slightly higher for NoStim lists compared to record-only sessions (record-only 30.6% ± 1.7%, NoStim lists 33.5% ± 1.9%, t(51) = 2.6, p < 0.02). This suggests that the relation between neural activity and memory states was stable from the record-only to the stimulation sessions.

(C and D) Recall probability as a function of serial position (C) and inter-item lag (D) does not significantly differ as a function of stimulation condition.

(A) We applied stimulation across alternating pairs of words on Stim lists; NoStim lists were devoid of stimulation.

We derived a forward model [] for each subject using classifier weights, accounting for covariance between features in the input data, to estimate the relative importance of each region × frequency feature for classifier performance. This showed that the classifier relied on widespread low-frequency power decreases simultaneous with high-frequency power increases across the frontal, temporal, and occipital cortex, as well as in the hippocampus, to predict successful recall ( Figure 2 F). We observed a similar pattern when contrasting power for remembered and forgotten words ( Figure 2 G), with the MTL and parietal cortex also showing high-frequency power increases. This echoes prior work in intracranial and scalp EEG [] and suggests consistency in the features that predict efficient memory function across people.

(B and D) AUC for both subjects was significantly greater than chance. (B) Example patient 1. (D) Example patient 2.

(A and C) Classifier output probability for an eight-list period of the delayed free recall task in two example subjects. Dashed lines indicate the optimal decision threshold dividing recalled from forgotten trials. Red, later recalled words; blue, later forgotten words. (A) Example patient 1. (C) Example patient 2.

One hundred and two subjects participated in the record-only phase of the study. Subjects performed a free recall memory task during which they studied at least 25 lists of 12 unrelated words, with each list followed by a 20 s mental arithmetic distractor task (a subset of subjects also performed additional sessions of free recall with categorized word lists). Subjects then freely recalled the words from the list in any order ( Figure 1 A; mean recall = 27.2% ± 1.2%; SEM). For each encoded word, we computed the time-frequency decomposition of the iEEG signal for each bipolar electrode pair (50 frequencies between 1 and 200 Hz; Figure 1 B). We used these estimates of spectral power at each frequency and electrode, for each encoded word, as input for training a logistic regression classifier. We employed L2 penalization to avoid overfitting, then assessed performance using N − 1 cross-validation by experimental session and area under the receiver-operating characteristic curve (AUC), a standard measure of a classifier’s ability to generate true positives while avoiding false positives.

(B) The electrode frequency pattern of spectral power for each word-encoding period was used as input ( X ) to fit a classifier to discriminate recalled from forgotten patterns (resulting weight; w ). We assessed classifier performance using area under the receiver-operating characteristic curve (AUC).

Discussion

27 Fell J.

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Dogali M. Left temporal neocortex mediation of verbal memory: evidence from functional mapping with cortical stimulation. We applied direct electrical stimulation to nodes of the memory network and found that stimulation reliably modulates memory in a way that depends on the brain’s encoding state. In showing that stimulation improves memory in low encoding states and disrupts memory in high encoding states, the findings suggest that stimulation alters the ongoing course of memory processing in the brain. By using brain-state-matched trials from non-stimulated lists, our data show that stimulation modulates neural activity beyond what might be expected by regression to the mean arising from temporal autocorrelation in the brain’s encoding state. Our data offer insight into the inconsistent effects that have been reported in studies of how brain stimulation modulates memory performance [], and suggest that using brain-state decoding can improve the ability to influence memory outcomes with stimulation.

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de Rougemont J. Combined (thalamotomy and stimulation) stereotactic surgery of the VIM thalamic nucleus for bilateral Parkinson disease. 12 McIntyre C.C.

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Pascual-Leone A. Resting-state networks link invasive and noninvasive brain stimulation across diverse psychiatric and neurological diseases. Our results are consistent with a model in which targeted stimulation leads to changes in network activity across brain areas that contribute to successful memory encoding. There is growing consensus that direct electrical stimulation is likely to influence physiology across a network connected to the targeted site []. In using DBS for the treatment of Parkinson’s [], for example, researchers have had success targeting multiple structures within the affected motor network [], which suggests that it is more important to target the relevant functional network rather than individual structures within the network. In the case of episodic memory, it may therefore be possible to enhance the effectiveness of stimulation by using measures of connectivity to identify nodes that offer a high degree of controllability over the memory network []. Resting-state data could be leveraged to predict the stimulation targets that are most likely to modulate the core memory network [].

26 Pollen D.A. Responses of single neurons to electrical stimulation of the surface of the visual cortex. 24 Hyman J.M.

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Winson J. Long-term potentiation in the dentate gyrus is induced preferentially on the positive phase of theta-rhythm. 16 Seager M.A.

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Berry S.D. Oscillatory brain states and learning: impact of hippocampal theta-contingent training. Prior work has shown that stimulation’s effects on physiology depend not only on the excitability of the targeted neurons [] but also on ongoing rhythms generated by synchronous activity in larger populations. Hippocampal stimulation, for example, alternately promotes long-term potentiation or long-term depression depending on whether theta phase is at the peak or trough at the time of stimulation delivery []. Learning itself is also state dependent, as shown in classical conditioning experiments in which animals show faster learning when trials are triggered based on theta rhythm []. Our data confirm the role of pre-stimulus brain states for upcoming learning and show that these states can be directly modulated.

17 Burke J.F.

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Kahana M.J. Human intracranial high-frequency activity during memory processing: neural oscillations or stochastic volatility?. 45 Wang J.X.

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Voss J.L. Targeted enhancement of cortical-hippocampal brain networks and associative memory. We applied classification to whole-brain iEEG to decode brain states that predict later recall. Multivariate decoding allowed us to overcome individual differences in neural connectivity, clinical etiology, and electrode placement that could increase variability in stimulation’s neural and behavioral effects. Decoding may also have provided a more sensitive index of the encoding state than would be possible if using a single feature to identify good and poor memory states []. We then related stimulation’s effect on physiology to its effect on memory, extending prior work that has used stimulation to map behavior. Using direct brain recordings most likely facilitated decoding, but future work should address the extent to which non-invasive decoding and stimulation methods [] could be combined to modulate memory states.

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Kahana M.J. Prestimulus theta in the human hippocampus predicts subsequent recognition but not recall. We used the classifier trained on encoding data to decode pre-stimulation states. Our approach suggests that, at a broad level, similar whole-brain patterns of neural activity predict successful encoding during and prior to stimulus onset. In both training and testing our classifier, we averaged spectral power over a temporal interval of several hundred milliseconds, meaning our model was sensitive to consistent spectral power fluctuations over the pre- and post-stimulus intervals. There is evidence that assessing neural activity at a finer temporal scale can identify distinct patterns of pre- and post-stimulus activity that predict encoding. Increased pre-stimulus theta power recorded using non-invasive methods, for example, has been shown to predict successful memory [], and increased intracranial theta power has also been shown to predict memory pre-stimulus [], although not during free recall. Taken together with our data, these findings suggest that both tonic and phasic pre-stimulus signals are predictive of memory success. Algorithms to identify good encoding states could therefore be improved by incorporating time as a feature, which would allow both sustained and transient fluctuations in spectral power to influence estimates of the encoding state.

20 Lohnas L.J.

Polyn S.M.

Kahana M.J. Expanding the scope of memory search: modeling intralist and interlist effects in free recall. 41 Long N.M.

Kahana M.J. Successful memory formation is driven by contextual encoding in the core memory network. By testing classifier generalization across days and using the free recall task to measure memory performance, our data support the interpretation that the decoded brain states are stable in their neural representation over time and globally predict memory function. Free recall is a complex task that recruits multiple core episodic memory processes []. We show that stimulation increases encoding states by increasing high-frequency activity (HFA) power and decreasing low-frequency activity (LFA) power across the brain, a pattern that predicts behavioral measures of item-context encoding []. Although such encoding processes promote memory function, free recall is also known to depend heavily on retrieval processes, suggesting that future work may find success influencing memory function by applying stimulation during memory search.

31 Jacobs J.

Miller J.

Lee S.A.

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Berry B.

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et al. Direct electrical stimulation of the human entorhinal region and hippocampus impairs memory. 48 Young N.P.

Deisseroth K. Cognitive neuroscience: in search of lost time. We show an overall reduction in verbal memory performance when stimulating a large set of brain regions, including many outside of the MTL. This is broadly consistent with recent work focused on the hippocampus and entorhinal cortex that showed that stimulation tends to impair both verbal and spatial memory []. However, we further test the hypothesis that stimulation’s effects on memory depend on timing relative to the brain’s encoding state []. Our findings therefore extend prior studies of human intracranial brain stimulation in several ways. First, we use multivariate decoding of neural activity to separate pre-stimulation brain states, and show that stimulation counteracts low encoding states but disrupts high encoding states. Second, we show that stimulation at low and high encoding states differentially modulates neural activity in a manner consistent with the effect on memory. Third, we show that stimulation’s effect on the encoding state is correlated with the spectral tilt, a biomarker of successful memory encoding. Our work therefore identifies situations in which stimulation increases and decreases memory, and relates stimulation’s effects on behavior to its influence on neural activity through novel use of subject-specific multivariate classification.