The noisy brain: power of resting-state fluctuations predicts individual recognition performance

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Article

Reference

The noisy brain: power of resting-state fluctuations predicts individual recognition performance

GROSSMAN, Shany, et al.

Abstract

The unique profile of strong and weak cognitive traits characterizing each individual is of a fundamental significance, yet their neurophysiological underpinnings remain elusive. Here, we present intracranial electroencephalogram (iEEG) measurements in humans pointing to resting-state cortical "noise" as a possible neurophysiological trait that limits visual recognition capacity. We show that amplitudes of slow (

GROSSMAN, Shany, et al . The noisy brain: power of resting-state fluctuations predicts individual recognition performance. Cell Reports , 2019, vol. 29, no. 12, p. 3775-3784.e4

DOI : 10.1016/j.celrep.2019.11.081 PMID : 31851911

Available at:

http://archive-ouverte.unige.ch/unige:142595

Disclaimer: layout of this document may differ from the published version.

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Report

The Noisy Brain: Power of Resting-State Fluctuations Predicts Individual Recognition Performance

Graphical Abstract

Highlights

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Intracranial recordings in 26 patients during rest sessions and visual 1-back tasks

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Amplitude of slow fluctuations at rest was negatively correlated with performance

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The effect was specific to fluctuations in task-related cortical sites

Authors

Shany Grossman, Erin M. Yeagle, Michal Harel, ..., David M. Groppe, Ashesh D. Mehta, Rafael Malach

Correspondence

[email protected]

In Brief

The amplitude of neural fluctuations during rest varies between individuals and cortical networks. Using intracranial recordings in patients, Grossman et al.

find that the amplitudes of slow (<1 Hz) fluctuations during rest are predictive of individual differences in recognition memory performance, a link that is specific to task-relevant cortical sites.

Grossman et al., 2019, Cell Reports29, 3775–3784 December 17, 2019ª2019 The Author(s).

https://doi.org/10.1016/j.celrep.2019.11.081

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Cell Reports

Report

The Noisy Brain: Power of Resting-State Fluctuations Predicts Individual

Recognition Performance

Shany Grossman,^1,2Erin M. Yeagle,³Michal Harel,^1,2Elizabeth Espinal,³Roy Harpaz,^1,2,4Niv Noy,^1,2Pierre Me´gevand,^3,5 David M. Groppe,^3,6Ashesh D. Mehta,³and Rafael Malach^1,2,7,*

1Department of Neurobiology, Weizmann Institute of Science, Rehovot 76100, Israel

2The Azrieli National Institute for Human Brain Imaging and Research, Weizmann Institute of Science, Rehovot 76100, Israel

3Department of Neurosurgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, and Feinstein Institute for Medical Research, Manhasset, NY 11030, USA

4Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA

5Neurology Division, Clinical Neuroscience Department, Geneva University Hospital and Faculty of Medicine, Geneva 1205, Switzerland

6The Krembil Neuroscience Centre, Toronto, ON M5T 2S8, Canada

7Lead Contact

*Correspondence:[email protected] https://doi.org/10.1016/j.celrep.2019.11.081

SUMMARY

The unique profile of strong and weak cognitive traits characterizing each individual is of a fundamental significance, yet their neurophysiological underpin- nings remain elusive. Here, we present intracranial electroencephalogram (iEEG) measurements in hu- mans pointing to resting-state cortical ‘‘noise’’ as a possible neurophysiological trait that limits visual recognition capacity. We show that amplitudes of slow (<1 Hz) spontaneous fluctuations in high-fre- quency power measured during rest were predictive of the patients’ performance in a visual recognition 1-back task (26 patients, total of 1,389 bipolar con- tacts pairs). Importantly, the effect was selective only to task-related cortical sites. The prediction was significant even across long (mean distance 4.6

±

2.8 days) lags. These findings highlight the level of the individuals’ internal ‘‘noise’’ as a trait that limits performance in externally oriented demanding tasks.

INTRODUCTION

Each human individual exhibits a unique profile of strong and weak capacities. Some individuals that excel in chess may be poor in creative writing, while others that are prolific poets may be slow in math. Thus, human individuals greatly differ in their ability to perform a wide range of cognitive tasks. These unique cognitive profiles impact upon numerous life factors, ranging from career choices, through life styles, to even survival. Yet little is knwon about the possible neurophysiological characteristics that underlie these individual differences.

One process that could potentially limit performance is suggested by previous studies in primates. These studies have shown that when placed under demanding task conditions, local noise fluctuations in neuronal responses evoked by visual

stimuli are downregulated (Churchland et al., 2010; Cohen and Maunsell, 2009; Mitchell et al., 2009). Similar findings have been reported also during human performance using electroencephalogram (EEG) (Arazi et al., 2017; Schurger et al., 2015) and fMRI (Broday-Dvir et al., 2018). Similarly, reduced spontaneous fluctuations prior to the onset of visual targets have been linked to improved task performance on a single trial basis in humans (Ghuman et al., 2014; He and Zempel, 2013), as well as in rats anticipating auditory signals (Carcea et al., 2017). These noise reductions have been demonstrated in tasks that require a steady focus on a target, suggesting that such externally oriented demanding tasks are naturally prone to disruption by intrinsic fluctuations. Interestingly, when the task may benefit from correlated noise fluctuations, the relevant networks are capable of upregulating the amplitude of their shared fluctuations (Ruff and Cohen, 2014).

However, it is important to distinguish such state changes that may transiently affect performance through modulations in arousal, fatigue, etc. from trait differences that constitute a long-term upper limit on specific individual cognitive capabilities.

Regarding such trait variations, previous studies have demonstrated a correlation between anatomical-structural differences and cognitive capacity (e.g., Fleming et al., 2010; Forstmann et al., 2010; Gomez et al., 2017; Kable and Levy, 2015), yet the neurophysiological traits that may account for long-term inter-individual variations in specific cognitive traits remain elusive.

In light of the evidence suggesting that neuronal noise may be downregulated during task performance, we sought to examine whether individuals with inherently high levels of spontaneous fluctuations such as those measured during resting state (e.g., Koukouli et al., 2016; Nir et al., 2008) may also show hampered performance in a demanding cognitive task. To examine this hy- pothesis, we obtained intracranial recordings from patients un- dergoing clinical monitoring to identify epileptic foci. Specifically, we tested the prediction that individuals with inherently high levels of noise in the visual system, as reflected in the amplitude of spontaneous fluctuations during rest, will show decreased performance on a 1-back visual task.

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Previous research that examined the relationship between the amplitude of resting-state fluctuations and performance using non-invasive methods provides a partial and somewhat incon- sistent account of this relation. Blood-oxygen-level-dependent (BOLD) fMRI fluctuations (<0.08 Hz) in fronto-parietal networks were found to be positively linked to working memory performance (van Dam et al., 2015). By contrast, fronto-parietal large-scale fluctuations in MEG recordings (up to 7 Hz) were reported to be negatively linked to performance in working memory tasks (Heister et al., 2013).

Here, we aimed to resolve these inconsistencies using invasive iEEG measures in patients, a signal that offers important advantages over noninvasive methods. Relative to BOLD, iEEG shows a more direct link to neuronal activity and a superior temporal resolution. Importantly, iEEG allows a high spatial resolution even compared to BOLD-fMRI (Privman et al., 2007) and more so when compared to the relatively low

anatomical resolution of source localization in scalp EEG or MEG recordings.

Results

Task-Related and Task-Unrelated Recording Sites Given that our study aimed at searching for individual differences, we obtained a relatively large iEEG dataset, consisting of 26 patients (Table S2). The experimental design is depicted inFigure 1A.

All patients performed a visual 1-back task that consisted of brief image presentations from six different visual categories. Images were presented at a fixed pace of 1 Hz for 250 ms. Patients were instructed to maintain fixation and report the occurrence of infrequent (12%) image repeats. Thirteen out of the 26 individuals participated in an additional visual 1-back task, with a similar design to that of the first version (seeSTAR Methods). Critically, in either one or two separate rest sessions (separated by a mean of 56±61 h from the task) patients were asked to rest

LH RH

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1-back visual task resting-state

V1 (BA 17) V2 (BA 18)

Intermediate retinotopic Fusiform Gyrus

Visually responsive bipoles Hit responsive bipoles Task-unrelated bipoles P

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Figure 1. Experimental Paradigm and Contacts Localization

(A) During the visual 1-back task, images from different categories were flashed on the screen at a 1 Hz rate for 250 ms. Patients were instructed to maintain fixation and detect infrequent image repeats. In separate sessions (mean distance 56.6±61.5 h), one to two closed eyes resting-state periods (mean duration 6.7

±1.9 SD minutes) were collected for each patient.

(B) Contacts localization of the 26 patients plotted on the flattened (upper left) and inflated (posterior-ventral view, upper right; lateral view, lower left; medial view, lower right) common cortical surface. All contacts constituting a bipole are presented. Bipoles were classified as visually-responsive, hit-responsive, and task- unrelated, based on their activation profile during the task. The remaining bipoles, colored in black, were defined as intermediate. Colored labels on the cortical surface were derived from surface-based atlases as implemented in FreeSurfer 5.3 (seeSTAR Methods).

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with their eyes closed (mean duration 6.7±1.9 min; two rest periods collected for 9 out of the 26 patients; seeTable S2for individual rest durations and temporal distances from task).

To remove global and local artifacts, we employed bipolar re- referencing whereby a single recording site was defined as the difference in raw local field potential (LFP) across a neighboring pair of contacts (the term ‘‘bipole’’ is used hereafter to denote a channel received from a bipolar referenced pair of contacts).

Based on the high-frequency broad-band signal (HFB, 45–154 Hz, also termed at times high-frequency amplitude [HFA]; see STAR Methods), bipoles were classified as task- related, comprising both visually-responsive and hit-responsive bipoles, and task-unrelated, according to their activation profile during the visual 1-back task. Visually-responsive bipoles were defined as those significantly responsive to all visual stimuli (paired t test, p < 0.01, Bonferroni correction applied per patient) and exhibiting an effect size (Glass’D) greater than 1 for at least A

B

log(frequency) Correlation with d’ (Pearson’s r)HFB (<1 Hz) during rest (envelope of demeaned signal, μV)

p_pearson<0.05, cluster corr.

p_spearman<0.05, cluster corr.

5 sec P140, d’=4.16

P070, d’=4

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Figure 2. Spectral analysis reveals a negative link between slow HFB fluctuations during rest and task performance

(A) Examples of resting-state HFB time series from four patients, presented in a descending order of performance scores. Each panel corresponds to a different patient, with HFB signals from three visually-responsive bipoles presented in super- position. The signal is the low-passed HFB (<1 Hz;

see following result below). Anatomical locations of displayed bipoles are presented on the right (red circles denote the mean location of the two contacts constituting a bipole). Note the substantial differences in the fluctuations’ amplitude across individuals.

(B) Correlation of performance in the visual 1-back task with HFB power during rest in different frequency bands (20 logarithmically spaced bands ranging from 0.03 to 10 Hz). Pearson’s correlation coefficients are plotted along the y axis for each frequency band. p values were derived from a permutation test and a cluster threshold was applied to correct for multiple comparisons (see STAR Methods). Note the negative correlation at lower-frequency bands (up to1 Hz).

one of the visual categories. Similarly, hit- responsive bipoles were defined as those significantly responsive to all hit trials (Wilcoxon signed rank test, p < 0.01, Bon- ferroni correction applied per patient) and exhibiting an effect size greater than 1 (see STAR Methodsfor details andFig- ure S1A for individual response profiles).

Task-unrelated bipoles were defined as those with minimal to no task-related activation with an effect size of up to 0.5 for any of the visual categories, located up to 10 mm from cortical tissue.

Out of 1,389 bipoles analyzed in total, a subset of 229 bipoles, ranging from 3 to 19 per patient (mean 8.8±3.7 SD), were found to be visually- or hit-responsive and were defined as task-related bipoles, with most located in the visual cortex (Figure 1B), including early and high-order visual areas. 299 bipoles met the criteria for task-unrelated bipoles, ranging from 3 to 25 per individual (mean 11.5±7 SD). The remaining bipoles, showing an intermediate effect size of 0.5–1, were defined as the intermediate subset, consisting of 344 bipoles in total.

Amplitude of Slow Fluctuations in Task-Related Sites during Rest Is Negatively Linked to Performance

Examining the spontaneous fluctuations measured during rest in task-related recording sites revealed an intriguing diversity in their amplitudes across patients. These differences could be discerned even by direct inspection of the resting-state HFB time courses. Examples of individuals with high- and low-amplitude fluctuations are depicted inFigure 2A.

We next examined whether the individual differences in the amplitudes of spontaneous fluctuations during rest may be

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predictive of the patients’ performance in the visual 1-back task.

To this end, we computed the correlation between the fractional amplitude (power) of resting-state fluctuations and patients’ performance in 20 logarithmically spaced frequency bands (note that these are frequencies of fluctuations in HFB, and not the fluctuations of the raw iEEG signal; seeSTAR Methods).Fig- ure 2B illustrates the results of this analysis. As can be seen, the relative amplitude of resting-state fluctuations showed a negative correlation with patients’ performance, measured by their d⁰sensitivity indices. Moreover, a clear bias toward low frequencies of HFB fluctuations (up to1 Hz) was observed, as the correlation rapidly diminished at higher frequencies (all non- parametric p values <0.05 for both Pearson’s r and Spearman’s r, permutation-based cluster correction applied). A positive trend was observed for the top-three frequency bands (4.33–

10 Hz); however, this effect did not survive cluster correction.

Similar significant correlations up to 1 Hz were also found without applying fractional normalization.

For a more direct inspection of the observed correlation with the HFB low-frequencies range, we estimated the fractional power of slow resting-state HFB fluctuations using the variance of the signal. Here, we quantified the amplitude of slow HFB fluctuations as the variance of the low-passed (<1 Hz) HFB signal, divided by the variance of the unfiltered signal. We then generated scatterplots depicting the relationship between the mean fractional variance across task-related bipoles and patients’

performance. The resultant plots are presented inFigure 3A. As indicated by the spectral analysis, the amplitude of slow HFB fluctuations in task-related regions was significantly anti-correlated with d⁰ scores (Spearman’s r = 0.66, non-parametric p = 0.0006; Pearson’s r =0.62, non-parametric p = 0.0009). Further- more, the correlation was maintained when averaging the two d⁰scores of the 13 individuals who participated in an additional visual 1-back task (Spearman’sr=0.68, non-parametric p = 0.0003; Pearson’s r =0.66, non-parametric p = 0.0005;Fig- ure 3A, left panel). This result was preserved when taking the me- dian instead on the mean value of the fractional variance across bipoles (single task d⁰:r=0.63, non-parametric p = 0.0005;

two tasks d⁰:r=0.62, non-parametric p = 0.0004).

Fractional HFB variance was highly correlated between repeated rest sessions (n = 9, Pearson’s r = 0.81, non-parametric p = 0.005, bootstrap SE = 0.14) and d⁰indices were highly correlated between the two tasks (n = 13, Pearson’s r = 0.68, non-parametric p = 0.006, bootstrap SE = 0.12). To further examine the sta- bility of the resting-state fractional variance measure, we re- computed its correlation with performance while including only the early or late halves of resting sessions. Both halves resulted in equivalent correlations to those observed when analyzing the complete rest sessions (early half:r=0.63 andr=0.63 for single and two tasks, respectively; late half:r=0.64 andr=0.66 for single and two tasks, respectively; all non-parametric p

<0.001). In addition, matching all rest sessions in duration by trun- cating them according to the shortest rest session (1.5 min) and re-computing the correlation with performance yielded similar results (r=0.58, non-parametric p = 0.002 for both single and two tasks).

To examine whether the effect was sensitive to the definition of bipoles as task-related (i.e., the choice of effect size threshold

for task-related bipoles), we gradually changed the minimal effect size (Glass’ D) threshold from 0.5 to 1.5 (Figure S1B).

The effect remained highly similar following these threshold manipulations, as well as when including all statistically significant task-related bipoles, i.e., without applying an additional minimal effect size threshold (Figure S1B).

We next tested whether the effect can be accounted for by various confounding variables. To this end, we tested the relation of five possible confounding measures with both the mean fractional variance (<1 Hz) across task-related bipoles and performance. There was no significant correlation with the number of task-related bipoles, arguing against the possibility that un- equal sampling across individuals contributed to the effect. In addition, no correlation was found with the severity of symp- toms, as measured by self-reported seizure frequency; the number of years passed since the first seizure; or with the age of participants. Last, we found no correlation with the minimal distance of task-related bipoles to contacts located over the seizure onset zone.Table S1presents the exact correlation coefficients and p values for these five possible confound variables.

Moreover, the correlation between performance and resting- state fluctuations’ amplitude remained highly significant when computing the partial correlations, with the effect of each of these five control variables partialled out (all p values for either Pearson’s r or Spearman’sr= < 0.001; seeTable S1).

An interesting aspect of the current dataset is the extent of covariation between task-related bipoles. The mean pairwise correlation between slow HFB (<1 Hz) activity fluctuations in task-related sites varied among patients, ranging from 0.05 to 0.51 (seeFigure S2for individual correlation matrices). Inter- estingly, the degree of mean pairwise correlation per patient was significantly correlated with the fractional power of HFB fluctuations (r= 0.81, non-parametric p = 0.001) and was negatively correlated with performance (r=0.55, non-parametric p = 0.005). The latter effect persisted after partialling out the mean pairwise distances between task-related bipoles (partial r=0.52, non-parametric p = 0.001).

Amplitude of Slow Fluctuations in Task-Unrelated Sites during Rest Is Not Predictive of Performance

Was the effect specific to the task-related cortical sites or was it a more general phenomenon reflected in fluctuating activity across the entire cortical mantle? To examine this question, we compared the correlation with performance of task-related bipoles to the correlation with the performance of the task-unrelated bipoles. The results of this analysis are shown inFigure 3B.

As can be seen, by contrast to task-related bipoles, the set of task-unrelated bipoles was not significantly anti-correlated with performance. A statistical comparison of the predictive power of task-related versus task-unrelated bipoles revealed a significant difference between the two correlations (permutation test on absolute correlations difference: single task d⁰, p = 0.006;

two tasks d⁰, p = 0.004; bootstrap 95% CI on correlations difference: single task d⁰, [0.88, 0.23]; two tasks d⁰, [0.87, 0.19]), indicating that the link between spontaneous fluctuations during rest and task performance was specific to task- related cortical sites rather than being a whole-cortex phenomenon. Equivalent null correlations were obtained when gradually changing the maximal effect size (Glass’D) threshold for defining

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A

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HFB var.

during rest in task-related sites

HFB var.

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seizure

frequency yrs passed

since 1st seizureminimal distance of task-related sites to seizure onset zone

# task-related sitesage

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^n.s. ^n.s. ^n.s. ^n.s. ^n.s. ^n.s.

D

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HFB fractional variance (<1Hz) during rest

1 1.5 2 2.5 3 3.5 4 4.5

d'

U= -0.66***

pperm= 0.0006

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d' (2 tasks)

U= -0.68***

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Correlation between actual and predicted performance (N=13)

0 100 200 300 400

Count

Shufﬂed data Task-unrelated Task-related

Task-related bipoles

Task-unrelated bipoles

Figure 3. Amplitude of Slow HFB Fluctua- tions during Rest Is Anti-Correlated with Performance in Task-Related Cortical Sites (A and B) Scatterplots depict the relation between task performance across patients (n = 26) and the fractional variance of slow (<1 Hz) resting-state fluctuations in task-related (A) and in task-unrelated (B) bipoles. Panels on the right show the same correlation while averaging two independent d⁰estimates (available in 13 individuals). For each patient, the fractional resting-state variance (<1 Hz) was averaged across the relevant subset of bipoles (task-related/task-unrelated). Error bars denote SEM across bipoles. Black lines are the least-squares regression fits, with gray bounds indicating 90% bootstrap confidence intervals. p values for the reported Spearman’srwere derived from a permutation test.

(C) Distribution of linear correlation coefficients between actual and predicted d⁰ scores. Pre- dictions were based on linear fits to 10,000 random half-splits of the patients and tested on the held-out halves.

(D) Beta estimates of predictors modeled in a general linear model constructed to explain performance differences. Error bars denote SE. Only fractional variance in task-related sites during rest was a significant predictor (beta =6.9±1.5 SE, p = 0.0004; last four beta estimates are relatively negligible and can hardly be discerned).

Task-related bipoles

Task-unrelated bipoles

0.1 0.15 0.2 0.25 0.3 1

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Correlation between actual and predicted performance (N=13) Pre-stimulus trial to trial CV (-200 to 0 ms)

U= -0.45*

Figure 4. Pre-stimulus Variability Is Anti- Correlated with Performance in Task- Related Cortical Sites

(A and B) Scatterplots depict the relation between trial-to-trial variability, measured by the coefficient of variance (CV) across pre-stimulus segments (200–0 ms) during the visual 1-back task, and performance across patients (n = 26). Shown are (A) task-related and (B) task-unrelated bipoles. P values were derived from a permutation test. Error bars denote SEM of CV across bipoles. Black lines are the least-squares regression fits, with 90%

bootstrap confidence intervals indicated by gray bounds.

(C) Distribution of linear correlation coefficients between actual and predicted d⁰ scores; same analysis as inFigure 3C.

See alsoFigure S4.

task had no impact on the effect. Further- more, there was no significant difference between the correlations obtained while including only pre- or post-task rest periods (pre-taskr = 0.68, Pperm = 0.04, n = 9; post-taskr=0.62, Pperm = 0.0008, n = 26; permutations test on absolute difference between correlations, p = 0.85).

LFP Fluctuations Amplitude during Rest and Task Performance

Finally, we examined whether the effect was confined to HFB or whether the fluctuations’ amplitude in the raw iEEG signal also showed a similar correlation with performance. Applying the same spectral analysis on the raw signal, as we did on the HFB (Figure 2B; see STAR Methods), revealed a significant negative correlation in low (delta) iEEG frequencies (0.3–2.7 Hz) with d⁰ indices (all r < 0.43, all non-parametric p <

0.05). Could this result indicate that delta frequencies were directly correlated with HFB modulations? To examine this possibility, we computed the correlation between the fractional variance of the delta LFP with the fractional variance of the HFB modulation for all task-related bipoles (n = 229). The result indicates a mild but significant correlation (r = 0.44, non-parametric p = 0.001).

Discussion

The current findings highlight local network ‘‘noise’’ during rest, reflected in the amplitude of slow spontaneous fluctuations in the HFB of local field intracranial recordings, as a possible process that constrains individual performance in specific tasks.

The results provide a demonstration that the amplitude of HFB resting-state fluctuations captures relevant information about the individual and should not be disregarded as measurement noise (Kirst et al., 2016).

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We used bipolar reference, which greatly reduces local and global signal artifacts, and carefully examined various con- founds that may have contributed to the effect, such as the severity of the patients’ condition —seizure frequency, years passed since seizure onset, bipoles’ distance from seizure onset zone, and age. All these turned out to be negligible in affecting the observed correlation with performance (Figure 3D;

Table S1). Moreover, the task selectivity of our effect further ar- gues against global factors such as disease state as driving the effect.

It could be argued that the link found between spontaneous fluctuations during rest and task performance reflects a tempo- rary state modulation in the patients’ fatigue or arousal. How- ever, two aspects of our results argue against such a possibility.

First, the link between performance and resting-state fluctuations was not related to the temporal separation between the measurements of the two, and was perserved when including only rest periods obtained at least a day apart from the task (Fig- ure S4D). Second, the effect was specific to task-related bipoles, whereas prior studies linking vigilance modulations at seconds (Chang et al., 2016) and minutes (Wong et al., 2013) timescales to resting-state fMRI patterns have found a widespread cortical involvement.

The current results thus indicate that resting-state fluctuations can be diagnostic of individual long-term cognitive capabilities.

While the particular cognitive construct implicated by the 1-back visual task used here has not been directly studied, performance on a 2-back visual task has been found to moderately correlate with fluid intelligence (Jaeggi et al., 2010), as well as with psychometric measures of processing speed and atten- tional focus (Gajewski et al., 2018; Miller et al., 2009). It should be emphasized that the putative constraints imposed by erratic cortical activity studied here are likely to be specific to tasks that require maintained external focus over extended periods of time and are therefore particularly susceptible to such intrinsic disruptions, whereas other, more internally generated behaviors, may benefit from higher variability (for a detailed review seeDin- stein et al., 2015). Particularly, it is tempting to speculate that when more spontaneous actions are called for, such as voluntary movements or free recall, then the intrinsic fluctuations may actually serve a performance-enhancing role in eliciting fluidity in the emergence of internally generated events (Moutard et al., 2015).

Our results revealed a clear bias toward low frequencies (<1 Hz) in the correlation with behavior (Figure 2B). This bias may result from the power-law behavior of resting-state fluctuations in which low frequencies of HFB power modulations have larger amplitude and may therefore be less susceptible to measurement noise.

In addition to the HFB fluctuations amplitude, we found that the mean pairwise correlations between these fluctuations across task-selective bipoles was positively correlated with the fluctuations’ magnitude as well as negatively correlated with performance. This link between the strength of resting- state fluctuations and their spatial spread is interesting. We have previously suggested that the ultra-slow nature of the fluctuations is a result of activity accumulation through network interactions (Moutard et al., 2015). The present findings are

compatible with this notion in showing that the more widespread the network correlations are, the higher is the fluctuation magnitude.

It is important to distinguish between resting-state functional connectivity, mostly studied using BOLD-fMRI (but observed also in iEEG; seeKucyi et al., 2018andNir et al., 2008), and the aspect of resting-state activity studied here, which focused on the local amplitude of intrinsic fluctuations rather than on their inter- and intra-regional correlations. Previous research examining the behavioral relevance of resting-state functional connectivity showed that it is, at least partially, sculpted by experience (Harmelech and Malach, 2013; Harmelech et al., 2013; Strappini et al., 2018; Wilf et al., 2017) and suggested links to individual differences in performance (e.g., to perceptual learning abilities; Baldassarre et al., 2012). The relation between the amplitude of local resting-state fluctuations and functional connectivity in fMRI is not yet fully elucidated (but seeGarrett et al., 2018for a recent study).

A straightforward consequence of the proposed negative link between resting-state variance and differences in performance is that a similar link should be found between variance in neural activity during the task proper and differences in performance.

Furthermore, a positive link is expected between neural variance during the task proper and resting-state variance. Indeed, when examining the correlation between pre-stimulus CV and performance, we found a negative correlation (Figure 4A), compatible with previous scalp EEG findings in a contrast discrimination task (Arazi et al., 2017). We also found a high level of positive correlation between pre-stimulus variance and resting-state fractional variance, further supporting the robustness of the proposed link.

What is the mechanism by which intrinsic fluctuations impact performance? Previous experimental and theoretical works have shown that shared variability among neurons with similar selectivity profiles during stimuli processing (namely, high noise correlations) can decrease the amount of encoded information in a neural population (Averbeck et al., 2006; Ruff and Cohen, 2014). In light of this work, a reduction in signal-to-noise ratio driven by intrinsically generated fluctuations could be introduced by a simple linear sum- mation of intrinsic fluctuations and evoked responses (but obviously more complex interactions are feasible as well).

Such increased noise may be manifested both by local increases in the fluctuations’ amplitude as well as increases in pairwise correlations. However, whether high spatial correlations alone may hamper performance is a question yet to be resolved.

It should be noted that compared to recent fMRI and EEG studies of individual differences, the current sample size (n = 26), while extensive in iEEG studies, is still relatively small. Relevant to this, the current intra-cranial recordings also revealed a significant negative correlation between raw iEEG fluctuations amplitude (0.3–2.7Hz) and performance.

A mild but significant correlation was also observed between the low-frequency (delta) modulations of the LFP and the HFB fluctuations. This result may open the possibility of exploring similar effects in non-invasive EEG and MEG recordings.

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STAR+METHODS

Detailed methods are provided in the online version of this paper and include the following:

d KEY RESOURCES TABLE

d LEAD CONTACT AND MATERIALS AVAILABILITY

d EXPERIMENTAL MODEL AND SUBJECT DETAILS B Human Patients

d METHOD DETAILS

B Experimental Procedures

B Electrodes Implant and Data Acquisition B Anatomical Localization of Electrodes

B Task-Related and Task-Unrelated Bipoles Definition

d QUANTIFICATION AND STATISTICAL ANALYSIS B Estimating Power of Resting-State HFB Fluctuations B Estimating Pre-stimulus Trial-to-Trial Variability B Estimating Response Variability to Repeated Stimuli B Assessing Statistical Significance of Correlation Coef-

ficients

d DATA AND CODE AVAILABILITY SUPPLEMENTAL INFORMATION

Supplemental Information can be found online athttps://doi.org/10.1016/j.

celrep.2019.11.081.

ACKNOWLEDGMENTS

This study has received funding from the CIFAR-Azrieli program of mind, brain, and consciousness (grant 7129380101 to R.M.) and the US-Israel Binational Foundation (grant 2017015 to R.M. and A.D.M.).

AUTHOR CONTRIBUTIONS

Conceptualization, S.G. and R.M.; Formal Analysis, S.G., R.H., and N.N.;

Investigation, S.G., E.M.Y., E.E., D.M.G, P.M., M.H., and A.D.M.; Writing – Original Draft, S.G. and R.M.; Writing – Review & Editing, all authors; Funding Acquisition, R.M. and A.D.M.; Supervision, R.M.

DECLARATIONS OF INTERESTS

The authors declare no competing interests.

Received: April 24, 2019 Revised: October 8, 2019 Accepted: November 20, 2019 Published: December 17, 2019 REFERENCES

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STAR + METHODS

KEY RESOURCES TABLE

LEAD CONTACT AND MATERIALS AVAILABILITY

Further information and requests for resources should be directed to and will be fulfilled by the Lead Contact, Rafi Malach (rafi.

[email protected]). No new materials have been generated by this study.

EXPERIMENTAL MODEL AND SUBJECT DETAILS Human Patients

26 patients monitored for pre-surgical evaluation of epileptic foci were included in the study (10 females, mean age 36±11; seeTable S2for individual demographic, clinical, and experimental details). Inclusion criteria were having at least one resting-state and one task session, and a minimum of 3 task-related bipoles. 1 patient for whom data were collected during both the visual 1-back task and a resting period was excluded due to misunderstanding of task instructions. All participants gave fully informed consent, including consent to publish, according to NIH guidelines, as monitored by the institutional review board at the Feinstein Institute for Medical Research, in accordance with the Declaration of Helsinki.

METHOD DETAILS Experimental Procedures

During the visual 1-back task, participants were seated in bed in front of an LCD monitor. Stimuli were embedded in a 600 mm gray square and included gray scale images from 6 categories: faces, houses, body parts, textures, objects, and words. Images were flashed at a fixed pace of 1 Hz, each image presented for 250 ms and followed by a fixed inter stimulus interval of 750 ms. Participants were instructed to maintain fixation during the entire task, while clicking the mouse button whenever a consecutive repetition of the exact same image occurred. The task included 205 trials, 25 of which were 1-back repeats (12%).

13 out of the 26 subjects participated in an additional 1-back visual task shortly after completing the first version. The additional version consisted of 600 mm squared colored images from 6 categories: faces, places, animals, objects, textures, and words. Im- ages were presented for 250 ms and were followed by a jittered inter stimulus interval ranging from 750 to 1050 ms, spaced by 50 ms steps. The task included 360 trials, 24 of which were 1-back repetitions (7%).

During resting-state periods, patients were instructed to rest with closed eyes. Resting-state recordings were collected at different time points relative to the task for each individual, ranging from10 minutes prior to the task to up to 13 days following the task. 9 out of the 26 patients participated in an additional rest session on a different day from that of the first rest session. Rest duration varied between subjects, ranging from1.5 to10 minutes. Individual temporal distances between rest periods and task as well as rest durations are presented inTable S2.

It is noteworthy that both task repeated participants (13 out of 26) and rest repeated participants (9 out of 26) were not significantly different from the corresponding non-repeated participants in their performance, number of task-related bipoles, resting-state fractional variance (< 1 Hz) in task-relate sites and resting-state overall amplitude in task-relate sites (seeFigure S5).

Electrodes Implant and Data Acquisition

Recordings were conducted at North Shore University Hospital, Manhasset, NY, USA. Electrodes were either subdural grids/strips placed directly on the cortical surface and/or depth electrodes (Ad-Tech Medical Instrument, Racine, Wisconsin, and PMT Corpo- ration, Chanhassen, Minnesota). Subdural contacts were 3 mm in diameter and 1 cm spaced, whereas depth contacts were 2 or 1 mm in diameter and 2.5 or 5 mm spaced, for Ad-Tech and PMT, respectively. Out of the 26 patients included in the study, 17 were implanted with both subdural grids/strips and depth electrodes and 9 were implanted only with depth electrodes (seeTable

REAGENT or RESOURCE SOURCE IDENTIFIER

Software and Algorithms

MATLAB 2018a MathWorks Inc. https://ch.mathworks.com/; RRID: SCR_001622

BioImage Suite Papademetris et al., 2006 http://bisweb.yale.edu/binaries/binaries.html; RRID: SCR_002986 FSL FLIRT Jenkinson and Smith, 2001 https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FLIRT; RRID: SCR_002823 FreeSurfer 5.3 Dale et al., 1999 https://surfer.nmr.mgh.harvard.edu/fswiki/recon-all

SUMA Argall et al., 2006 https://afni.nimh.nih.gov/pub/dist/doc/SUMA/suma/SUMA_do1.htm

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S1for individual specification). The signals were referenced to a vertex screw or a subdermal electrode, filtered electronically (analog bandpass filter with half-power boundaries at 0.07 and 40% of sampling rate Hz), sampled at a rate of either 512Hz or 500Hz and stored for offline analysis by XLTEK EMU128FS or NeuroLink IP 256 systems (Natus Medical Inc., San Carlos, CA). Electrical pulses were sent upon stimuli onsets and recorded along with the iEEG data for precise alignment of task protocol to neural activity.

Anatomical Localization of Electrodes

Prior to electrode implantation, patients were scanned with a T1-weighted 1mm isometric anatomical MRI on a 3-tesla Signa HDx scanner (GE Healthcare, Chicago, Illinois). Following the implant, a computed tomography (CT) and a T1-weighted anatomical MRI scan on a 1.5 Tesla Signa Excite scanner (GE Healthcare) were collected to enable electrode localization. The pre-implant MRI was aligned with the post-implant MRI, and the post-implant MRI was aligned with the post-implant CT using a rigid affine trans- formation as implemented by FSL’s Flirt (Jenkinson and Smith, 2001). Concatenation of the two alignment transformations allowed visualization of the post-implant CT scan on top of the pre- and post-implant MRI scan. Individual electrodes were then identified manually by inspection of the CT along with the post-implant MRI and were marked in each patient’s pre-implant MRI native space, using BioImage Suite (Papademetris et al., 2006). Electrode projection onto the cortical surface was performed as previously described (Golan et al., 2016): Individual patients’ cortical surface was segmented and reconstructed from the pre-implant MRI using FreeSurfer 5.3 (Dale et al., 1999), and each electrode was allocated to the nearest vertex on the cortical surface. In order to project electrodes from all patients onto a single template, the unfolded spherical mesh of each individual was resampled into a common unfolded spherical mesh using SUMA (Argall et al., 2006). Colored labels on the cortical surface as presented inFigure 1B were derived from surface-based atlases as implemented in FreeSurfer 5.3: functional atlas of retinotopic areas (Wang et al., 2014) (intermediate retinotopic areas), Destrieux anatomical atlas (Destrieux et al., 2010) (Fusiform gyrus) and Juelich histological atlas (V1, V2).

iEEG Signal Preprocessing and HFB Estimation

For the analysis, we focused on the high-frequency broad-band signals (HFB, 45-154Hz) which we and others have demonstrated to reflect aggregate firing rate in human (Manning et al., 2009; Mukamel et al., 2005; Nir et al., 2007) and non-human (Rasch et al., 2008;

Ray et al., 2008) primates (this signal is also termed at times high-frequency amplitude – HFA, or high-gamma; seeParvizi and Kast- ner, 2018for a recent review).

Signals that were initially recorded at a sampling rate of 512 Hz were down sampled to 500 Hz for consistency. Raw time-courses and power spectra of all channels were initially manually inspected for noticeable abnormal signals and other contaminations, and channels appearing as highly irregular were excluded from further analysis. In addition, channels identified as located over the seizure onset zone by an epileptologist were excluded. Next, channels were re-referenced using a bipolar montage, whereby each electrode was re-referenced by subtracting the raw signal of the most adjacent available electrode (< 20 mm). Single contacts were not as- signed more than once to a bipolar pair. Contacts with no available reference at a distance of up to 20 mm were discarded from all analysis. The bipolar re-referenced data were then used for all subsequent analyses.

To extract high-frequency amplitude (HFB) modulations, the signal was divided into 9 frequency sub-ranges of 9 Hz width, ranging from 45 to 154 Hz. The sub-ranges did not include 55-64 Hz and 115-124 Hz to discard line noise and its harmonies. Each frequency sub-range was then band passed and the momentary amplitude in each sub-range was estimated by the absolute value of the filtered signal’s Hilbert transform (Fisch et al., 2009; Lachaux et al., 2005; Noy et al., 2015). Since the 1/f profile of the signal’s power spectrum results in greater contribution of lower frequencies to the overall HFB estimation, we normalized each sub-range by dividing it with its mean value, and averaged the normalized values across all 9 sub-ranges. All data preprocessing and analyses were carried out using in house MATLAB codes. For filtering of high-frequency sub-ranges, we used original EEGLAB’s Hamming windowed FIR filter (po- p_eegfiltnew function) (Delorme and Makeig, 2004).

Task-Related and Task-Unrelated Bipoles Definition

The subset of task-related bipoles was composed of both visually- and hit- responsive cortical bipoles. Visually-responsive bipoles were defined based on their HFB response upon the onset of visual stimuli (50 – 400 ms) as compared to baseline (200 – 0 ms) during the visual 1-back task. Hit, miss and false alarm trials were excluded from this analysis. Bipoles that were both significantly responsive (paired t test, p < 0.01, Bonferroni correction applied per participant) and exhibited a considerable effect size of greater than 1 for at least one of the visual categories were defined as visually-responsive. Hit-responsive electrodes were defined based on their HFB response upon the onset of hit trials alone (100 – 700 ms) as compared to baseline (200 – 0 ms) during the task. We used a delayed and wider time window to detect hit-responsive bipoles since we reasoned that hit-related activations would arise later in time relative to visual responses and following prior studies which have shown a delayed and prolonged neural activation expanding beyond the sensory cortices upon visuo-motor responses to target stimuli (Gaillard et al., 2009; Noy et al., 2015). Here as well, bipoles that were both significantly responsive (Wilcoxon signed rank test, p < 0.01, Bonferroni correction applied per participant) and exhibited an effect size greater than 1 were defined as hit-responsive. The union of visually- and hit- responsive bipoles was defined as the group of task-related bipoles, consisting a total of 229 bipoles. The amount of task-related bipoles per individual ranged from 3 to 19 (mean 8.8±3.7 SD, seeTable S2andFigure S1for individual counts and mean response profiles).

Task-unrelated bipoles were defined as those minimally activated by the visual 1-back task. All bipoles that did not meet the inclusion criteria for task-related bipoles were initially considered. From this batch, bipoles exhibiting a maximal effect size (hit- or visual-related) smaller than 0.5 and located up to 10 mm from cortical gray matter were defined as task-unrelated. For subjects