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Gamma oscillations during episodic memory processing reveal reversal of information flow between the hippocampus and prefrontal cortex

07 Aug 2019-bioRxiv (W.B. Saunders Ltd)-pp 728469

TL;DR: It is suggested that in humans, it is the aVLPFC rather than medial prefrontal cortex that demonstrate these reciprocal interactions between the hippocampus and prefrontal cortex during encoding and retrieval of items and their contexts.
Abstract: A critical and emerging question in human episodic memory is how the hippocampus interacts with the prefrontal cortex during the encoding and retrieval of items and their contexts. In the present study, participants performed an episodic memory task (free recall) while intracranial electrodes were simultaneously inserted into the hippocampus and multiple prefrontal locations, allowing the quantification of relative onset times of gamma band activity in the cortex and the hippocampus in the same individual. We observed that in left anterior ventrolateral prefrontal cortex (aVLPFC) gamma band activity onset was significantly later than in the hippocampus during memory encoding, whereas its activity significantly preceded that in the hippocampus during memory retrieval. These findings provide direct evidence to support models of prefrontal-hippocampal interactions derived from studies of rodents, but suggest that in humans, it is the aVLPFC rather than medial prefrontal cortex that demonstrate these reciprocal interactions.
Topics: Prefrontal cortex (66%), Hippocampus (64%), Episodic memory (62%), Ventrolateral prefrontal cortex (59%), Cortex (anatomy) (57%)

Summary (5 min read)

Introduction

  • Prefrontal monitoring and control during episodic memory processing is thought to be critical for contextually mediated memory retrieval (Miller, 2013; Preston and Eichenbaum, 2013 ).
  • An influential model characterizing one of the mnemonic roles of the prefrontal cortex (PFC) -termed here the reciprocal flow hypothesis -posits that during memory encoding, contextual information flows from the hippocampus to the PFC while during retrieval, the PFC uses this stored information to guide selection of a contextually appropriate hippocampal memory representation (Desimone and Duncan, 1995; Desimone, 1998; Miller and Cohen, 2001; Preston and Eichenbaum, 2013) .
  • Evidence supporting this model has come from rodent investigations employing lagged correlation between the hippocampus and PFC in theta band oscillatory power (e.g. Place et al., 2016) .
  • CC-BY 4.0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
  • (Lin et al., 2017; Watrous and Ekstrom, 2014) .

Encoding Retrieval

  • Here the authors sought evidence of reversal of information flow between the hippocampus and prefrontal cortex during the encoding versus the retrieval of episodic memories.
  • The authors did this by taking advantage of a unique dataset obtained from 77 human patients implanted with stereo EEG electrodes for seizure mapping purposes who performed a verbal free recall paradigm.
  • During the study and recall phases of the task, the authors identified activation peaks in gamma oscillations from 40 to 120 Hz, using the onset of gamma activation as an estimate of the initial timing of activity in a given brain region.
  • As their data set included subjects with electrodes implanted in both the hippocampus and PFC (in addition to other cortical locations), the authors were able to directly compare the timing of.

Behavioral Performance

  • Across participants, the average probability of recall for all words was 24.4%.
  • The average percentage of list intrusions (recall errors) per subject was 12.8%.
  • The authors derived an estimate of temporal clustering (the tendency for items adjacent to each other in the study list to be recalled sequentially) to determine if temporal contextual factors were operating at retrieval (Watrous and Ekstrom, 2014) .
  • The mean clustering factor across all participants was 0.642, robustly higher than the chance value of 0.500 ( (36) = 8.294, < 0.001), indicating that participants incorporated temporal contextual information into encoded representations of the study words (Sederberg et al., 2010) .

sEEG Data

  • For their principal analysis, the authors identified the lag in onset of activation (Δ ) for five prefrontal locations relative to the hippocampus (positive Δ indicating activation following the hippocampus, negative Δ indicating activation preceding the hippocampus).
  • The left aVLPFC exhibited a mean activation lag relative to the hippocampus during successful encoding of +13.4 msec (FDR corrected < 0. (continued) compared to hippocampus), and a reversal of this effect during retrieval, such that the region led the hippocampus by -10.4 msec (FDR corrected = 0.0116).
  • Moreover, the Δ distributions for encoding and retrieval were significantly different (FDR corrected < 0.001) across electrodes when compared with a paired t-test.
  • Across all electrode pairs, 38% of aVLPFC electrodes exhibited this pattern of Δ reversal, which was significantly greater than the fraction exhibiting this effect in the DLPFC ( 2 (1, N=630) = 17.160, < 0.001) .
  • Further, the authors analyzed prior list intrusions (PLI) to test more directly whether Δ reversal is associated with the transmission of contextual information, as hypothesized by the reciprocal flow model.

L. aVLPFC

  • In a convergent approach, the authors looked for evidence of lagged activation using a different method, this time employing the lagged correlation of the gamma power envelope (rather than activation onset), following established methods (Ossandon et al., 2011) .
  • Which has a very different rationale and underlying set of assumptions than the lagged activation analysis described above, the results for the left aVLPFC were highly consistent with those observed previously, with a significant positive lag of 14 msec during encoding (uncorrected = 0.0442) (hippocampus leading) and negative 8 msec lag during retrieval (in the same direction as their initial analysis with the PFC 5 of 12 .
  • The copyright holder for this preprint (which was not this version posted August 7, 2019.
  • ; https://doi.org/10.1101/728469 doi: bioRxiv preprint Manuscript submitted to eLife leading, though it did not reach significance -uncorrected = 0.1426).
  • As with the previous method, the encoding versus retrieval lag distributions were significantly different (FDR corrected = 0.0276).

Discussion

  • The authors data reveal direct human electrophysiological evidence of the reversal of information flow between the hippocampus and prefrontal cortex (left aVLPFC) during episodic memory encoding versus retrieval.
  • These results are analogous to observations in rodents held to be consistent with Using data drawn from a series of verbal memory tasks, Badre et al. proposed that the aVLPFC supports cue specification (Badre et al., 2005) , and Simons and Spiers integrated similar previous findings into a model that distinguished between ventral and dorsal PFC contributions to verbal memory encoding and retrieval (Simons and Spiers, 2003) , with ventral regions providing cue specification consistent with onset of activation preceding the hippocampus during retrieval, as the authors observed.
  • The authors acknowledge however that their data by itself does not allow us to make strong claims regarding the content of information characterized by reciprocal flow.
  • CC-BY 4.0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
  • The authors principal results identifying Δ reversal (consistent with rodent findings) were obtained using an unbiased approach incorporating all electrodes in their dataset, but the authors acknowledge that the magnitude of the Δ offset depends upon whether one includes all electrodes in the calculation or only those that exhibit the reversal (a minority of electrodes in the aVLPFC exhibited the opposite pattern to the overall effect, and therefore, opposite to that predicted by the reciprocal information flow hypothesis).

Place et al., 2016).

  • Taken together, their data support the relevance of the reciprocal flow hypothesis to human memory and establish lagged gamma activation as a method to identify functional interactions between memory-relevant regions in humans.
  • The identification of electrode contacts in the aVLPFC that exhibit these functional properties may be a strategy for the identification of propitious targets of neuromodulation to treat memory disorders.

Methods and Materials

  • Participants 77 patients with medically intractable epilepsy who underwent stereoelectroencephalography surgery for clinical purposes were recruited to participate in this study.
  • In total, there were 46 men and 31 women between 21-64 years of age.
  • Frontal contacts were divided into 5 regions: the anterior ventro-lateral prefrontal cortex (principally BA45), the posterior ventro-lateral prefrontal cortex (BA8, BA44), the dorsolateral prefrontal cortex (BA9, BA46), the medial orbitofrontal cortex (BA10, BA11, BA12), and the anterior cingulate cortex (ACC) (BA24).
  • The anatomical features described above were used in expert neuroradiology review to localize all electrodes and in situations of conflict between the reviewed anatomical location and Talairach-based assignment to Brodmann areas the former was used for 7 of 12 .
  • CC-BY 4.0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

Experimental Paradigm

  • Participants preformed a free recall task consisting of multiple study/test cycles.
  • Participants were then instructed to verbally recall as many items as possible from the immediately prior list in no particular order.
  • A full session consists of 12 full study/test cycles and 1 practice study/test cycle which was excluded from analysis.
  • One complete session yielded electrophysiological recordings from 144 word encoding epochs (12 lists x 12 words) and a variable number of retrieval epochs.
  • The authors used the temporal clustering factor, which is a measure of temporal contiguity for each recall transition relative to all possible recall transitions at a given time, to determine if contextual factors were operating at retrieval (Sederberg et al., 2008) .

Data Processing

  • Stereo-EEG data were recorded using a Nihon Kohden EEG-1200 clinical system.
  • Raw signals were subsequently re-referenced to a bipolar montage, with each contact referenced to the superficial adjacent contact.
  • All analyses were conducted using MATLAB with both built-in and custom-made scripts.
  • The raw signals were filtered for noise on a session by session basis using the following steps: 1) the power spectral density was estimated across the entire session, 2) a 7th order polynomial was fit to the power spectral density estimate to obtain a trend line, 3) the trend line was subtracted from the power spectral density estimate to identify peaks in the periodogram, and 4) for each peak above 15 dB, the local minima surrounding the peak were used to define the cutoff frequencies for a first-order Butterworth notch filter.
  • Retrieval trials were isolated such that each included trial was isolated from any other retrieval events by at least 1200 msec before the onset of vocalization and 200 msec after the onset of vocalization (this led to the exclusion of 3,258/12,791 [25.5% trials]).

Activation Onset Detection (Calculation of )

  • The authors compared the temporal patterns of high gamma band power changes in the hippocampus and frontal cortex in the 1000 msec immediately following study item presentation and the 1000 msec immediately preceding word vocalization .
  • CC-BY 4.0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
  • To obtain an estimate of gamma power, a threshold was defined as the mean spectral power within each frequency band across all trials for a given session and for each condition.
  • Prior to obtaining the threshold for encoding trials, the trials were further divided into subsequently recalled and non-recalled items to account for possible power differences due to memory success.
  • This was repeated for each frequency band and trial, and the was averaged across all bands and then across all trials for the recalled epochs, non-recalled epochs, and retrieval epochs to obtain a single time estimate of activation onset, , for each hippocampal and PFC electrode and each condition .

Δ = −

  • Where is the estimate of activation for the PFC and hippocampus, respectively.
  • Given this, a positive Δ indicates that activation in the hippocampus is leading activation in the PFC, whereas negative Δ indicates that activation in the hippocampus is lagging activation in the PFC.
  • For each hippocampal-PFC electrode pair, the Δ was calculated separately for the encoding (recalled and non-recalled separately) and retrieval trials to yield a single Δ per electrode pair for each condition.

Cross-Correlation

  • In a convergent analysis, the authors used cross-correlation of the gamma band amplitude envelope to investigate the temporal dynamics between the hippocampus and the PFC.
  • CC-BY 4.0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
  • For each hippocampal-PFC electrode pair, the normalized cross-correlation was calculated on the meancentered amplitude envelope on a trial-by-trial basis using an 800 msec moving window with a 1 msec step size and a maximum lag of 150 msec.
  • The initial 800 msec cross-correlation moving window for the encoding epochs was centered 100 msec prior to word presentation and stepped by 1 msec until 1700 msec after word presentation.
  • Lastly, the correlation lag time with the highest z-score (i.e. the correlation lag where the correlation coefficient was maximally greater than zero across trials) was determined for each correlation moving window to produce a 1 by 1800 matrix for all recalled and non-recalled study trials and a 1 by 1200 matrix for all retrieval trials for each electrode pair.

Statistical Procedure

  • To test for significant Δ across the electrodes within each hippocampal-PFC region pair during the encoding (subsequently recalled only) and retrieval conditions, the authors combined the Δ for all electrode pairs into a single matrix and used a t-test to compare the distribution of Δ against a null hypothesis of zero lag in onset activation (Δ = 0).
  • In order to account for the type I error rate, p-values were false discovery rate (FDR) corrected.
  • A one-sample t-test was used to compare the distribution of lag estimates across electrode pairs against a null hypothesis of zero lag (i.e. the correlation coefficient is maximized at zero lag) for each condition.
  • The left aVLPFC shows a Δ reversal between conditions that is consistent with the result using the original threshold.
  • Z-scores were calculated for each region using a paired t-test between gamma power for recalled words and non-recalled words.

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Manuscript submitted to eLife
Gamma oscillations during episodic1
memory processing reveal reversal2
of information ow between the3
hippocampus and prefrontal cortex4
Sarah Seger
1
, Michael D. Rugg
2,3,4
, Bradley C. Lega
1*
5
*For correspondence:
bradlega@gmail.com
Present address:
**
B. Lega,
UT-Southwestern, Neurological
Surgery MS 8855, 5323 Harry Hines
Blvd, Dallas, TX 75390
1
Department of Neurological Surgery, University of Texas-Southwestern Medical Center,6
Dallas, Texas 75390;
2
Center for Vital Longevity, University of Texas at Dallas, Dallas, Texas
7
75235;
3
School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas,8
Texas 75080;
4
Department of Psychiatry, University of Texas Southwestern Medical9
Center, Dallas, Texas 7539010
11
Abstract A critical and emerging question in human episodic memory is how the hippocampus12
interacts with the prefrontal cortex during the encoding and retrieval of items and their contexts. In
13
the present study, participants performed an episodic memory task (free recall) while intracranial14
electrodes were simultaneously inserted into the hippocampus and multiple prefrontal locations,15
allowing the quantication of relative onset times of gamma band activity in the cortex and the16
hippocampus in the same individual. We observed that in left anterior ventrolateral prefrontal17
cortex (aVLPFC) gamma band activity onset was signicantly later than in the hippocampus during
18
memory encoding, whereas its activity signicantly preceded that in the hippocampus during19
memory retrieval. These ndings provide direct evidence to support models of20
prefrontal-hippocampal interactions derived from studies of rodents, but suggest that in humans, it
21
is the aVLPFC rather than medial prefrontal cortex that demonstrate these reciprocal interactions.22
23
Introduction24
Prefrontal monitoring and control during episodic memory processing is thought to be critical for
25
contextually mediated memory retrieval (
Miller, 2013
;
Preston and Eichenbaum, 2013
). An inuen-
26
tial model characterizing one of the mnemonic roles of the prefrontal cortex (PFC) - termed here
27
the reciprocal ow hypothesis - posits that during memory encoding, contextual information ows
28
from the hippocampus to the PFC while during retrieval, the PFC uses this stored information to
29
guide selection of a contextually appropriate hippocampal memory representation (
Desimone and30
Duncan, 1995
;
Desimone, 1998
;
Miller and Cohen, 2001
;
Preston and Eichenbaum, 2013
). Stated
31
another way, the model posits that information ow between the hippocampus and PFC reverses
32
direction between encoding and retrieval. Evidence supporting this model has come from rodent
33
investigations employing lagged correlation between the hippocampus and PFC in theta band
34
oscillatory power (e.g.
Place et al., 2016
). In humans, noninvasive data have stimulated the hy-
35
pothesis that the VLPFC is necessary for generating retrieval cues during episodic memory search
36
(
Kim, 2019
), consistent with rodent ndings, and lesion studies suggest that patients with frontal
37
lobe dysfunction have diculty recalling items when the context is altered between encoding
38
and subsequent retrieval (
Chao, 1997
;
Fletcher, 2001
). However, to date there is no direct human
39
1 of 12
.CC-BY 4.0 International licensea
certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under
The copyright holder for this preprint (which was notthis version posted August 7, 2019. ; https://doi.org/10.1101/728469doi: bioRxiv preprint

Manuscript submitted to eLife
electrophysiological evidence of reversed lags in the timing of hippocampal and PFC activation that
40
would be indicative of dierential information ow during encoding and retrieval. fMRI studies lack
41
sucient temporal resolution to identify such an eect, precise source localization of MEG signals
42
to dierent mesial temporal structures is problematic, and the absence of direct homology between
43
rodent and human prefrontal cortex means that human intracranial EEG studies are necessary to
44
establish whether this phenomenon is characteristic of human episodic memory and to determine
45
in which brain regions it may occur.46
Encoding Retrieval
CAT
SAND
SAND
TREE
TREE
3
+
7
+
1 = ?
1
+
4
+
5 = ?
6
+
2
+
5 = ?
. . .
.
. .
.
. .
+
1 2
12
Distractor
A
B
HIPP
ACC
MedOrbFront
aVLPFC
pVLPFC
DLPFC
ACC
MedOrbFront
HIPP
DLPFC
pVLPFC
aVLPFC
L
R
Subjects
15
40
2500 500 750
-500-750 -250 0
L. HIPP
L. aVLPFC
L. DLPFC
time (ms) after
word presentation
time (ms) before
vocalization
Encoding Retrieval
t
γ
t
γ
t
γ
t
γ
t
γ
t
γ
Peak Detection Algorithm
100 ms
normalized power
time (ms)
slope of
power
0
maximum
slope =
t
γ
power rises
above threshold
100 ms
100 200 400300
0
1
gamma power
threshold
time of activation (
t
γ
)
normalized power
normalized power
C
D
Figure 1.
(A) Schematic of the experimental paradigm used in this study. Black boxes indicate the encoding and
retrieval epochs. (B) Number of subjects included in each prefrontal and hippocampal region. The colorbar
indicates number of subjects, a minimum of 15 subjects and a maximum of 40 subjects contributed to any
given unilateral region. (C) Example trace from a single electrode contact depicts the activation onset detection
algorithm. The time point of activation (
𝑡
𝛾
) is marked as the time point where the slope is maximized in the 200
msec window centered at the time point where power passes threshold. The 𝑡
𝛾
demonstrates that onset of
activation does not necessarily coincide with the time that power passes the threshold. (D) Example encoding
and retrieval trial show 𝑡
𝛾
for three electrodes.
Figure 1Figure supplement 1.
Figure 1Figure supplement 2.
A complicating factor when testing the reciprocal ow hypothesis in humans is that there
47
appear to be multiple oscillations within traditional theta frequency bands, and the dominant theta
48
frequency in the hippocampus may dier that in the neocortex (
Lega et al., 2012
;
Miller, 2013
;
49
Watrous and Ekstrom, 2014
). Furthermore, unlike rodents, human hippocampal recordings do
50
not universally exhibit theta modulation as a function of memory processing (although this might
51
be more prevalent in posterior hippocampal locations) (
Lin et al., 2017
;
Watrous and Ekstrom,52
2014
). By contrast, gamma oscillations exhibit widespread and reproducible power increases in
53
multiple neural regions during episodic memory encoding and retrieval, including in the PFC and
54
hippocampus (Burke et al., 2014; Sederberg et al., 2007).55
Here we sought evidence of reversal of information ow between the hippocampus and pre-
56
frontal cortex during the encoding versus the retrieval of episodic memories. We did this by taking
57
advantage of a unique dataset obtained from 77 human patients implanted with stereo EEG elec-
58
trodes for seizure mapping purposes who performed a verbal free recall paradigm. During the
59
study and recall phases of the task, we identied activation peaks in gamma oscillations from 40 to
60
120 Hz, using the onset of gamma activation as an estimate of the initial timing of activity in a given
61
brain region. As our data set included subjects with electrodes implanted in both the hippocampus
62
and PFC (in addition to other cortical locations), we were able to directly compare the timing of
63
2 of 12
.CC-BY 4.0 International licensea
certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under
The copyright holder for this preprint (which was notthis version posted August 7, 2019. ; https://doi.org/10.1101/728469doi: bioRxiv preprint

Manuscript submitted to eLife
memory-related gamma activation in the PFC and hippocampus within-subjects.64
Results65
Behavioral Performance66
Across participants, the average probability of recall for all words was 24.4%. The average percent-
67
age of list intrusions (recall errors) per subject was 12.8%. We derived an estimate of temporal
68
clustering (the tendency for items adjacent to each other in the study list to be recalled sequentially)
69
to determine if temporal contextual factors were operating at retrieval (
Watrous and Ekstrom,70
2014
). The mean clustering factor across all participants was 0.642, robustly higher than the chance
71
value of 0.500 (
𝑡(36) = 8.294
,
𝑝 < 0.001
), indicating that participants incorporated temporal contextual
72
information into encoded representations of the study words (Sederberg et al., 2010).73
sEEG Data74
For our principal analysis, we identied the lag in onset of activation (
Δ𝑡
𝛾
) for ve prefrontal locations
75
relative to the hippocampus (positive
Δ𝑡
𝛾
indicating activation following the hippocampus, negative
76
Δ𝑡
𝛾
indicating activation preceding the hippocampus). We divided the PFC into ve distinct bilateral
77
regions: dorsal PFC (dorsal to the inferior frontal sulcus, anterior to pre-motor cortex, ventral to
78
the superior frontal gyrus), the posterior VLPFC (posterior to the anterior ascending ramus of the
79
sylvian ssure, anterior to motor cortex), the anterior VLPFC (anterior to that ascending ramus), the
80
medial orbitofrontal cortex and the anterior cingulate cortex. These regions were selected based
81
upon targeting strategies employed for seizure mapping, providing sucient numbers of electrodes
82
for analysis. Exact timing of activation onset (
𝑡
𝛾
) was estimated on a trial by trial basis for recording
83
sites by calculating a gamma power threshold in the 40-120 Hz range to determine the timing of
84
onset relative to hippocampal contacts in the same individual following established methods (Figure
85
1).86
HIPP
*
Encoding Retrieval
Encoding Retrieval
*
*
Time Lag in Onset of Activation
ACC
MedOrbFront
HIPP
ACC
MedOrbFront
DLPFC
aVLPFC
pVLPFC
aVLPFC
pVLPFC
DLPFC
ACC
MedOrbFront
ACC
MedOrbFront
HIPP
HIPP
DLPFC
pVLPFC
aVLPFC
DLPFC
pVLPFC
aVLPFC
R
L
L
R
Δt
γ
+ 25 ms- 25 ms 0 ms
Cortex Leads Hippocampus Leads
*
*
Figure 2. Mean Δ𝑡
𝛾
across electrodes for all prefrontal cortex regions for the encoding (recalled words only) and retrieval condition. Red colors
indicate that activation in the hippocampus precedes activation in the cortex and blue indicates that the cortical activation precedes hippocampal.
For each memory condition, a black region border indicates that Δ𝑡
𝛾
across all electrode pairs is signicantly dierent than zero (t-test, FDR
corrected 𝑝 < 0.011 for encoding and 𝑝 < 0.007 for retrieval). An asterisk (*) between the encoding and retrieval conditions indicates that the
encoding and retrieval Δ𝑡
𝛾
are signicantly dierent when compared with a paired t-test (FDR corrected 𝑝 < 0.019). The left aVLPFC exhibited a
mean activation lag relative to the hippocampus during successful encoding of +13.4 msec (FDR corrected 𝑝 < 0.001, t-test of activation times
Figure 2 continued on next page.
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Figure 2. (continued)
compared to hippocampus), and a reversal of this eect during retrieval, such that the region led the hippocampus by -10.4 msec (FDR corrected
𝑝 = 0.0116
). Moreover, the
Δ𝑡
𝛾
distributions for encoding and retrieval were signicantly dierent (FDR corrected
𝑝 < 0.001
) across electrodes when
compared with a paired t-test. Of the remaining PFC regions, only one other region, the right DLPFC, exhibited a
Δ𝑡
𝛾
that was signicantly greater
than zero during encoding (FDR corrected 𝑝 = 0.0037); however this region did not exhibit a reversal in Δ𝑡
𝛾
values during retrieval (with the
hippocampus leading during both encoding and retrieval). The left ACC exhibited a signicant dierence in the distribution of
Δ𝑡
𝛾
during encoding
vs. retrieval (-6.86 msec during encoding, 6.46 msec during retrieval; FDR corrected
𝑝 = 0.0173
); but the
Δ𝑡
𝛾
was not signicantly dierent than zero
during neither encoding (FDR corrected 𝑝 = 0.0700) or retrieval (FDR corrected 𝑝 = 0.0700) indicating onset nearly commensurate with that of the
hippocampus. The pattern of Δ𝑡
𝛾
reversal was evident for the left but not the right aVLPFC. In the latter region, while there was a signicant
dierence between encoding and retrieval (FDR corrected
𝑝 = 0.0080
), the values indicated that the cortex led the hippocampus during both phases
of the free recall task (lag = -23.5 msec during retrieval, -7.44 msec during encoding).
Figure 2Figure supplement 1.
Convincing evidence of a reversal in the ow of information consistent with the reciprocal ow87
model would require a PFC region to exhibit 1) a lag in activation onset relative to the hippocampus
88
that is signicantly greater than zero during successful item encoding (signicant positive
Δ𝑡
𝛾
, hip-
89
pocampus leading), 2) a lag in activation that is signicantly less than zero during retrieval (negative
90
Δ𝑡
𝛾
, hippocampus trailing), and nally 3) a signicant dierence in these
Δ𝑡
𝛾
values when directly
91
compared in a paired test (reversed Δ𝑡
𝛾
). Across all electrodes in our dataset (without any ltering92
of electrodes based upon their functional properties), we observed that the left aVLPFC exhibited
93
the following set of properties: a mean activation lag relative to the hippocampus during successful
94
encoding of 13.4 msec (FDR corrected
𝑝 < 0.001
, t-test of activation times compared to hippocam-
95
pus), and a reversal of this eect during retrieval, such that the region led the hippocampus by -10.4
96
msec (FDR corrected
𝑝 = 0.0116
). Moreover, the
Δ𝑡
𝛾
distributions for encoding and retrieval were
97
signicantly dierent (FDR corrected 𝑝 < 0.001) (Figure 1).98
Time Lag SME
HIPP
ACC
MedOrbFront
aVLPFC
pVLPFC
DLPFC
ACC
MedOrbFront
HIPP
DLPFC
pVLPFC
aVLPFC
L
L
R
R
z-score
> 2.5
0
Δt
γ
vs.
recalled
non
recalled
Δt
γ
Figure 3. Subsequent memory eect in Δ𝑡
𝛾
for all regions. Z-scores were calculated for each region using a
paired t-test between Δ𝑡
𝛾
for recalled words and Δ𝑡
𝛾
for non-recalled words. The subsequent memory eect
for left aVLPFC, left DLPFC, and left ACC was signicant (FDR corrected p < 0.007), which is indicated by black
borders for those regions. No regions in the right hemisphere show a signicant subsequent memory eect.
Figure 3Figure supplement 1.
Next, we compared the
Δ𝑡
𝛾
distributions during successful versus unsuccessful encoding, looking
99
for evidence of a subsequent memory eect in this measurement. For the left aVLPFC, this contrast
100
was signicant (FDR corrected
𝑝 = 0.0216
), suggesting that
Δ𝑡
𝛾
measurements are sensitive to encod-
101
ing success (Figure 3). Taken together, these ndings indicate that the timing of gamma activation102
onset in the left aVLPFC constitute a signal that is sensitive to memory encoding success (exhibiting
103
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an SME), with a pattern that ts a putative model of the transfer of contextual information to the
104
frontal cortex during successful encoding (signicantly positive relative to hippocampal activation)
105
with evidence of a reversal during retrieval (signicantly negative relative to the hippocampus).
106
Across all electrode pairs, 38% of aVLPFC electrodes exhibited this pattern of
Δ𝑡
𝛾
reversal, which
107
was signicantly greater than the fraction exhibiting this eect in the DLPFC (
𝜒
2
(1, N=630) = 17.160,
108
𝑝 < 0.001
) (Figure 4). Across the subjects who contributed an electrode pair to the left aVLPFC, 59%
109
showed a pattern of Δ𝑡
𝛾
reversal in at least one electrode pair.110
L. aVLPFC
Recalled Retrieval
*
Δt
γ
reversal (direction of mean)
No
Δt
γ
reversal
Δt
γ
reversal (opposite of mean)
38%
52%
10%
# of Electrodes
t
_
γ
(ms)
Retrieval
15
-600 -500
L. HIPP
L. aVLPFC
10
5
Recalled
t
_
γ
(ms)
10
5
250 350
L. HIPP
L. aVLPFC
C
B
# of Electrodes
A
-50 50 100-100 0
Δt
γ
(
m
s
)
Recalled
Non
recalled
Figure 4. The left aVLPFC shows a reversal in the Δ𝑡
𝛾
between encoding and retrieval consistent with the
reciprocal ow information. (A) Distribution of Δ𝑡
𝛾
for all aVLPFC electrodes during the recalled and
non-recalled conditions. The Δ𝑡
𝛾
for non-recalled words is not signicantly dierent than zero (mean
non-recalled lag is +2.91 msec). (B) 38% of aVLPFC electrodes have Δ𝑡
𝛾
reversal between conditions, with the
hippocampus leading in activation during encoding and the cortex leading during retrieval, and 10% show the
opposite pattern of activation, with the cortex leading in activation during encoding and the hippocampus
leading during retrieval. 52% of electrodes show no reversal in lag between conditions. (C) Histograms of the
mean
𝑡
𝛾
during encoding (subsequently recalled only) and retrieval for the 38% of aVLPFC - HIPP electrode pairs
exhibiting the eect depicts the dierences in timing of activation onset for hippocampal and aVLPFC
electrodes between memory conditions.
Further, we analyzed prior list intrusions (PLI) to test more directly whether
Δ𝑡
𝛾
reversal is
111
associated with the transmission of contextual information, as hypothesized by the reciprocal
112
ow model. List intrusions represent errors of itemcontext association (the wrong item for a
113
given context, i.e. the list on which the item was presented, although we discuss caveats to the
114
interpretation of PLI data in the Discussion below). For this analysis, oscillatory activity can be
115
analyzed only during item retrieval. We observed no evidence of information reversal for PLI events,
116
with the onset of left aVLPFC activation not signicantly dierent than for the hippocampus (mean
117
Δ𝑡
𝛾
= -3.5 msec, uncorrected
𝑝 = 0.3861
). In addition, the
Δ𝑡
𝛾
during correct retrieval events was
118
signicantly less than that of PLI events (uncorrected 𝑝 = 0.0436).119
In a convergent approach, we looked for evidence of lagged activation using a dierent method,
120
this time employing the lagged correlation of the gamma power envelope (rather than activation
121
onset), following established methods (
Ossandon et al., 2011
). With this approach, which has a very
122
dierent rationale and underlying set of assumptions than the lagged activation analysis described
123
above, the results for the left aVLPFC were highly consistent with those observed previously, with a
124
signicant positive lag of 14 msec during encoding (uncorrected
𝑝 = 0.0442
) (hippocampus leading)
125
and negative 8 msec lag during retrieval (in the same direction as our initial analysis with the PFC
126
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Journal Article
Abstract: The authors present a new model of free recall on the basis of M. W. Howard and M. J. Kahana's (2002a) temporal context model and M. Usher and J. L. McClelland's (2001) leaky-accumulator decision model. In this model, contextual drift gives rise to both short-term and long-term recency effects, and contextual retrieval gives rise to short-term and long-term contiguity effects. Recall decisions are controlled by a race between competitive leaky accumulators. The model captures the dynamics of immediate, delayed, and continual distractor free recall, demonstrating that dissociations between short- and long-term recency can naturally arise from a model in which an internal contextual state is used as the sole cue for retrieval across time scales.

252 citations


References
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Book
01 Jan 1975-
Abstract: Contents: Preface. Introduction. Bivariate Correlation and Regression. Multiple Regression/Correlation With Two or More Independent Variables. Data Visualization, Exploration, and Assumption Checking: Diagnosing and Solving Regression Problems I. Data-Analytic Strategies Using Multiple Regression/Correlation. Quantitative Scales, Curvilinear Relationships, and Transformations. Interactions Among Continuous Variables. Categorical or Nominal Independent Variables. Interactions With Categorical Variables. Outliers and Multicollinearity: Diagnosing and Solving Regression Problems II. Missing Data. Multiple Regression/Correlation and Causal Models. Alternative Regression Models: Logistic, Poisson Regression, and the Generalized Linear Model. Random Coefficient Regression and Multilevel Models. Longitudinal Regression Methods. Multiple Dependent Variables: Set Correlation. Appendices: The Mathematical Basis for Multiple Regression/Correlation and Identification of the Inverse Matrix Elements. Determination of the Inverse Matrix and Applications Thereof.

29,559 citations


Journal ArticleDOI
Earl K. Miller1, Jonathan D. Cohen2Institutions (2)
TL;DR: It is proposed that cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them, which provide bias signals to other brain structures whose net effect is to guide the flow of activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task.
Abstract: ▪ Abstract The prefrontal cortex has long been suspected to play an important role in cognitive control, in the ability to orchestrate thought and action in accordance with internal goals. Its neural basis, however, has remained a mystery. Here, we propose that cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them. They provide bias signals to other brain structures whose net effect is to guide the flow of activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task. We review neurophysiological, neurobiological, neuroimaging, and computational studies that support this theory and discuss its implications as well as further issues to be addressed

10,033 citations


"Gamma oscillations during episodic ..." refers background or methods in this paper

  • ...…information flows from the hippocampus to the PFC while during retrieval, the PFC uses this stored information to guide selection of a contextually appropriate hippocampal memory representation (Desimone and Duncan, 1995; Desimone, 1998; Miller and Cohen, 2001; Preston and Eichenbaum, 2013)....

    [...]

  • ...Nonetheless, previous rodent and human studies have provided evidence for the relevance of prefrontal regions in supporting context representations (Desimone and Duncan, 1995; Desimone, 1998; Miller and Cohen, 2001)....

    [...]


Journal ArticleDOI
Robert Desimone1, John S. DuncanInstitutions (1)
TL;DR: The two basic phenomena that define the problem of visual attention can be illustrated in a simple example and selectivity-the ability to filter out un­ wanted information is illustrated.
Abstract: The two basic phenomena that define the problem of visual attention can be illustrated in a simple example. Consider the arrays shown in each panel of Figure 1. In a typical experiment, before the arrays were presented, subjects would be asked to report letters appearing in one color (targets, here black letters), and to disregard letters in the other color (nontargets, here white letters). The array would then be briefly flashed, and the subjects, without any opportunity for eye movements, would give their report. The display mimics our. usual cluttered visual environment: It contains one or more objects that are relevant to current behavior, along with others that are irrelevant. The first basic phenomenon is limited capacity for processing information. At any given time, only a small amount of the information available on the retina can be processed and used in the control of behavior. Subjectively, giving attention to any one target leaves less available for others. In Figure 1, the probability of reporting the target letter N is much lower with two accompa­ nying targets (Figure la) than with none (Figure Ib). The second basic phenomenon is selectivity-the ability to filter out un­ wanted information. Subjectively, one is aware of attended stimuli and largely unaware of unattended ones. Correspondingly, accuracy in identifying an attended stimulus may be independent of the number of nontargets in a display (Figure la vs Ie) (see Bundesen 1990, Duncan 1980).

7,133 citations


"Gamma oscillations during episodic ..." refers background or methods in this paper

  • ...…information flows from the hippocampus to the PFC while during retrieval, the PFC uses this stored information to guide selection of a contextually appropriate hippocampal memory representation (Desimone and Duncan, 1995; Desimone, 1998; Miller and Cohen, 2001; Preston and Eichenbaum, 2013)....

    [...]

  • ...Nonetheless, previous rodent and human studies have provided evidence for the relevance of prefrontal regions in supporting context representations (Desimone and Duncan, 1995; Desimone, 1998; Miller and Cohen, 2001)....

    [...]


Journal ArticleDOI
Paul C. Fletcher1, Richard N. HensonInstitutions (1)
01 May 2001-Brain
TL;DR: It is predicted that the resolution of questions concerning the functional neuroanatomical subdivisions of the frontal cortex will ultimately depend on a fuller cognitive psychological fractionation of memory control processes, an enterprise that will be guided and tested by experimentation.
Abstract: The new functional neuroimaging techniques, PET and functional MRI (fMRI), offer sufficient experimental flexibility and spatial resolution to explore the functional neuroanatomical bases of different memory stages and processes. They have had a particular impact on our understanding of the role of the frontal cortex in memory processing. We review the insights that have been gained, and attempt a synthesis of the findings from functional imaging studies of working memory, encoding in episodic memory and retrieval from episodic memory. Though these different aspects of memory have usually been studied in isolation, we suggest that there is sufficient convergence with respect to frontal activations to make such a synthesis worthwhile. We concentrate in particular on three regions of the lateral frontal cortex-ventro-lateral, dorsolateral and anterior-that are consistently activated in these studies, and attribute these activations to the updating/maintenance of information, the selection/manipulation/monitoring of that information, and the selection of processes/subgoals, respectively. We also acknowledge a number of empirical inconsistencies associated with this synthesis, and suggest possible reasons for these. More generally, we predict that the resolution of questions concerning the functional neuroanatomical subdivisions of the frontal cortex will ultimately depend on a fuller cognitive psychological fractionation of memory control processes, an enterprise that will be guided and tested by experimentation. We expect that the neuroimaging techniques will provide an important part of this enterprise.

1,308 citations


"Gamma oscillations during episodic ..." refers result in this paper

  • ...…retrieval cues during episodic memory search (Kim, 2019), consistent with rodent findings, and lesion studies suggest that patients with frontal lobe dysfunction have difficulty recalling items when the context is altered between encoding and subsequent retrieval (Chao, 1997; Fletcher, 2001)....

    [...]


Journal ArticleDOI
S. T. Carmichael1, Joseph L. Price1Institutions (1)
TL;DR: This study has shown that the orbital and medial prefrontal cortex (OMPFC) is extensively connected with medial temporal and cingulate limbic structures, and the organization of these projections was defined in relation to architectonic areas within the OMPFC.
Abstract: Previous studies have shown that the orbital and medial prefrontal cortex (OMPFC) is extensively connected with medial temporal and cingulate limbic structures. In this study, the organization of these projections was defined in relation to architectonic areas within the OMPFC. All of the limbic structures were substantially connected with the following posterior and medial orbital areas: the posteromedial, medial, intermediate, and lateral agranular insular areas (Iapm, Iam, Iai, and Ial, respectively) and areas 11m, 13a, 13b, 14c and 14r. In contrast, lateral orbital areas 12o, 12m, and 12l and medial wall areas 24a,b and 32 were primarily connected with the amygdala, the temporal pole, and the cingulate cortex. Data were not obtained on the posteroventral medial wall. Three distinct projections were recognized from the basal amygdaloid nucleus: 1) The dorsal part projected to area 12l; 2) the ventromedial part projected to most areas in the posterior and medial orbital cortex except for area Iai, 12o, 13a, and 14c; and 3) the ventrolateral part projected to orbital areas 12o, Iai, 13a, 14c, and to the medial wall areas. The accessory basal and lateral amygdaloid nuclei projected most strongly to areas in the posterior and medial orbital cortex. The medial, anterior cortical, and central amygdaloid nuclei and the periamygdaloid cortex were connected with the posterior orbital areas. The projection from the hippocampus originated from the rostral subiculum and terminated in the medial orbital areas. The same region was reciprocally connected with the anteromedial nucleus of the thalamus, which received input from the rostral subiculum. The parahippocampal cortical areas (including the temporal polar, entorhinal, perirhinal, and posterior parahippocampal cortices) were primarily connected with posterior and medial orbital areas, with some projections to the dorsal part of the medial wall. The rostral cingulate cortex sent fibers to the medial wall, to the medial orbital areas, and to lateral areas 12o, 12r, and Iai. The posterior cingulate gyrus, including the caudomedial lobule, was especially strongly connected with area 11m.

1,147 citations


"Gamma oscillations during episodic ..." refers background in this paper

  • ...This focus on the mPFC is motivated by direct anatomical connectivity between the anterior cingulate cortex and the hippocampus in humans (along with indirect connections between medial Brodmann area 10 and the hippocampus) (Carmichael and Price, 1995)....

    [...]