An Amygdala-Hippocampus Subnetwork that Encodes Variation in Human Mood

Lowry A. Kirkby; Francisco J. Luongo; Morgan B. Lee; Mor Nahum; Thomas M. Van Vleet; Vikram R. Rao; Heather E. Dawes; Edward F. Chang; Vikaas S. Sohal

doi:10.1016/j.cell.2018.10.005

An Amygdala-Hippocampus Subnetwork that Encodes Variation in Human Mood

Lowry A. Kirkby, Francisco J. Luongo, Morgan B. Lee, Mor Nahum, Thomas M. Van Vleet, Vikram R. Rao, Heather E. Dawes, Edward F. Chang^*, Vikaas S. Sohal

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

87 Scopus citations

Abstract

Human brain networks that encode variation in mood on naturalistic timescales remain largely unexplored. Here we combine multi-site, semi-chronic, intracranial electroencephalography recordings from the human limbic system with machine learning methods to discover a brain subnetwork that correlates with variation in individual subjects’ self-reported mood over days. First we defined the subnetworks that influence intrinsic brain dynamics by identifying regions that showed coordinated changes in spectral coherence. The most common subnetwork, found in 13 of 21 subjects, was characterized by β-frequency coherence (13-30 Hz) between the amygdala and hippocampus. Increased variability of this subnetwork correlated with worsening mood across these 13 subjects. Moreover, these subjects had significantly higher trait anxiety than the 8 of 21 for whom this amygdala-hippocampus subnetwork was absent. These results demonstrate an approach for extracting network-behavior relationships from complex datasets, and they reveal a conserved subnetwork associated with a psychological trait that significantly influences intrinsic brain dynamics and encodes fluctuations in mood.

Original language	American English
Pages (from-to)	1688-1700.e14
Journal	Cell
Volume	175
Issue number	6
DOIs	https://doi.org/10.1016/j.cell.2018.10.005
State	Published - 29 Nov 2018
Externally published	Yes

Bibliographical note

Funding Information:
We thank the anonymous reviewers for their constructive comments. This research was partially funded by the Defense Advanced Research Projects Agency (DARPA) under cooperative agreement W911NF-14-2-0043 issued by the Army Research Office contracting office in support of DARPA’s SUBNETS program. The views, opinions, and/or findings expressed are those of the author(s) and should not be interpreted as representing the official views or policies of the Department of Defense or the United States government. This work was also supported by a Swartz Foundation postdoctoral fellowship 127713C (to L.A.K).

Publisher Copyright:
© 2018 Elsevier Inc.

Keywords

amygdala
brain networks
coherence
hippocampus
human brain
intracranial electroencephalography
machine learning
mood
β-frequency

Access to Document

10.1016/j.cell.2018.10.005

Cite this

@article{166b817c08314a98b2989d3817a09409,

title = "An Amygdala-Hippocampus Subnetwork that Encodes Variation in Human Mood",

abstract = "Human brain networks that encode variation in mood on naturalistic timescales remain largely unexplored. Here we combine multi-site, semi-chronic, intracranial electroencephalography recordings from the human limbic system with machine learning methods to discover a brain subnetwork that correlates with variation in individual subjects{\textquoteright} self-reported mood over days. First we defined the subnetworks that influence intrinsic brain dynamics by identifying regions that showed coordinated changes in spectral coherence. The most common subnetwork, found in 13 of 21 subjects, was characterized by β-frequency coherence (13-30 Hz) between the amygdala and hippocampus. Increased variability of this subnetwork correlated with worsening mood across these 13 subjects. Moreover, these subjects had significantly higher trait anxiety than the 8 of 21 for whom this amygdala-hippocampus subnetwork was absent. These results demonstrate an approach for extracting network-behavior relationships from complex datasets, and they reveal a conserved subnetwork associated with a psychological trait that significantly influences intrinsic brain dynamics and encodes fluctuations in mood.",

keywords = "amygdala, brain networks, coherence, hippocampus, human brain, intracranial electroencephalography, machine learning, mood, β-frequency",

author = "Kirkby, {Lowry A.} and Luongo, {Francisco J.} and Lee, {Morgan B.} and Mor Nahum and {Van Vleet}, {Thomas M.} and Rao, {Vikram R.} and Dawes, {Heather E.} and Chang, {Edward F.} and Sohal, {Vikaas S.}",

note = "Funding Information: We thank the anonymous reviewers for their constructive comments. This research was partially funded by the Defense Advanced Research Projects Agency (DARPA) under cooperative agreement W911NF-14-2-0043 issued by the Army Research Office contracting office in support of DARPA{\textquoteright}s SUBNETS program. The views, opinions, and/or findings expressed are those of the author(s) and should not be interpreted as representing the official views or policies of the Department of Defense or the United States government. This work was also supported by a Swartz Foundation postdoctoral fellowship 127713C (to L.A.K). Publisher Copyright: {\textcopyright} 2018 Elsevier Inc.",

year = "2018",

month = nov,

day = "29",

doi = "10.1016/j.cell.2018.10.005",

language = "American English",

volume = "175",

pages = "1688--1700.e14",

journal = "Cell",

issn = "0092-8674",

publisher = "Cell Press",

number = "6",

}

TY - JOUR

T1 - An Amygdala-Hippocampus Subnetwork that Encodes Variation in Human Mood

AU - Kirkby, Lowry A.

AU - Luongo, Francisco J.

AU - Lee, Morgan B.

AU - Nahum, Mor

AU - Van Vleet, Thomas M.

AU - Rao, Vikram R.

AU - Dawes, Heather E.

AU - Chang, Edward F.

AU - Sohal, Vikaas S.

N1 - Funding Information: We thank the anonymous reviewers for their constructive comments. This research was partially funded by the Defense Advanced Research Projects Agency (DARPA) under cooperative agreement W911NF-14-2-0043 issued by the Army Research Office contracting office in support of DARPA’s SUBNETS program. The views, opinions, and/or findings expressed are those of the author(s) and should not be interpreted as representing the official views or policies of the Department of Defense or the United States government. This work was also supported by a Swartz Foundation postdoctoral fellowship 127713C (to L.A.K). Publisher Copyright: © 2018 Elsevier Inc.

PY - 2018/11/29

Y1 - 2018/11/29

N2 - Human brain networks that encode variation in mood on naturalistic timescales remain largely unexplored. Here we combine multi-site, semi-chronic, intracranial electroencephalography recordings from the human limbic system with machine learning methods to discover a brain subnetwork that correlates with variation in individual subjects’ self-reported mood over days. First we defined the subnetworks that influence intrinsic brain dynamics by identifying regions that showed coordinated changes in spectral coherence. The most common subnetwork, found in 13 of 21 subjects, was characterized by β-frequency coherence (13-30 Hz) between the amygdala and hippocampus. Increased variability of this subnetwork correlated with worsening mood across these 13 subjects. Moreover, these subjects had significantly higher trait anxiety than the 8 of 21 for whom this amygdala-hippocampus subnetwork was absent. These results demonstrate an approach for extracting network-behavior relationships from complex datasets, and they reveal a conserved subnetwork associated with a psychological trait that significantly influences intrinsic brain dynamics and encodes fluctuations in mood.

AB - Human brain networks that encode variation in mood on naturalistic timescales remain largely unexplored. Here we combine multi-site, semi-chronic, intracranial electroencephalography recordings from the human limbic system with machine learning methods to discover a brain subnetwork that correlates with variation in individual subjects’ self-reported mood over days. First we defined the subnetworks that influence intrinsic brain dynamics by identifying regions that showed coordinated changes in spectral coherence. The most common subnetwork, found in 13 of 21 subjects, was characterized by β-frequency coherence (13-30 Hz) between the amygdala and hippocampus. Increased variability of this subnetwork correlated with worsening mood across these 13 subjects. Moreover, these subjects had significantly higher trait anxiety than the 8 of 21 for whom this amygdala-hippocampus subnetwork was absent. These results demonstrate an approach for extracting network-behavior relationships from complex datasets, and they reveal a conserved subnetwork associated with a psychological trait that significantly influences intrinsic brain dynamics and encodes fluctuations in mood.

KW - amygdala

KW - brain networks

KW - coherence

KW - hippocampus

KW - human brain

KW - intracranial electroencephalography

KW - machine learning

KW - mood

KW - β-frequency

UR - http://www.scopus.com/inward/record.url?scp=85055055167&partnerID=8YFLogxK

U2 - 10.1016/j.cell.2018.10.005

DO - 10.1016/j.cell.2018.10.005

M3 - Article

C2 - 30415834

AN - SCOPUS:85055055167

SN - 0092-8674

VL - 175

SP - 1688-1700.e14

JO - Cell

JF - Cell

IS - 6

ER -

An Amygdala-Hippocampus Subnetwork that Encodes Variation in Human Mood

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this