Mood variations decoded from multi-site intracranial human brain activity

Mood state changes are decoded using human neural activity data from electrodes implanted in seven epilepsy patients. The ability to decode mood state over time from neural activity could enable closed-loop systems to treat neuropsychiatric disorders. However, this decoding has not been demonstrated...

Full description

Saved in:
Bibliographic Details
Published inNature biotechnology Vol. 36; no. 10; pp. 954 - 961
Main Authors Sani, Omid G, Yang, Yuxiao, Lee, Morgan B, Dawes, Heather E, Chang, Edward F, Shanechi, Maryam M
Format Journal Article
LanguageEnglish
Published New York Springer New York 01.11.2018
Nature Publishing Group
Subjects
631/378/116/2394
631/378/1457
631/61
639/166/985
9/30
Agriculture
Analysis
Bioinformatics
Biomedical Engineering/Biotechnology
Biomedicine
Biotechnology
Brain
Closed loops
Decoders
Decoding
Drug therapy
Dynamic models
Emotional disorders
Epilepsy
Feedback control
Human performance
Human subjects
Life Sciences
Medical imaging
Mental disorders
Modelling
Mood
Mood disorders
Nervous system diseases
Neural coding
Neuroimaging
Neurophysiology
Sparsity
Variation
United States
Online AccessGet full text

Cover

Abstract Mood state changes are decoded using human neural activity data from electrodes implanted in seven epilepsy patients. The ability to decode mood state over time from neural activity could enable closed-loop systems to treat neuropsychiatric disorders. However, this decoding has not been demonstrated, partly owing to the difficulty of modeling distributed mood-relevant neural dynamics while dealing with the sparsity of mood state measurements. Here we develop a modeling framework to decode mood state variations from multi-site intracranial recordings in seven human subjects with epilepsy who self-reported their mood state intermittently over multiple days. We built dynamic neural encoding models of mood state and corresponding decoders for each individual and demonstrated that mood state variations over time can be decoded from neural activity. Across subjects, the decoders largely recruited neural signals from limbic regions, whose spectro-spatial features were tuned to mood variations. The dynamic models also provided an analytical tool to compute the timescales of the decoded mood state. These results provide an initial line of evidence indicating the feasibility of mood state decoding.
AbstractList The ability to decode mood state over time from neural activity could enable closed-loop systems to treat neuropsychiatric disorders. However, this decoding has not been demonstrated, partly owing to the difficulty of modeling distributed mood-relevant neural dynamics while dealing with the sparsity of mood state measurements. Here we develop a modeling framework to decode mood state variations from multi-site intracranial recordings in seven human subjects with epilepsy who self-reported their mood state intermittently over multiple days. We built dynamic neural encoding models of mood state and corresponding decoders for each individual and demonstrated that mood state variations over time can be decoded from neural activity. Across subjects, the decoders largely recruited neural signals from limbic regions, whose spectro-spatial features were tuned to mood variations. The dynamic models also provided an analytical tool to compute the timescales of the decoded mood state. These results provide an initial line of evidence indicating the feasibility of mood state decoding.The ability to decode mood state over time from neural activity could enable closed-loop systems to treat neuropsychiatric disorders. However, this decoding has not been demonstrated, partly owing to the difficulty of modeling distributed mood-relevant neural dynamics while dealing with the sparsity of mood state measurements. Here we develop a modeling framework to decode mood state variations from multi-site intracranial recordings in seven human subjects with epilepsy who self-reported their mood state intermittently over multiple days. We built dynamic neural encoding models of mood state and corresponding decoders for each individual and demonstrated that mood state variations over time can be decoded from neural activity. Across subjects, the decoders largely recruited neural signals from limbic regions, whose spectro-spatial features were tuned to mood variations. The dynamic models also provided an analytical tool to compute the timescales of the decoded mood state. These results provide an initial line of evidence indicating the feasibility of mood state decoding.
The ability to decode mood state over time from neural activity could enable closed-loop systems to treat neuropsychiatric disorders. However, this decoding has not been demonstrated, partly owing to the difficulty of modeling distributed mood-relevant neural dynamics while dealing with the sparsity of mood state measurements. Here we develop a modeling framework to decode mood state variations from multi-site intracranial recordings in seven human subjects with epilepsy who self-reported their mood state intermittently over multiple days. We built dynamic neural encoding models of mood state and corresponding decoders for each individual and demonstrated that mood state variations over time can be decoded from neural activity. Across subjects, the decoders largely recruited neural signals from limbic regions, whose spectro-spatial features were tuned to mood variations. The dynamic models also provided an analytical tool to compute the timescales of the decoded mood state. These results provide an initial line of evidence indicating the feasibility of mood state decoding.
Mood state changes are decoded using human neural activity data from electrodes implanted in seven epilepsy patients.The ability to decode mood state over time from neural activity could enable closed-loop systems to treat neuropsychiatric disorders. However, this decoding has not been demonstrated, partly owing to the difficulty of modeling distributed mood-relevant neural dynamics while dealing with the sparsity of mood state measurements. Here we develop a modeling framework to decode mood state variations from multi-site intracranial recordings in seven human subjects with epilepsy who self-reported their mood state intermittently over multiple days. We built dynamic neural encoding models of mood state and corresponding decoders for each individual and demonstrated that mood state variations over time can be decoded from neural activity. Across subjects, the decoders largely recruited neural signals from limbic regions, whose spectro-spatial features were tuned to mood variations. The dynamic models also provided an analytical tool to compute the timescales of the decoded mood state. These results provide an initial line of evidence indicating the feasibility of mood state decoding.
Mood state changes are decoded using human neural activity data from electrodes implanted in seven epilepsy patients. The ability to decode mood state over time from neural activity could enable closed-loop systems to treat neuropsychiatric disorders. However, this decoding has not been demonstrated, partly owing to the difficulty of modeling distributed mood-relevant neural dynamics while dealing with the sparsity of mood state measurements. Here we develop a modeling framework to decode mood state variations from multi-site intracranial recordings in seven human subjects with epilepsy who self-reported their mood state intermittently over multiple days. We built dynamic neural encoding models of mood state and corresponding decoders for each individual and demonstrated that mood state variations over time can be decoded from neural activity. Across subjects, the decoders largely recruited neural signals from limbic regions, whose spectro-spatial features were tuned to mood variations. The dynamic models also provided an analytical tool to compute the timescales of the decoded mood state. These results provide an initial line of evidence indicating the feasibility of mood state decoding.
Audience Academic
Author Dawes, Heather E
Shanechi, Maryam M
Yang, Yuxiao
Lee, Morgan B
Chang, Edward F
Sani, Omid G
Author_xml – sequence: 1
  givenname: Omid G
  orcidid: 0000-0003-3032-5669
  surname: Sani
  fullname: Sani, Omid G
  organization: Ming Hsieh Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California
– sequence: 2
  givenname: Yuxiao
  orcidid: 0000-0001-9208-7956
  surname: Yang
  fullname: Yang, Yuxiao
  organization: Ming Hsieh Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California
– sequence: 3
  givenname: Morgan B
  orcidid: 0000-0001-6936-0109
  surname: Lee
  fullname: Lee, Morgan B
  organization: Department of Neurological Surgery, University of California, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA., Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, California, USA
– sequence: 4
  givenname: Heather E
  surname: Dawes
  fullname: Dawes, Heather E
  organization: Department of Neurological Surgery, University of California, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA., Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, California, USA
– sequence: 5
  givenname: Edward F
  surname: Chang
  fullname: Chang, Edward F
  email: edward.chang@ucsf.edu
  organization: Department of Neurological Surgery, University of California, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA., Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, California, USA
– sequence: 6
  givenname: Maryam M
  orcidid: 0000-0002-0544-7720
  surname: Shanechi
  fullname: Shanechi, Maryam M
  email: shanechi@usc.edu
  organization: Ming Hsieh Department of Electrical Engineering, Viterbi School of Engineering, University of Southern California, Neuroscience Graduate Program, University of Southern California
BackLink https://www.ncbi.nlm.nih.gov/pubmed/30199076$$D View this record in MEDLINE/PubMed
BookMark eNqNkm9rFDEQxoNU7B8FP4Es-EbBPZPNZpJ9WYrWg0pBi29Dks1eU3aTmmSL_fbm2qvnaREJJGH4zSTPzHOI9nzwFqGXBC8IpuK913nRNhg_QQeEtVAT6GCv3LHgNSYM9tFhSlcYY2gBnqF9iknXYQ4HaPk5hL66UdGp7IJPVW9N6G1fDTFM1TSP2dXJZVs5n6MyUXmnxupynpSvdFTOV8pkd-Py7XP0dFBjsi825xG6-Pjh4uRTfXZ-ujw5PqsNtCzXFjTVjAveEKBCgMC8x5grbrSgHdegDaeDEQyYGAbTG8Ws0CViDNUC6BF6c1_2Oobvs01ZTi4ZO47K2zAn2ZCijRAgrKCv_0Cvwhx9-ZykTQOUUgL8X1RDGAfaNRi21EqNVjo_hHU71k_LY8YbjlvS0EItHqHK6u3kTJnZ4Ep8J-HtTkJhsv2RV2pOSS6_fvl_9vzbLvvuN1bPyXmbypbc6jKn-5Qd_NWmB7OebC-vo5tUvJUPPtkKMzGkFO0gjct3jikC3SgJlmsjymJEuTbidky_Eh5qPoJudKWC-JWN2wH8xf4Ex7nl3w
CitedBy_id crossref_primary_10_1088_1741_2552_adba8d
crossref_primary_10_3390_brainsci10100669
crossref_primary_10_1016_j_bspc_2023_105249
crossref_primary_10_1038_s41593_020_00733_0
crossref_primary_10_1097_HRP_0000000000000367
crossref_primary_10_1038_s41551_023_01117_y
crossref_primary_10_1109_TNSRE_2023_3327740
crossref_primary_10_1016_j_cobme_2018_11_004
crossref_primary_10_1038_s41531_022_00399_4
crossref_primary_10_3758_s13415_024_01216_6
crossref_primary_10_1088_1741_2552_ab5d47
crossref_primary_10_1088_1741_2552_ab3dbc
crossref_primary_10_1088_1741_2552_ab51a5
crossref_primary_10_1088_1741_2552_ac02dc
crossref_primary_10_3389_fnins_2021_789868
crossref_primary_10_7554_eLife_89421_3
crossref_primary_10_2139_ssrn_4124479
crossref_primary_10_1016_j_crmeth_2023_100691
crossref_primary_10_1146_annurev_pharmtox_010919_023253
crossref_primary_10_3389_fnins_2019_00152
crossref_primary_10_1007_s11948_020_00218_0
crossref_primary_10_1038_s41467_020_20197_x
crossref_primary_10_1002_aisy_202200313
crossref_primary_10_1038_s41582_020_00426_z
crossref_primary_10_1088_1741_2552_ac9b94
crossref_primary_10_1016_j_patter_2022_100602
crossref_primary_10_1088_1741_2552_abcefd
crossref_primary_10_1371_journal_pcbi_1010401
crossref_primary_10_1088_1741_2552_ad3678
crossref_primary_10_1109_JSSC_2022_3204508
crossref_primary_10_1088_1741_2552_ac160f
crossref_primary_10_1088_1741_2552_ac160e
crossref_primary_10_3389_fnins_2022_818214
crossref_primary_10_1016_j_tics_2021_04_003
crossref_primary_10_1016_j_eswa_2024_126311
crossref_primary_10_1088_1741_2552_ac857c
crossref_primary_10_3389_fnins_2022_829755
crossref_primary_10_1109_TBME_2023_3319956
crossref_primary_10_31083_j_jin2301018
crossref_primary_10_1088_1741_2552_ac4e1c
crossref_primary_10_1109_TNSRE_2019_2908156
crossref_primary_10_1016_j_neures_2020_01_002
crossref_primary_10_1038_s41587_019_0244_6
crossref_primary_10_1088_1741_2552_ace932
crossref_primary_10_1038_s41551_020_00666_w
crossref_primary_10_1088_1741_2552_ad8839
crossref_primary_10_1002_EXP_20230146
crossref_primary_10_1016_j_neubiorev_2020_06_024
crossref_primary_10_7554_eLife_89421
crossref_primary_10_1016_j_crneur_2022_100071
crossref_primary_10_1016_j_psychres_2019_02_076
crossref_primary_10_3389_fnhum_2021_717401
crossref_primary_10_1088_1741_2552_ad038d
crossref_primary_10_1088_1741_2552_abda0b
crossref_primary_10_7554_eLife_72440
crossref_primary_10_1073_pnas_2409423122
crossref_primary_10_1088_1741_2552_acbe20
crossref_primary_10_3389_fnins_2021_658930
crossref_primary_10_1088_1741_2552_ac63e9
crossref_primary_10_1038_s41551_024_01314_3
crossref_primary_10_1109_TNSRE_2019_2913218
crossref_primary_10_1016_j_neuroimage_2022_118969
crossref_primary_10_1038_s41386_020_00805_6
crossref_primary_10_3389_fpsyt_2023_1129954
crossref_primary_10_1088_1741_2552_ac33e7
crossref_primary_10_1016_j_brs_2023_07_049
crossref_primary_10_1088_1741_2552_ab2214
crossref_primary_10_1016_j_brainres_2024_148914
crossref_primary_10_1016_j_yebeh_2024_109659
crossref_primary_10_3389_fnhum_2024_1332451
crossref_primary_10_3389_fnhum_2021_702605
crossref_primary_10_1016_j_jns_2021_120121
crossref_primary_10_1093_pnasnexus_pgad442
crossref_primary_10_1016_j_biopsych_2021_11_007
crossref_primary_10_1016_j_cell_2024_09_028
crossref_primary_10_1126_sciadv_adh0974
crossref_primary_10_3389_fncom_2024_1273053
crossref_primary_10_1016_j_biopsych_2022_09_032
crossref_primary_10_1088_1741_2552_ab225b
crossref_primary_10_1002_ana_25821
crossref_primary_10_1088_1741_2552_ac6d7c
crossref_primary_10_1360_TB_2024_0469
crossref_primary_10_1088_1741_2552_ad200e
crossref_primary_10_1159_000540319
crossref_primary_10_1038_s41562_022_01310_0
crossref_primary_10_1038_s41551_023_01106_1
crossref_primary_10_1038_s41593_023_01338_z
crossref_primary_10_1088_1741_2552_aaeb1a
crossref_primary_10_1016_j_conb_2019_03_008
crossref_primary_10_1088_1741_2552_ad1053
crossref_primary_10_3389_fnhum_2024_1388267
crossref_primary_10_1126_scitranslmed_aay4682
crossref_primary_10_3389_fnins_2020_00123
crossref_primary_10_1016_j_pmip_2019_07_002
crossref_primary_10_1093_cercor_bhad517
crossref_primary_10_1176_appi_neuropsych_20100268
crossref_primary_10_1007_s11517_023_02865_4
crossref_primary_10_1016_j_wneu_2024_09_131
crossref_primary_10_3389_fnins_2021_748165
crossref_primary_10_3389_fncir_2021_701080
crossref_primary_10_1088_1741_2552_ac7005
crossref_primary_10_1162_neco_a_01491
crossref_primary_10_1073_pnas_2212887121
crossref_primary_10_1016_j_pmip_2024_100135
crossref_primary_10_1007_s11263_022_01713_6
crossref_primary_10_1016_j_biomaterials_2025_123288
crossref_primary_10_1038_s41386_023_01643_y
crossref_primary_10_1016_S2215_0366_20_30187_5
crossref_primary_10_1038_s41467_023_43257_4
crossref_primary_10_3389_fnhum_2021_746499
crossref_primary_10_1109_JBHI_2023_3292452
crossref_primary_10_1016_j_neuron_2020_06_012
crossref_primary_10_1038_s41467_024_48367_1
crossref_primary_10_1088_1741_2552_ad5406
crossref_primary_10_3389_fncom_2023_1119685
crossref_primary_10_1038_s41593_019_0488_y
crossref_primary_10_3390_brainsci13070977
crossref_primary_10_1016_j_conb_2019_06_008
crossref_primary_10_1038_s41587_019_0397_3
crossref_primary_10_1109_MSSC_2023_3309782
crossref_primary_10_3389_fnins_2021_653965
crossref_primary_10_1016_j_brs_2021_06_009
crossref_primary_10_1038_s41593_024_01731_2
crossref_primary_10_1161_JAHA_122_026067
crossref_primary_10_1088_1741_2552_acec14
crossref_primary_10_1016_j_conb_2020_11_016
crossref_primary_10_1038_s42003_019_0360_3
crossref_primary_10_1111_1744_1633_70002
crossref_primary_10_1088_1741_2552_ab2c58
crossref_primary_10_1038_s41551_021_00804_y
crossref_primary_10_1039_D4CS00413B
crossref_primary_10_3389_fpsyt_2022_938694
crossref_primary_10_1016_j_neuroimage_2020_117515
crossref_primary_10_1088_1741_2552_ab0ea4
crossref_primary_10_1088_1741_2552_ad9956
crossref_primary_10_1016_j_expneurol_2022_113993
crossref_primary_10_53053_MHCS4874
crossref_primary_10_1038_nbt_4258
crossref_primary_10_1016_j_biopsych_2023_01_020
crossref_primary_10_3171_2020_4_JNS2061
crossref_primary_10_1038_s44222_024_00177_2
crossref_primary_10_1177_08830738231167736
crossref_primary_10_1162_neco_a_01196
Cites_doi 10.3389/fnhum.2012.00112
10.1371/journal.pcbi.1006168
10.1176/appi.ajp.2014.14020263
10.1016/j.neuroimage.2005.01.014
10.1002/ana.22613
10.1038/nn.4101
10.1088/1741-2560/13/2/026017
10.1016/j.neulet.2009.03.094
10.1016/j.neuroimage.2012.01.021
10.1007/978-0-387-21606-5
10.1016/j.jneumeth.2007.10.001
10.1038/sj.npp.1301408
10.2196/mhealth.6544
10.1016/j.neuron.2014.08.038
10.1093/brain/aws059
10.1038/mp.2014.2
10.1037/0021-843X.100.3.316
10.1371/journal.pone.0055344
10.1046/j.1525-1497.2001.016009606.x
10.1016/S0378-3758(99)00041-5
10.1016/j.biopsych.2016.02.032
10.1109/TNSRE.2017.2677443
10.1016/j.biopsych.2007.05.033
10.1371/journal.pone.0032508
10.1109/TNSRE.2016.2639501
10.1038/nature11911
10.1037/0033-295X.110.1.145
10.1371/journal.pcbi.1004730
10.1016/j.neuron.2006.09.019
10.1152/jn.2001.86.5.2125
10.1586/14737175.7.1.63
10.1038/nn.4553
10.1016/S0959-4388(00)00203-8
10.1088/1741-2560/12/3/036009
10.1038/ncomms13825
10.1093/bmb/65.1.193
10.1016/S0140-6736(06)67964-6
10.1016/j.neuroimage.2014.06.078
10.1176/ajp.156.5.675
10.1002/hbm.20426
10.1016/j.neuron.2011.10.020
10.1016/j.neuron.2005.02.014
10.1016/j.neubiorev.2015.07.014
10.1038/nature14366
10.1006/ebeh.2000.0046
10.1098/rstb.2001.0915
10.1007/978-1-4613-0465-4
10.1038/nrn3119
10.1016/j.tics.2010.06.007
10.1016/j.biopsych.2008.08.029
10.1038/s41598-017-12457-6
10.1038/nature12018
10.1371/journal.pbio.2000106
10.1088/1741-2560/1/2/001
10.1109/MSP.2008.4408438
10.1038/nrn2653
10.1016/S0140-6736(11)60602-8
10.1038/nature11740
10.1126/scitranslmed.3007303
10.1176/appi.ajp.2007.07030504
10.1038/nm.4246
10.1001/archinte.166.10.1092
10.1016/j.cell.2015.01.045
10.1016/j.cub.2014.07.068
10.1088/1741-2560/13/6/066019
10.1192/S0007125000298395
10.1088/1741-2560/11/2/026002
10.1016/j.neuron.2008.10.037
10.1038/nature13665
10.1016/j.jpain.2009.09.001
10.1017/S0954579405050340
10.1073/pnas.1417017112
10.1038/ncomms8759
10.1016/j.pnpbp.2007.06.030
10.1176/appi.books.9780890425596
ContentType Journal Article
Copyright Springer Nature America, Inc. 2018
COPYRIGHT 2018 Nature Publishing Group
Copyright Nature Publishing Group Oct 2018
Springer Nature America, Inc. 2018.
Copyright_xml – notice: Springer Nature America, Inc. 2018
– notice: COPYRIGHT 2018 Nature Publishing Group
– notice: Copyright Nature Publishing Group Oct 2018
– notice: Springer Nature America, Inc. 2018.
DBID AAYXX
CITATION
NPM
N95
IOV
ISR
3V.
7QO
7QP
7QR
7T7
7TK
7TM
7X7
7XB
88A
88E
88I
8AO
8FD
8FE
8FG
8FH
8FI
8FJ
8FK
8G5
ABJCF
ABUWG
AEUYN
AFKRA
AZQEC
BBNVY
BENPR
BGLVJ
BHPHI
C1K
CCPQU
DWQXO
FR3
FYUFA
GHDGH
GNUQQ
GUQSH
HCIFZ
K9.
L6V
LK8
M0S
M1P
M2O
M2P
M7P
M7S
MBDVC
P64
PHGZM
PHGZT
PJZUB
PKEHL
PPXIY
PQEST
PQGLB
PQQKQ
PQUKI
PTHSS
Q9U
RC3
PRINS
7X8
DOI 10.1038/nbt.4200
DatabaseName CrossRef
PubMed
Gale Business Insights: Global
Gale In Context: Opposing Viewpoints
Gale In Context: Science
ProQuest Central (Corporate)
Biotechnology Research Abstracts
Calcium & Calcified Tissue Abstracts
Chemoreception Abstracts
Industrial and Applied Microbiology Abstracts (Microbiology A)
Neurosciences Abstracts
Nucleic Acids Abstracts
ProQuest Health & Medical Collection
ProQuest Central (purchase pre-March 2016)
Biology Database (Alumni Edition)
Medical Database (Alumni Edition)
Science Database (Alumni Edition)
ProQuest Pharma Collection
Technology Research Database
ProQuest SciTech Collection
ProQuest Technology Collection
ProQuest Natural Science Collection
Hospital Premium Collection
Hospital Premium Collection (Alumni Edition)
ProQuest Central (Alumni) (purchase pre-March 2016)
ProQuest Research Library
Materials Science & Engineering Collection
ProQuest Central (Alumni)
ProQuest One Sustainability (subscription)
ProQuest Central UK/Ireland
ProQuest Central Essentials
Biological Science Collection
ProQuest Central
Technology Collection
Natural Science Collection
Environmental Sciences and Pollution Management
ProQuest One Community College
ProQuest Central Korea
Engineering Research Database
Health Research Premium Collection
Health Research Premium Collection (Alumni)
ProQuest Central Student
ProQuest Research Library
SciTech Premium Collection
ProQuest Health & Medical Complete (Alumni)
ProQuest Engineering Collection
ProQuest Biological Science Collection
Health & Medical Collection (Alumni)
PML(ProQuest Medical Library)
ProQuest Research Library
Science Database
Biological Science Database
Engineering Database
Research Library (Corporate)
Biotechnology and BioEngineering Abstracts
ProQuest Central Premium
ProQuest One Academic
ProQuest Health & Medical Research Collection
ProQuest One Academic Middle East (New)
ProQuest One Health & Nursing
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Applied & Life Sciences
ProQuest One Academic
ProQuest One Academic UKI Edition
Engineering Collection
ProQuest Central Basic
Genetics Abstracts
ProQuest Central China
MEDLINE - Academic
DatabaseTitle CrossRef
PubMed
Research Library Prep
ProQuest Central Student
ProQuest Central Essentials
Nucleic Acids Abstracts
SciTech Premium Collection
Environmental Sciences and Pollution Management
ProQuest One Applied & Life Sciences
ProQuest One Sustainability
Health Research Premium Collection
Natural Science Collection
Health & Medical Research Collection
Biological Science Collection
Chemoreception Abstracts
Industrial and Applied Microbiology Abstracts (Microbiology A)
ProQuest Central (New)
ProQuest Medical Library (Alumni)
Engineering Collection
Engineering Database
ProQuest Science Journals (Alumni Edition)
ProQuest Biological Science Collection
ProQuest One Academic Eastern Edition
ProQuest Hospital Collection
ProQuest Technology Collection
Health Research Premium Collection (Alumni)
Biological Science Database
Neurosciences Abstracts
ProQuest Hospital Collection (Alumni)
Biotechnology and BioEngineering Abstracts
ProQuest Health & Medical Complete
ProQuest One Academic UKI Edition
Engineering Research Database
ProQuest One Academic
Calcium & Calcified Tissue Abstracts
ProQuest One Academic (New)
Technology Collection
Technology Research Database
ProQuest One Academic Middle East (New)
ProQuest Health & Medical Complete (Alumni)
ProQuest Central (Alumni Edition)
ProQuest One Community College
ProQuest One Health & Nursing
Research Library (Alumni Edition)
ProQuest Natural Science Collection
ProQuest Pharma Collection
ProQuest Biology Journals (Alumni Edition)
ProQuest Central
ProQuest Health & Medical Research Collection
Genetics Abstracts
ProQuest Engineering Collection
Biotechnology Research Abstracts
Health and Medicine Complete (Alumni Edition)
ProQuest Central Korea
ProQuest Research Library
ProQuest Central Basic
ProQuest Science Journals
ProQuest SciTech Collection
ProQuest Medical Library
Materials Science & Engineering Collection
ProQuest Central (Alumni)
ProQuest Central China
MEDLINE - Academic
DatabaseTitleList MEDLINE - Academic
Research Library Prep
Research Library Prep

PubMed
Database_xml – sequence: 1
  dbid: NPM
  name: PubMed
  url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 2
  dbid: 8FG
  name: ProQuest Technology Collection
  url: https://search.proquest.com/technologycollection1
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Medicine
Engineering
Agriculture
Biology
EISSN 1546-1696
EndPage 961
ExternalDocumentID A572704123
30199076
10_1038_nbt_4200
Genre Research Support, U.S. Gov't, Non-P.H.S
Research Support, Non-U.S. Gov't
Journal Article
GeographicLocations United States
GeographicLocations_xml – name: United States
GroupedDBID ---
-~X
.55
.GJ
0R~
123
29M
2FS
2XV
36B
39C
4.4
4R4
53G
5BI
5M7
5RE
5S5
70F
7X7
88E
88I
8AO
8CJ
8FE
8FG
8FH
8FI
8FJ
8G5
8R4
8R5
A8Z
AAHBH
AAIKC
AAMNW
AARCD
AAYOK
AAYZH
ABAWZ
ABDBF
ABDPE
ABEFU
ABJCF
ABJNI
ABLJU
ABOCM
ABUWG
ACBTR
ACBWK
ACGFO
ACGFS
ACGOD
ACIWK
ACMFV
ACMJI
ACPRK
ACUHS
ADBBV
ADFRT
AENEX
AEUYN
AFANA
AFBBN
AFFNX
AFKRA
AFRAH
AFSHS
AGAYW
AHBCP
AHMBA
AHOSX
AHSBF
AIBTJ
ALFFA
ALIPV
ALMA_UNASSIGNED_HOLDINGS
ALPWD
AMTXH
ARMCB
ASPBG
ATHPR
AVWKF
AXYYD
AZFZN
AZQEC
BAAKF
BBNVY
BENPR
BGLVJ
BHPHI
BKKNO
BKOMP
BPHCQ
BVXVI
C0K
CCPQU
D1J
DB5
DU5
DWQXO
EAD
EAP
EAS
EBC
EBS
EE.
EJD
EMB
EMK
EMOBN
ESX
EXGXG
F5P
FA8
FEDTE
FQGFK
FSGXE
FYUFA
GNUQQ
GUQSH
GX1
HCIFZ
HMCUK
HVGLF
HZ~
IAG
IAO
IEA
IEP
IH2
IHR
INH
INR
IOV
ISR
ITC
KOO
L6V
LGEZI
LK8
LOTEE
M1P
M2O
M2P
M7P
M7S
ML0
MVM
N95
NADUK
NEJ
NFIDA
NNMJJ
NXXTH
O9-
ODYON
P2P
PHGZM
PHGZT
PQQKQ
PROAC
PSQYO
PTHSS
Q2X
QF4
QM4
QN7
QO4
RNS
RNT
RNTTT
RVV
RXW
SHXYY
SIXXV
SJN
SNYQT
SOJ
SV3
TAE
TAOOD
TBHMF
TDRGL
TN5
TSG
TUS
U5U
UKHRP
X7M
XOL
Y6R
YZZ
ZGI
ZHY
ZXP
~KM
AAYXX
CITATION
PJZUB
PPXIY
PQGLB
ABFSG
ACSTC
AEZWR
AFHIU
AHWEU
AIXLP
NPM
PMFND
3V.
7QO
7QP
7QR
7T7
7TK
7TM
7XB
88A
8FD
8FK
C1K
FR3
K9.
MBDVC
P64
PKEHL
PQEST
PQUKI
Q9U
RC3
PRINS
7X8
ID FETCH-LOGICAL-c645t-e6b3b57872163886807d007a7cb8397b6bc73fc85658ffcdca5e8b3fccc3b863
IEDL.DBID 7X7
ISSN 1087-0156
1546-1696
IngestDate Fri Jul 11 02:40:30 EDT 2025
Sat Aug 23 14:03:49 EDT 2025
Fri Jul 25 09:00:37 EDT 2025
Tue Jun 17 21:24:04 EDT 2025
Tue Jun 10 20:41:37 EDT 2025
Fri Jun 27 05:08:33 EDT 2025
Fri Jun 27 03:49:42 EDT 2025
Fri Jun 27 03:06:22 EDT 2025
Mon Jul 21 06:04:33 EDT 2025
Thu Apr 24 22:58:05 EDT 2025
Tue Aug 12 04:01:14 EDT 2025
Wed May 21 12:01:57 EDT 2025
IsDoiOpenAccess false
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 10
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c645t-e6b3b57872163886807d007a7cb8397b6bc73fc85658ffcdca5e8b3fccc3b863
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ORCID 0000-0002-0544-7720
0000-0001-9208-7956
0000-0003-3032-5669
0000-0001-6936-0109
OpenAccessLink https://www.nature.com/articles/nbt.4200.pdf
PMID 30199076
PQID 2157639206
PQPubID 47191
PageCount 8
ParticipantIDs proquest_miscellaneous_2101911615
proquest_journals_3226333167
proquest_journals_2157639206
gale_infotracmisc_A572704123
gale_infotracacademiconefile_A572704123
gale_incontextgauss_ISR_A572704123
gale_incontextgauss_IOV_A572704123
gale_businessinsightsgauss_A572704123
pubmed_primary_30199076
crossref_citationtrail_10_1038_nbt_4200
crossref_primary_10_1038_nbt_4200
springer_journals_10_1038_nbt_4200
PublicationCentury 2000
PublicationDate 2018-11-01
PublicationDateYYYYMMDD 2018-11-01
PublicationDate_xml – month: 11
  year: 2018
  text: 2018-11-01
  day: 01
PublicationDecade 2010
PublicationPlace New York
PublicationPlace_xml – name: New York
– name: United States
PublicationSubtitle The Science and Business of Biotechnology
PublicationTitle Nature biotechnology
PublicationTitleAbbrev Nat Biotechnol
PublicationTitleAlternate Nat Biotechnol
PublicationYear 2018
Publisher Springer New York
Nature Publishing Group
Publisher_xml – name: Springer New York
– name: Nature Publishing Group
References BP Hermann (BFnbt4200_CR53) 2000; 41
JP Dmochowski (BFnbt4200_CR3) 2012; 6
PB Fitzgerald (BFnbt4200_CR29) 2008; 29
D Yekutieli (BFnbt4200_CR63) 1999; 82
S Grimm (BFnbt4200_CR21) 2008; 63
AF Leuchter (BFnbt4200_CR15) 2012; 7
DA Malone Jr. (BFnbt4200_CR17) 2009; 65
TE Schlaepfer (BFnbt4200_CR19) 2008; 33
MM Shanechi (BFnbt4200_CR68) 2016; 12
NG Hatsopoulos (BFnbt4200_CR33) 2011; 72
AK Mak (BFnbt4200_CR47) 2009; 457
KP Ebmeier (BFnbt4200_CR75) 2006; 367
MH Schieber (BFnbt4200_CR30) 2001; 86
NV Thakor (BFnbt4200_CR43) 2013; 5
S-Y Kim (BFnbt4200_CR76) 2013; 496
KE Bouchard (BFnbt4200_CR23) 2013; 495
J Posner (BFnbt4200_CR13) 2005; 17
K So (BFnbt4200_CR69) 2014; 11
LA Clark (BFnbt4200_CR12) 1991; 100
BFnbt4200_CR49
A Burgos-Robles (BFnbt4200_CR74) 2017; 20
A Korzeniewska (BFnbt4200_CR24) 2014; 101
HS Mayberg (BFnbt4200_CR10) 1999; 156
M Nahum (BFnbt4200_CR26) 2017; 5
T Pistohl (BFnbt4200_CR67) 2008; 167
RA Andersen (BFnbt4200_CR31) 2014; 24
KM Tye (BFnbt4200_CR80) 2013; 493
MM Shanechi (BFnbt4200_CR40) 2017; 25
L-L Zeng (BFnbt4200_CR11) 2012; 135
AT Drysdale (BFnbt4200_CR5) 2017; 23
B-T Lee (BFnbt4200_CR22) 2007; 31
JC Kao (BFnbt4200_CR66) 2015; 6
MA Nicolelis (BFnbt4200_CR36) 2009; 10
K Haroush (BFnbt4200_CR6) 2015; 160
MM Shanechi (BFnbt4200_CR41) 2017; 8
RL Spitzer (BFnbt4200_CR52) 2006; 166
HS Mayberg (BFnbt4200_CR18) 2005; 45
PT Sadtler (BFnbt4200_CR37) 2014; 512
PA Kragel (BFnbt4200_CR7) 2016; 14
DM Brandman (BFnbt4200_CR32) 2017; 25
JA Russell (BFnbt4200_CR50) 2003; 110
P Sajda (BFnbt4200_CR38) 2008; 25
MS George (BFnbt4200_CR55) 2007; 7
WC Drevets (BFnbt4200_CR4) 2001; 11
GG Calhoon (BFnbt4200_CR1) 2015; 18
T Yanagisawa (BFnbt4200_CR45) 2012; 71
H-L Hsieh (BFnbt4200_CR65) 2018; 14
BFnbt4200_CR73
U Habel (BFnbt4200_CR46) 2005; 26
K Kroenke (BFnbt4200_CR51) 2001; 16
RB Yaffe (BFnbt4200_CR25) 2014; 111
DJ Kupfer (BFnbt4200_CR8) 2012; 379
V Michopoulos (BFnbt4200_CR77) 2015; 172
P Namburi (BFnbt4200_CR79) 2015; 520
W Wang (BFnbt4200_CR44) 2013; 8
BFnbt4200_CR72
RZ Goldstein (BFnbt4200_CR57) 2011; 12
JP Donoghue (BFnbt4200_CR64) 2008; 60
A Etkin (BFnbt4200_CR56) 2007; 164
BFnbt4200_CR27
KV Shenoy (BFnbt4200_CR42) 2014; 84
BFnbt4200_CR28
EC Leuthardt (BFnbt4200_CR35) 2004; 1
Y Yang (BFnbt4200_CR71) 2016; 13
DL Clark (BFnbt4200_CR14) 2016; 80
WJ Neumann (BFnbt4200_CR16) 2014; 19
DA Clark (BFnbt4200_CR20) 2010; 14
R Dan (BFnbt4200_CR2) 2017; 7
J Mazziotta (BFnbt4200_CR62) 2001; 356
SD Stavisky (BFnbt4200_CR70) 2015; 12
PC Mulders (BFnbt4200_CR78) 2015; 56
N Sartorius (BFnbt4200_CR48) 1996; 1996
AB Schwartz (BFnbt4200_CR39) 2006; 52
B Fischl (BFnbt4200_CR61) 2012; 62
G Hotson (BFnbt4200_CR34) 2016; 13
HS Mayberg (BFnbt4200_CR9) 2003; 65
I Tracey (BFnbt4200_CR58) 2009; 10
Y Yang (BFnbt4200_CR60) 2017; 2017
CL Harden (BFnbt4200_CR54) 2000; 1
Y Yang (BFnbt4200_CR59) 2016; 2016
References_xml – volume: 6
  start-page: 112
  year: 2012
  ident: BFnbt4200_CR3
  publication-title: Front. Hum. Neurosci.
  doi: 10.3389/fnhum.2012.00112
– volume: 14
  start-page: e1006168
  year: 2018
  ident: BFnbt4200_CR65
  publication-title: PLoS Comput. Biol.
  doi: 10.1371/journal.pcbi.1006168
– volume: 172
  start-page: 353
  year: 2015
  ident: BFnbt4200_CR77
  publication-title: Am. J. Psychiatry
  doi: 10.1176/appi.ajp.2014.14020263
– volume: 26
  start-page: 206
  year: 2005
  ident: BFnbt4200_CR46
  publication-title: Neuroimage
  doi: 10.1016/j.neuroimage.2005.01.014
– volume: 71
  start-page: 353
  year: 2012
  ident: BFnbt4200_CR45
  publication-title: Ann. Neurol.
  doi: 10.1002/ana.22613
– volume: 18
  start-page: 1394
  year: 2015
  ident: BFnbt4200_CR1
  publication-title: Nat. Neurosci.
  doi: 10.1038/nn.4101
– volume: 13
  start-page: 026017
  year: 2016
  ident: BFnbt4200_CR34
  publication-title: J. Neural Eng.
  doi: 10.1088/1741-2560/13/2/026017
– volume: 457
  start-page: 101
  year: 2009
  ident: BFnbt4200_CR47
  publication-title: Neurosci. Lett.
  doi: 10.1016/j.neulet.2009.03.094
– volume: 62
  start-page: 774
  year: 2012
  ident: BFnbt4200_CR61
  publication-title: Neuroimage
  doi: 10.1016/j.neuroimage.2012.01.021
– ident: BFnbt4200_CR27
  doi: 10.1007/978-0-387-21606-5
– volume: 167
  start-page: 105
  year: 2008
  ident: BFnbt4200_CR67
  publication-title: J. Neurosci. Methods
  doi: 10.1016/j.jneumeth.2007.10.001
– volume: 33
  start-page: 368
  year: 2008
  ident: BFnbt4200_CR19
  publication-title: Neuropsychopharmacology
  doi: 10.1038/sj.npp.1301408
– volume: 5
  start-page: e44
  year: 2017
  ident: BFnbt4200_CR26
  publication-title: JMIR Mhealth Uhealth
  doi: 10.2196/mhealth.6544
– volume: 41
  start-page: S31
  issue: Suppl. 2
  year: 2000
  ident: BFnbt4200_CR53
  publication-title: Epilepsia
– volume: 84
  start-page: 665
  year: 2014
  ident: BFnbt4200_CR42
  publication-title: Neuron
  doi: 10.1016/j.neuron.2014.08.038
– volume: 135
  start-page: 1498
  year: 2012
  ident: BFnbt4200_CR11
  publication-title: Brain
  doi: 10.1093/brain/aws059
– volume: 19
  start-page: 1186
  year: 2014
  ident: BFnbt4200_CR16
  publication-title: Mol. Psychiatry
  doi: 10.1038/mp.2014.2
– volume: 100
  start-page: 316
  year: 1991
  ident: BFnbt4200_CR12
  publication-title: J. Abnorm. Psychol.
  doi: 10.1037/0021-843X.100.3.316
– volume: 8
  start-page: e55344
  year: 2013
  ident: BFnbt4200_CR44
  publication-title: PLoS One
  doi: 10.1371/journal.pone.0055344
– volume: 2016
  start-page: 1766
  year: 2016
  ident: BFnbt4200_CR59
  publication-title: Conf. Proc. IEEE Eng. Med. Biol. Soc.
– volume: 16
  start-page: 606
  year: 2001
  ident: BFnbt4200_CR51
  publication-title: J. Gen. Intern. Med.
  doi: 10.1046/j.1525-1497.2001.016009606.x
– volume: 82
  start-page: 171
  year: 1999
  ident: BFnbt4200_CR63
  publication-title: J. Stat. Plan. Inference
  doi: 10.1016/S0378-3758(99)00041-5
– ident: BFnbt4200_CR72
– volume: 80
  start-page: e93
  year: 2016
  ident: BFnbt4200_CR14
  publication-title: Biol. Psychiatry
  doi: 10.1016/j.biopsych.2016.02.032
– volume: 25
  start-page: 1687
  year: 2017
  ident: BFnbt4200_CR32
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
  doi: 10.1109/TNSRE.2017.2677443
– volume: 63
  start-page: 369
  year: 2008
  ident: BFnbt4200_CR21
  publication-title: Biol. Psychiatry
  doi: 10.1016/j.biopsych.2007.05.033
– volume: 7
  start-page: e32508
  year: 2012
  ident: BFnbt4200_CR15
  publication-title: PLoS One
  doi: 10.1371/journal.pone.0032508
– volume: 25
  start-page: 1725
  year: 2017
  ident: BFnbt4200_CR40
  publication-title: IEEE Trans. Neural Syst. Rehabil. Eng.
  doi: 10.1109/TNSRE.2016.2639501
– volume: 495
  start-page: 327
  year: 2013
  ident: BFnbt4200_CR23
  publication-title: Nature
  doi: 10.1038/nature11911
– volume: 110
  start-page: 145
  year: 2003
  ident: BFnbt4200_CR50
  publication-title: Psychol. Rev.
  doi: 10.1037/0033-295X.110.1.145
– volume: 12
  start-page: e1004730
  year: 2016
  ident: BFnbt4200_CR68
  publication-title: PLOS Comput. Biol.
  doi: 10.1371/journal.pcbi.1004730
– volume: 52
  start-page: 205
  year: 2006
  ident: BFnbt4200_CR39
  publication-title: Neuron
  doi: 10.1016/j.neuron.2006.09.019
– volume: 86
  start-page: 2125
  year: 2001
  ident: BFnbt4200_CR30
  publication-title: J. Neurophysiol.
  doi: 10.1152/jn.2001.86.5.2125
– volume: 7
  start-page: 63
  year: 2007
  ident: BFnbt4200_CR55
  publication-title: Expert Rev. Neurother.
  doi: 10.1586/14737175.7.1.63
– volume: 20
  start-page: 824
  year: 2017
  ident: BFnbt4200_CR74
  publication-title: Nat. Neurosci.
  doi: 10.1038/nn.4553
– volume: 11
  start-page: 240
  year: 2001
  ident: BFnbt4200_CR4
  publication-title: Curr. Opin. Neurobiol.
  doi: 10.1016/S0959-4388(00)00203-8
– volume: 12
  start-page: 036009
  year: 2015
  ident: BFnbt4200_CR70
  publication-title: J. Neural Eng.
  doi: 10.1088/1741-2560/12/3/036009
– volume: 8
  start-page: 13825
  year: 2017
  ident: BFnbt4200_CR41
  publication-title: Nat. Commun.
  doi: 10.1038/ncomms13825
– volume: 65
  start-page: 193
  year: 2003
  ident: BFnbt4200_CR9
  publication-title: Br. Med. Bull.
  doi: 10.1093/bmb/65.1.193
– volume: 367
  start-page: 153
  year: 2006
  ident: BFnbt4200_CR75
  publication-title: Lancet
  doi: 10.1016/S0140-6736(06)67964-6
– volume: 101
  start-page: 96
  year: 2014
  ident: BFnbt4200_CR24
  publication-title: Neuroimage
  doi: 10.1016/j.neuroimage.2014.06.078
– volume: 156
  start-page: 675
  year: 1999
  ident: BFnbt4200_CR10
  publication-title: Am. J. Psychiatry
  doi: 10.1176/ajp.156.5.675
– volume: 29
  start-page: 683
  year: 2008
  ident: BFnbt4200_CR29
  publication-title: Hum. Brain Mapp.
  doi: 10.1002/hbm.20426
– volume: 72
  start-page: 477
  year: 2011
  ident: BFnbt4200_CR33
  publication-title: Neuron
  doi: 10.1016/j.neuron.2011.10.020
– volume: 45
  start-page: 651
  year: 2005
  ident: BFnbt4200_CR18
  publication-title: Neuron
  doi: 10.1016/j.neuron.2005.02.014
– volume: 56
  start-page: 330
  year: 2015
  ident: BFnbt4200_CR78
  publication-title: Neurosci. Biobehav. Rev.
  doi: 10.1016/j.neubiorev.2015.07.014
– volume: 520
  start-page: 675
  year: 2015
  ident: BFnbt4200_CR79
  publication-title: Nature
  doi: 10.1038/nature14366
– ident: BFnbt4200_CR73
– volume: 1
  start-page: 93
  year: 2000
  ident: BFnbt4200_CR54
  publication-title: Epilepsy Behav.
  doi: 10.1006/ebeh.2000.0046
– volume: 356
  start-page: 1293
  year: 2001
  ident: BFnbt4200_CR62
  publication-title: Phil. Trans. R. Soc. Lond. B
  doi: 10.1098/rstb.2001.0915
– ident: BFnbt4200_CR28
  doi: 10.1007/978-1-4613-0465-4
– volume: 12
  start-page: 652
  year: 2011
  ident: BFnbt4200_CR57
  publication-title: Nat. Rev. Neurosci.
  doi: 10.1038/nrn3119
– volume: 14
  start-page: 418
  year: 2010
  ident: BFnbt4200_CR20
  publication-title: Trends Cogn. Sci.
  doi: 10.1016/j.tics.2010.06.007
– volume: 65
  start-page: 267
  year: 2009
  ident: BFnbt4200_CR17
  publication-title: Biol. Psychiatry
  doi: 10.1016/j.biopsych.2008.08.029
– volume: 7
  start-page: 12164
  year: 2017
  ident: BFnbt4200_CR2
  publication-title: Sci. Rep.
  doi: 10.1038/s41598-017-12457-6
– volume: 496
  start-page: 219
  year: 2013
  ident: BFnbt4200_CR76
  publication-title: Nature
  doi: 10.1038/nature12018
– volume: 2017
  start-page: 1660
  year: 2017
  ident: BFnbt4200_CR60
  publication-title: Conf. Proc. IEEE Eng. Med. Biol. Soc.
– volume: 14
  start-page: e2000106
  year: 2016
  ident: BFnbt4200_CR7
  publication-title: PLoS Biol.
  doi: 10.1371/journal.pbio.2000106
– volume: 1
  start-page: 63
  year: 2004
  ident: BFnbt4200_CR35
  publication-title: J. Neural Eng.
  doi: 10.1088/1741-2560/1/2/001
– volume: 25
  start-page: 16
  year: 2008
  ident: BFnbt4200_CR38
  publication-title: IEEE Signal Process. Mag.
  doi: 10.1109/MSP.2008.4408438
– volume: 10
  start-page: 530
  year: 2009
  ident: BFnbt4200_CR36
  publication-title: Nat. Rev. Neurosci.
  doi: 10.1038/nrn2653
– volume: 379
  start-page: 1045
  year: 2012
  ident: BFnbt4200_CR8
  publication-title: Lancet
  doi: 10.1016/S0140-6736(11)60602-8
– volume: 493
  start-page: 537
  year: 2013
  ident: BFnbt4200_CR80
  publication-title: Nature
  doi: 10.1038/nature11740
– volume: 5
  start-page: 210ps17
  year: 2013
  ident: BFnbt4200_CR43
  publication-title: Sci. Transl. Med.
  doi: 10.1126/scitranslmed.3007303
– volume: 164
  start-page: 1476
  year: 2007
  ident: BFnbt4200_CR56
  publication-title: Am. J. Psychiatry
  doi: 10.1176/appi.ajp.2007.07030504
– volume: 23
  start-page: 28
  year: 2017
  ident: BFnbt4200_CR5
  publication-title: Nat. Med.
  doi: 10.1038/nm.4246
– volume: 166
  start-page: 1092
  year: 2006
  ident: BFnbt4200_CR52
  publication-title: Arch. Intern. Med.
  doi: 10.1001/archinte.166.10.1092
– volume: 160
  start-page: 1233
  year: 2015
  ident: BFnbt4200_CR6
  publication-title: Cell
  doi: 10.1016/j.cell.2015.01.045
– volume: 24
  start-page: R885
  year: 2014
  ident: BFnbt4200_CR31
  publication-title: Curr. Biol.
  doi: 10.1016/j.cub.2014.07.068
– volume: 13
  start-page: 066019
  year: 2016
  ident: BFnbt4200_CR71
  publication-title: J. Neural Eng.
  doi: 10.1088/1741-2560/13/6/066019
– volume: 1996
  start-page: 38
  year: 1996
  ident: BFnbt4200_CR48
  publication-title: Br. J. Psychiatry Suppl.
  doi: 10.1192/S0007125000298395
– volume: 11
  start-page: 026002
  year: 2014
  ident: BFnbt4200_CR69
  publication-title: J. Neural Eng.
  doi: 10.1088/1741-2560/11/2/026002
– volume: 60
  start-page: 511
  year: 2008
  ident: BFnbt4200_CR64
  publication-title: Neuron
  doi: 10.1016/j.neuron.2008.10.037
– volume: 512
  start-page: 423
  year: 2014
  ident: BFnbt4200_CR37
  publication-title: Nature
  doi: 10.1038/nature13665
– volume: 10
  start-page: 1113
  year: 2009
  ident: BFnbt4200_CR58
  publication-title: J. Pain
  doi: 10.1016/j.jpain.2009.09.001
– volume: 17
  start-page: 715
  year: 2005
  ident: BFnbt4200_CR13
  publication-title: Dev. Psychopathol.
  doi: 10.1017/S0954579405050340
– volume: 111
  start-page: 18727
  year: 2014
  ident: BFnbt4200_CR25
  publication-title: Proc. Natl. Acad. Sci. USA
  doi: 10.1073/pnas.1417017112
– volume: 6
  start-page: 7759
  year: 2015
  ident: BFnbt4200_CR66
  publication-title: Nat. Commun.
  doi: 10.1038/ncomms8759
– volume: 31
  start-page: 1487
  year: 2007
  ident: BFnbt4200_CR22
  publication-title: Prog. Neuropsychopharmacol. Biol. Psychiatry
  doi: 10.1016/j.pnpbp.2007.06.030
– ident: BFnbt4200_CR49
  doi: 10.1176/appi.books.9780890425596
SSID ssj0006466
Score 2.6196976
Snippet Mood state changes are decoded using human neural activity data from electrodes implanted in seven epilepsy patients. The ability to decode mood state over...
The ability to decode mood state over time from neural activity could enable closed-loop systems to treat neuropsychiatric disorders. However, this decoding...
Mood state changes are decoded using human neural activity data from electrodes implanted in seven epilepsy patients.The ability to decode mood state over time...
SourceID proquest
gale
pubmed
crossref
springer
SourceType Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 954
SubjectTerms 631/378/116/2394
631/378/1457
631/61
639/166/985
9/30
Agriculture
Analysis
Bioinformatics
Biomedical Engineering/Biotechnology
Biomedicine
Biotechnology
Brain
Closed loops
Decoders
Decoding
Drug therapy
Dynamic models
Emotional disorders
Epilepsy
Feedback control
Human performance
Human subjects
Life Sciences
Medical imaging
Mental disorders
Modelling
Mood
Mood disorders
Nervous system diseases
Neural coding
Neuroimaging
Neurophysiology
Sparsity
Variation
Title Mood variations decoded from multi-site intracranial human brain activity
URI https://link.springer.com/article/10.1038/nbt.4200
https://www.ncbi.nlm.nih.gov/pubmed/30199076
https://www.proquest.com/docview/2157639206
https://www.proquest.com/docview/3226333167
https://www.proquest.com/docview/2101911615
Volume 36
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3db9MwED_BJhB7QFC-CmMyE2hP3tI4sZ2naUMrG1IHGgP1LYo_gpBQMpYWif-eu8Tp2mpCvOShPlnN5Xz3830CvM0i7bNUGK5GruCJjxUvvM94ZjMdCeVFUlCh8ORcnn5NPk7TaXC4NSGtsteJraJ2tSUf-QGaJjwKWRzJw6tfnKZGUXQ1jNC4C5vUuoykWk0XFy60tm2schRpSq9MZd98VuiDysz2k5jq2pbM0bpSXrJKa2HS1vqMH8HDABvZUfedH8MdXw3gXjdI8s8AtpbaCg7g_iQEzJ_A2aSuHfuNF-LOM8ecpyJ2x6ishLXZhJzekP0gJ69Fw4XyyNrBfczQ9AhGhQ80X-IpXI5PLt-f8jA9gVuZpDPupRGGzmNMkEtLHSmHgKBQ1iAoUkYaq0RpNSI6XZbW2SL12uAv1gqjpXgGG1Vd-RfA8JrmUhvbrHRZ4tB-SS8RKpnEGu9KL4ew1_Mwt6GzOA24-Jm3EW6hc-R2TtwewpsF5VXXTeMWmnf0GfIwhBMfDbkpmu_FvGnyoxQBF_UIE0PYbemoiUVFWTIdwdmnb_9B9OVihWgvEJU18boIlQn49tQca4Vye4USj6JdXe5FJw-qoMlvBPfWZVSoUgjqR4DMWSzTxpT9Vvl6TlsgEB8RNh_C804iF-xDBY2AQuHmu72I3my-ztuX__6Dr-AB4kHdlVpuw8bseu5fI-aamZ32YOFTjz_swObxyfnni7_Gfisl
linkProvider ProQuest
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9NAEB6VIl4HBOEVKLBUVD2ZOl57vT4gVAEhoU2RIKDeVt6HERKyC05A_VH8R2a8dppEFeLSSw7eTyNnPLMzu_MCeJ6F0mUJ10E6sHkQuygNcueyIDOZDHnqeJxTofDkSIw-x--Pk-MN-NPVwlBaZbcnNhu1rQzdke-haUJVyKJQvDr5EdDUKIqudiM0vFgcuNPfeGSrX47f4PfdiaLh2-nrUdBOFQiMiJNZ4ITmmuQ0IldEChmmFg1lnhqNzkKqhTYpL4xET0cWhbEmT5zU-MQYrqXgSPYSXI45YqkwffhusfELHxodhJKyORPR9brlcq_UsxdxRGV0S9Zv3QYsGcG1qGxj7Ia34GbrpbJ9L1a3YcOVPbji51ae9uDGUhfDHlydtPH5OzCeVJVlv_D87S8CmXVUM28ZVbGwJnkxIIayb3SnbNBOovizZk4g0zSsglGdBY2zuAvTi2DrPdgsq9I9AIanQpuYyGSFzWKL5lI4gZ6Zjo12tnCiD7sdD5VpG5nTPI3vqgmoc6mQ24q43YdnC-SJb95xDmaHPoNqZ37iT023IvXXfF7Xaj9B_45akvE-bDc46plRUlKOB4w_fPkP0KePK6DdFlRUxOu8LYTAf0-9uFaQWytI1HyzutyJjmp3nlqd6cm5y7h_C86p_QEyZ7FMhCnZrnTVnEig3z-go0Af7nuJXLAP7QH6LykS3-5E9Iz4Om8f_vsFn8K10XRyqA7HRweP4Dq6opFPk9yCzdnPuXuM7t5MP2mUjIG6YKX-C2wRZYI
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtR3LbtNAcFRSUcEBQXgFCiwVVU8mjtderw8IFdqooSRUpUW9Wd6HERKyC05A_TT-jhl7nSZRhbj0koN3NHLG89x5AbxKfGmTiCsvHpjMC20Qe5m1iZfoRPo8tjzMqFF4PBEHp-GHs-hsDf60vTBUVtnqxFpRm1LTHXkfTROKQhL4op-7soijveHb8x8ebZCiTGu7TqNhkUN78RvDt-rNaA-_9XYQDPdP3h94bsOAp0UYTT0rFFfEswG5JVJIPzZoNLNYK3QcYiWUjnmuJXo9Ms-10VlkpcInWnMlBUe0N2A9pqCoA-vv9idHx3MzIJpE6cCXVNsZiXbyLZf9Qk1fhwE11S3YwlWLsGASV3K0tekb3oU7zmdluw2T3YM1W3ThZrPF8qILtxdmGnZhY-yy9fdhNC5Lw35hNN5cCzJjqYPeMOppYXUpo0ckZd_ohlmj1URhYPXWQKZodQWjrgtabvEATq6DsA-hU5SFfQwMY0QT6UAnuUlCg8ZTWIF-mgq1sia3ogc7LQ1T7caa03aN72mdXucyRWqnRO0evJxDnjejPK6A2abPkLoNoPhT0R1J9TWbVVW6G6G3RwPKeA-2ajiaoFEQLzYAo09f_gPo8_ES0I4DykuidebaIvDf02SuJcjNJUjUA3r5uGWd1OmhKr2UmiuPUZsLzmkYAhJnfkyIqfSusOWMUGAUMKDAoAePGo6ckw-tA3ozMSLfaln0EvkqbZ_8-wVfwAYKdPpxNDl8CrfQL5VNy-cmdKY_Z_YZ-n5T9dxJGYP0muX6Ly6baxs
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Mood+variations+decoded+from+multi-site+intracranial+human+brain+activity&rft.jtitle=Nature+biotechnology&rft.au=Sani%2C+Omid+G&rft.au=Yang%2C+Yuxiao&rft.au=Lee%2C+Morgan+B&rft.au=Dawes%2C+Heather+E&rft.date=2018-11-01&rft.pub=Nature+Publishing+Group&rft.issn=1087-0156&rft.eissn=1546-1696&rft.volume=36&rft.issue=10&rft.spage=954&rft_id=info:doi/10.1038%2Fnbt.4200&rft.externalDBID=HAS_PDF_LINK
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1087-0156&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1087-0156&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1087-0156&client=summon