Network-Targeted, Multi-site Direct Cortical Stimulation Enhances Working Memory by Modulating Phase Lag of Low-Frequency Oscillations

Sankaraleengam Alagapan, Justin Riddle, Wei Angel Huang, Eldad Hadar, Hae Won Shin, Flavio Fröhlich, Sankaraleengam Alagapan, Justin Riddle, Wei Angel Huang, Eldad Hadar, Hae Won Shin, Flavio Fröhlich

Abstract

Working memory is mediated by the coordinated activation of frontal and parietal cortices occurring in the theta and alpha frequency ranges. Here, we test whether electrically stimulating frontal and parietal regions at the frequency of interaction is effective in modulating working memory. We identify working memory nodes that are functionally connected in theta and alpha frequency bands and intracranially stimulate both nodes simultaneously in participants performing working memory tasks. We find that in-phase stimulation results in improvements in performance compared to sham stimulation. In addition, in-phase stimulation results in decreased phase lag between regions within working memory network, while anti-phase stimulation results in increased phase lag, suggesting that shorter phase lag in oscillatory connectivity may lead to better performance. The results support the idea that phase lag may play a key role in information transmission across brain regions. Thus, brain stimulation strategies to improve cognition may require targeting multiple nodes of brain networks.

Keywords: brain stimulation; cortical oscillations; direct cortical stimulation; human neuroscience; intracranial eeg; phase lag; working memory.

Conflict of interest statement

DECLARATION OF INTERESTS

F.F. is the lead inventor of intellectual property filed on the topics of noninvasive brain stimulation by UNC. F.F. is the founder, chief scientific officer (CSO), and majority owner of Pulvinar Neuro LLC, which played no role in this research. The other authors declare no competing interests.

Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.

Figures

Figure 1.. Schematic of Network-Targeted Stimulation
Figure 1.. Schematic of Network-Targeted Stimulation
(A) Intracranial EEG data from implanted electrodes, collected when participants performed WM tasks, are processed to identify functionally connected regions that are then targeted with direct cortical stimulation. (B) Sternberg WM task depicting the different epochs and timing of the components of each epoch. (C) The stimulation paradigms used in the study. Each vertical red line denotes a biphasic pulse. In-phase stimulation consists of pulses applied simultaneously to functionally connected regions without any phase offset (time delay). Anti-phase stimulation consists of pulses applied with a phase offset of 180° (time delay of half the inter-stimulus interval Ts). Dotted lines are provided for visual guidance.
Figure 2.. Functional Connectivity of Stimulation Electrodes
Figure 2.. Functional Connectivity of Stimulation Electrodes
(A) Mean dWPLI for the stimulation electrodes for the different cognitive loads and the different epochs. Shaded regions denote the jackknife estimate of SD. (B) The anatomical locations of the identified stimulation electrodes for the three participants. The beige-shaded regions denote WM regions identified from meta-analyses of functional neuroimaging studies. See also Figure S1.
Figure 3.. Effect of Network-Targeted Stimulation on…
Figure 3.. Effect of Network-Targeted Stimulation on WM Performance
In-phase stimulation increased accuracy relative to sham (top). Stimulation did not affect reaction time (bottom). See also Figure S2.
Figure 4.. Effect of Network-Targeted Stimulation on…
Figure 4.. Effect of Network-Targeted Stimulation on WM Network
(A) Pairwise difference of dWPLI between stimulation conditions. In-phase versus sham and anti-phase versus sham were significantly greater than 0. (B) Pairwise difference of coherence between stimulation conditions. All three pairwise differences were significantly greater than 0. (C) Circular histogram denoting the pairwise differences in phase lag across the three comparisons for the three participants. The black line denotes the mean phase lag difference for each comparison. *p

References

    1. Accolla EA, HerrojoRuiz M, Horn A, Schneider GH, Schmitz-Hübsch T, Draganski B, and Kühn AA (2016). Brain networks modulated by subthalamic nucleus deep brain stimulation. Brain 139, 2503–2515.
    1. Alagapan S, Schmidt SL, Lefebvre J, Hadar E, Shin HW, and Fröhlich F (2016). Modulation of Cortical Oscillations by Low-Frequency Direct Cortical Stimulation Is State-Dependent. PLoS Biol. 14, e1002424.
    1. Alagapan S, Lustenberger C, Hadar E, Shin HW, and Fröhlich F (2019a). Low-frequency direct cortical stimulation of left superior frontal gyrus enhances working memory performance. Neuroimage 184, 697–706.
    1. Alagapan S, Shin HW, Fröhlich F, and Wu HT (2019b). Diffusion geometry approach to efficiently remove electrical stimulation artifacts in intracranial electroencephalography. J. Neural Eng 16, 036010.
    1. Albouy P, Weiss A, Baillet S, and Zatorre RJ (2017). Selective Entrainment of Theta Oscillations in the Dorsal Stream Causally Enhances Auditory Working Memory Performance. Neuron 94, 193–206.e5.
    1. Alekseichuk I, Turi Z, Amador de Lara G, Antal A, and Paulus W (2016). Spatial Working Memory in Humans Depends on Theta and High Gamma Synchronization in the Prefrontal Cortex. Curr. Biol 26, 1513–1521.
    1. Allen DN, Randall C, Bello D, Armstrong C, Frantom L, Cross C, and Kinney J (2010). Are working memory deficits in bipolar disorder markers for psychosis? Neuropsychology 24, 244–254.
    1. Axmacher N, Schmitz DP, Wagner T, Elger CE, and Fell J (2008). Interactions between medial temporal lobe, prefrontal cortex, and inferior temporal regions during visual working memory: a combined intracranial EEG and functional magnetic resonance imaging study. J. Neurosci 28, 7304–7312.
    1. Baddeley AD, Bressi S, Della Sala S, Logie R, and Spinnler H (1991). The decline of working memory in Alzheimer’s disease. A longitudinal study. Brain 114, 2521–2542.
    1. Bassett DS, and Sporns O (2017). Network neuroscience. Nat. Neurosci 20, 353–364.
    1. Bassett DS, Wymbs NF, Porter MA, Mucha PJ, Carlson JM, and Grafton ST (2011). Dynamic reconfiguration of human brain networks during learning. Proceedings of the National Academy of Sciences 108, 7641–7646.
    1. Benjamini Y, and Hochberg Y (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Statist. Soc 57, 289–300.
    1. Bennett CM, Wolford GL, and Miller MB (2009). The principled control of false positives in neuroimaging. Soc. Cogn. Affect. Neurosci 4, 417–422.
    1. Brainard DH (1997). The Psychophysics Toolbox. Spat. Vis 10, 433–436.
    1. Brett M, Anton JL, Valabregue R, and Poline J-B (2002). Region of interest analysis using an SPM toolbox. Presented at the 8th International Conference on Functional Mapping of the Human Brain, June 2–6, 2002, Sendai, Japan Neuroimage 13, 210–217.
    1. Butson CR, and McIntyre CC (2005). Tissue and electrode capacitance reduce neural activation volumes during deep brain stimulation. Clin. Neurophysiol 116, 2490–2500.
    1. Corbin L, and Marquer J (2009). Individual differences in Sternberg’s memory scanning task. Acta Psychol. (Amst.) 131, 153–162.
    1. Corbin L, and Marquer J (2013). Is Sternberg’s Memory Scanning Task Really a Short-Term Memory Task? Swiss J. Psychol 72, 181–196.
    1. Daume J, Gruber T, Engel AK, and Friese U (2017). Phase-Amplitude Coupling and Long-Range Phase Synchronization Reveal Frontotemporal Interactions during Visual Working Memory. J. Neurosci 37, 313–322.
    1. Delorme A, and Makeig S (2004). EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21.
    1. Dima D, Jogia J, and Frangou S (2014). Dynamic causal modeling of load-dependent modulation of effective connectivity within the verbal working memory network. Hum. Brain Mapp 35, 3025–3035.
    1. Esslinger C, Schüler N, Sauer C, Gass D, Mier D, Braun U, Ochs E, Schulze TG, Rietschel M, Kirsch P, and Meyer-Lindenberg A (2014). Induction and quantification of prefrontal cortical network plasticity using 5 Hz rTMS and fMRI. Hum. Brain Mapp 35, 140–151.
    1. Ezzyat Y, Wanda PA, Levy DF, Kadel A, Aka A, Pedisich I, Sperling MR, Sharan AD, Lega BC, Burks A, et al. (2018). Closed-loop stimulation of temporal cortex rescues functional networks and improves memory. Nat. Commun 9, 365.
    1. Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, et al. (2012). 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn. Reson. Imaging 30, 1323–1341.
    1. Fell J, and Axmacher N (2011). The role of phase synchronization in memory processes. Nat. Rev. Neurosci 12, 105–118.
    1. Fell J, Staresina BP, Do Lam AT, Widman G, Helmstaedter C, Elger CE, and Axmacher N (2013). Memory modulation by weak synchronous deep brain stimulation: a pilot study. Brain Stimul. 6, 270–273.
    1. Fonov V, Evans AC, Botteron K, Almli CR, McKinstry RC, and Collins DL; Brain Development Cooperative Group (2011). Unbiased average age-appropriate atlases for pediatric studies. Neuroimage 54, 313–327.
    1. Fries P (2015). Rhythms for Cognition: Communication through Coherence. Neuron 88, 220–235.
    1. Fröhlich F (2015). Experiments and models of cortical oscillations as a target for noninvasive brain stimulation. Prog. Brain Res. 222, 41–73.
    1. Gagnon G, Blanchet S, Grondin S, and Schneider C (2010). Paired-pulse transcranial magnetic stimulation over the dorsolateral prefrontal cortex interferes with episodic encoding and retrieval for both verbal and non-verbal materials. Brain Res. 1344, 148–158.
    1. Genovese CR, Lazar NA, and Nichols T (2002). Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 15, 870–878.
    1. Gevins A, Smith ME, McEvoy L, and Yu D (1997). High-resolution EEG mapping of cortical activation related to working memory: effects of task difficulty, type of processing, and practice. Cereb. Cortex 7, 374–385.
    1. Hanslmayr S, Axmacher N, and Inman CS (2019). Modulating Human Memory via Entrainment of Brain Oscillations. Trends Neurosci. 42, 485–499.
    1. Harding IH, Yücel M, Harrison BJ, Pantelis C, and Breakspear M (2015). Effective connectivity within the frontoparietal control network differentiates cognitive control and working memory. Neuroimage 106, 144–153.
    1. Hoy KE, Bailey N, Michael M, Fitzgibbon B, Rogasch NC, Saeki T, and Fitzgerald PB (2016). Enhancement of Working Memory and Task-Related Oscillatory Activity Following Intermittent Theta Burst Stimulation in Healthy Controls. Cereb. Cortex 26, 4563–4573.
    1. Hsieh LT, and Ranganath C (2014). Frontal midline theta oscillations during working memory maintenance and episodic encoding and retrieval. Neuroimage 85, 721–729.
    1. Jaušovec N, Jaušovec K, and Pahor A (2014). The influence of theta transcranial alternating current stimulation (tACS) on working memory storage and processing functions. Acta Psychol. (Amst.) 146, 1–6.
    1. Jensen O, and Mazaheri A (2010). Shaping functional architecture by oscillatory alpha activity: gating by inhibition. Front. Hum. Neurosci 4, 186.
    1. Jensen O, and Tesche CD (2002). Frontal theta activity in humans increases with memory load in a working memory task. Eur. J. Neurosci 15, 1395–1399.
    1. Jensen O, Gelfand J, Kounios J, and Lisman JE (2002). Oscillations in the alpha band (9–12 Hz) increase with memory load during retention in a short-term memory task. Cereb. Cortex 12, 877–882.
    1. Kawasaki M, Kitajo K, and Yamaguchi Y (2010). Dynamic links between theta executive functions and alpha storage buffers in auditory and visual working memory. Eur. J. Neurosci 31, 1683–1689.
    1. Kerren C, Linde-Domingo J, Hanslmayr S, and Wimber M (2018). An Optimal Oscillatory Phase for Pattern Reactivation during Memory Retrieval. Curr. Biol. 28, 3383–3392.e6.
    1. Kim K, Schedlbauer A, Rollo M, Karunakaran S, Ekstrom AD, and Tandon N (2018). Network-based brain stimulation selectively impairs spatial retrieval. Brain Stimul. 11, 213–221.
    1. Klimesch W (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res. Brain Res. Rev 29, 169–195.
    1. Klimesch W, Freunberger R, Sauseng P, and Gruber W (2008). A short review of slow phase synchronization and memory: evidence for control processes in different memory systems? Brain Res. 1235, 31–44.
    1. Kopell NJ, Gritton HJ, Whittington MA, and Kramer MA (2014). Beyond the connectome: the dynome. Neuron 83, 1319–1328.
    1. Kucewicz MT, Berry BM, Miller LR, Khadjevand F, Ezzyat Y, Stein JM, Kremen V, Brinkmann BH, Wanda P, Sperling MR, et al. (2018). Evidence for verbal memory enhancement with electrical brain stimulation in the lateral temporal cortex. Brain 141, 971–978.
    1. Kuznetsova A, Brockhoff PB, and Christensen RHB (2017). lmerTest Package: Tests in Linear Mixed Effects Models. J. Stat. Softw 82, 1–26.
    1. Lancaster JL, Woldorff MG, Parsons LM, Liotti M, Freitas CS, Rainey L, Kochunov PV, Nickerson D, Mikiten SA, and Fox PT (2000). Automated Talairach atlas labels for functional brain mapping. Hum. Brain Mapp 10, 120–131.
    1. Lee J, and Park S (2005). Working memory impairments in schizophrenia: a meta-analysis. J. Abnorm. Psychol 114, 599–611.
    1. Lee TW, Girolami M, Bell AJ, and Sejnowski TJ (2000). A unifying information-theoretic framework for independent component analysis. Comput. Math. Appl 39, 1–21.
    1. Lempka SF, and McIntyre CC (2013). Theoretical analysis of the local field potential in deep brain stimulation applications. PLoS One 8, e59839.
    1. Lobier M, Palva JM, and Palva S (2018). High-alpha band synchronization across frontal, parietal and visual cortex mediates behavioral and neuronal effects of visuospatial attention. Neuroimage 165, 222–237.
    1. Luber B, Kinnunen LH, Rakitin BC, Ellsasser R, Stern Y, and Lisanby SH (2007). Facilitation of performance in a working memory task with rTMS stimulation of the precuneus: frequency- and time-dependent effects. Brain Res. 1128, 120–129.
    1. Lund U, and Agostinelli C (2017). R package ‘circular’: Circular Statistics (version 0.4–93). .
    1. Ma L, Steinberg JL, Hasan KM, Narayana PA, Kramer LA, and Moeller FG (2012). Working memory load modulation of parieto-frontal connections: evidence from dynamic causal modeling. Hum. Brain Mapp 33, 1850–1867.
    1. Meltzer JA, Zaveri HP, Goncharova II, Distasio MM, Papademetris X, Spencer SS, Spencer DD, and Constable RT (2008). Effects of working memory load on oscillatory power in human intracranial EEG. Cereb. Cortex 18, 1843–1855.
    1. Mottaghy FM (2006). Interfering with working memory in humans. Neuroscience 139, 85–90.
    1. Nee DE, Brown JW, Askren MK, Berman MG, Demiralp E, Krawitz A, and Jonides J (2013). A meta-analysis of executive components of working memory. Cereb. Cortex 23, 264–282.
    1. Nolte G, Bai O, Wheaton L, Mari Z, Vorbach S, and Hallett M (2004). Identifying true brain interaction from EEG data using the imaginary part of coherency. Clin. Neurophysiol 115, 2292–2307.
    1. Ojemann GA, Ojemann J, and Ramsey NF (2013). Relation between functional magnetic resonance imaging (fMRI) and single neuron, local field potential (LFP) and electrocorticography (ECoG) activity in human cortex. Front. Hum. Neurosci 7, 34.
    1. Oostenveld R, Fries P, Maris E, and Schoffelen JM (2011). FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci 2011, 156869.
    1. Osaka N, Otsuka Y, Hirose N, Ikeda T, Mima T, Fukuyama H, and Osaka M (2007). Transcranial magnetic stimulation (TMS) applied to left dorsolateral prefrontal cortex disrupts verbal working memory performance in humans. Neurosci. Lett 418, 232–235.
    1. Owen AM, McMillan KM, Laird AR, and Bullmore E (2005). N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies. Hum. Brain Mapp 25, 46–59.
    1. Palva JM, Monto S, Kulashekhar S, and Palva S (2010). Neuronal synchrony reveals working memory networks and predicts individual memory capacity. Proc. Natl. Acad. Sci. USA 107, 7580–7585.
    1. Payne L, and Kounios J (2009). Coherent oscillatory networks supporting short-term memory retention. Brain Res. 1247, 126–132.
    1. Penny WD, Friston KJ, Ashburner JT, Kiebel SJ, and Nichols TE (2011). Statistical Parametric Mapping: The Analysis of Functional Brain Images (Academic Press; ).
    1. Polanía R, Nitsche MA, Korman C, Batsikadze G, and Paulus W (2012). The importance of timing in segregated theta phase-coupling for cognitive performance. Curr. Biol 22, 1314–1318.
    1. Postle BR, Ferrarelli F, Hamidi M, Feredoes E, Massimini M, Peterson M, Alexander A, and Tononi G (2006). Repetitive transcranial magnetic stimulation dissociates working memory manipulation from retention functions in the prefrontal, but not posterior parietal, cortex. J. Cogn. Neurosci 18, 1712–1722.
    1. Quentin R, Elkin Frankston S, Vernet M, Toba MN, Bartolomeo P, Chanes L, and Valero-Cabré A (2016). Visual Contrast Sensitivity Improvement by Right Frontal High-Beta Activity Is Mediated by Contrast Gain Mechanisms and Influenced by Fronto-Parietal White Matter Microstructure. Cereb. Cortex 26, 2381–2390.
    1. Raghavachari S, Kahana MJ, Rizzuto DS, Caplan JB, Kirschen MP, Bourgeois B, Madsen JR, and Lisman JE (2001). Gating of human theta oscillations by a working memory task. J. Neurosci 21, 3175–3183.
    1. Riddle J, Hwang K, Cellier D, Dhanani S, and D’Esposito M (2019). Causal Evidence for the Role of Neuronal Oscillations in Top-Down and Bottom-Up Attention. J. Cogn. Neurosci 31, 768–779.
    1. Rottschy C, Langner R, Dogan I, Reetz K, Laird AR, Schulz JB, Fox PT, and Eickhoff SB (2012). Modelling neural correlates of working memory: a coordinate-based meta-analysis. Neuroimage 60, 830–846.
    1. Roux F, and Uhlhaas PJ (2014). Working memory and neural oscillations: α-γ versus θ-γ codes for distinct WM information? Trends Cogn. Sci 18, 16–25.
    1. Sarnthein J, Petsche H, Rappelsberger P, Shaw GL, and von Stein A (1998). Synchronization between prefrontal and posterior association cortex during human working memory. Proc. Natl. Acad. Sci. USA 95, 7092–7096.
    1. Sauseng P, Klimesch W, Doppelmayr M, Pecherstorfer T, Freunberger R, and Hanslmayr S (2005a). EEG alpha synchronization and functional coupling during top-down processing in a working memory task. Hum. Brain Mapp 26, 148–155.
    1. Sauseng P, Klimesch W, Schabus M, and Doppelmayr M (2005b). Fronto-parietal EEG coherence in theta and upper alpha reflect central executive functions of working memory. Int. J. Psychophysiol 57, 97–103.
    1. Sauseng P, Klimesch W, Heise KF, Gruber WR, Holz E, Karim AA, Glennon M, Gerloff C, Birbaumer N, and Hummel FC (2009). Brain oscillatory substrates of visual short-term memory capacity. Curr. Biol 19, 1846–1852.
    1. Sauseng P, Griesmayr B, Freunberger R, and Klimesch W (2010). Control mechanisms in working memory: a possible function of EEG theta oscillations. Neurosci. Biobehav. Rev 34, 1015–1022.
    1. Schack B, Klimesch W, and Sauseng P (2005). Phase synchronization between theta and upper alpha oscillations in a working memory task. Int. J. Psychophysiol 57, 105–114.
    1. Shine JM, Bissett PG, Bell PT, Koyejo O, Balsters JH, Gorgolewski KJ, Moodie CA, and Poldrack RA (2016). The dynamics of functional brain networks: integrated network states during cognitive task performance. Neuron 92, 544–554.
    1. Siegle JH, and Wilson MA (2014). Enhancement of encoding and retrieval functions through theta phase-specific manipulation of hippocampus. eLife 3, e03061.
    1. Stam CJ, Nolte G, and Daffertshofer A (2007). Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum. Brain Mapp 28, 1178–1193.
    1. Suthana N, Haneef Z, Stern J, Mukamel R, Behnke E, Knowlton B, and Fried I (2012). Memory enhancement and deep-brain stimulation of the entorhinal area. N. Engl. J. Med 366, 502–510.
    1. Thut G, Schyns PG, and Gross J (2011). Entrainment of perceptually relevant brain oscillations by non-invasive rhythmic stimulation of the human brain. Front. Psychol 2, 170.
    1. Vinck M, Oostenveld R, van Wingerden M, Battaglia F, and Pennartz CM (2011). An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. Neuroimage 55, 1548–1565.
    1. Violante IR, Li LM, Carmichael DW, Lorenz R, Leech R, Hampshire A, Rothwell JC, and Sharp DJ (2017). Externally induced frontoparietal synchronization modulates network dynamics and enhances working memory performance. eLife 6, e22001.
    1. Wager TD, and Smith EE (2003). Neuroimaging studies of working memory: a meta-analysis. Cogn. Affect. Behav. Neurosci 3, 255–274.
    1. Winawer J, and Parvizi J (2016). Linking Electrical Stimulation of Human Primary Visual Cortex, Size of Affected Cortical Area, Neuronal Responses, and Subjective Experience. Neuron 92, 1213–1219.
    1. Yarkoni T, Poldrack RA, Nichols TE, Van Essen DC, and Wager TD (2011). Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods 8, 665–670.
    1. Zalesky A, Fornito A, and Bullmore ET (2010). Network-based statistic: identifying differences in brain networks. Neuroimage 53, 1197–1207.
    1. Zanos S, Rembado I, Chen D, and Fetz EE (2018). Phase-Locked Stimulation during Cortical Beta Oscillations Produces Bidirectional Synaptic Plasticity in Awake Monkeys. Curr. Biol 28, 2515–2526.e4.
    1. Zanto TP, Rubens MT, Thangavel A, and Gazzaley A (2011). Causal role of the prefrontal cortex in top-down modulation of visual processing and working memory. Nat. Neurosci 14, 656–661.
    1. Zoefel B, Archer-Boyd A, and Davis MH (2018). Phase Entrainment of Brain Oscillations Causally Modulates Neural Responses to Intelligible Speech. Curr. Biol 28, 401–408.e55.

Source: PubMed

Patrocinadores y Colaboradores

Condiciones médicas

Intervenciones de drogas

3
Suscribir