神經回饋訓練讓使用者能夠學習控制神經活動以促自我調控特定的腦區，進而影響該腦區運作的行為或是相關病理路徑。本文專門探討使用腦電訊號當回饋訊息的神經回饋訓練，因為這是一種安全、廉價且使用廣泛的技術。該技術早已成功應用於臨床實踐和功能提升。神經回饋訓練後觀察到的這些臨床改善或是功能提升，顯示訓練所導致的一連串神經生理反應可能是一種內生性的神經刺激事件。大部分的神經回饋訓練包含每週數次的重複訓練，使得效果可以隨著時間累積。這樣的效果，被認為是神經回饋訓練對行為或功能相關的靜習態網路長期調節的自我調控或自我治療結果。在各種神經回饋訓練中，阿爾法腦波神經回饋訓練一直是相關研究的焦點。主要是因為阿爾法腦波普遍在人腦上被觀察到，與認知功能相關，以及之前的研究發現可以經由神經回饋訓練獲得自主控制。在我們已經發表的報告探討了關於訓練前的閉眼靜息態阿爾法腦波可專一預測神經回饋訓練的學習表現。這個報告顯示，靜息態的神經生理特徵是學習發阿爾法腦波的基礎。而這個基礎目前常用靜息態功能連結來表示。理論上，特定的靜息態功能連結可以被阿爾法腦波神經回饋訓練調節。但是目前的研究沒有提供清楚完整的證據告訴我們這個調節是如何操作。為此，我們需要回答以下兩個研究問題：阿爾法腦波神經回饋訓練是否是在特定腦區自主調控活性？是否進而調節相關的靜習態網路的功能性連結？了解與澄清這些研究問題對於神經回饋這個技術的科學發展至關重要。為了回答以上問題以了解阿爾法腦波神經回饋訓練的神經基礎，我們進行了以下兩個實驗：（1）使用ECD分析找出腦電訊號的來源以及相關的fMRI參數的變動；（2）使用fMRI分析找出活化腦區以及調查相關的靜習態功能性連結。
第一個實驗結果顯示ECD的位置主要是集中在後扣帶皮層，楔前葉，和內側顳葉。這些位置跟關鍵的靜習態網路–大腦預設網路重疊。第二個實驗結果找到活化的腦區位於右側後扣帶皮層和兩側的視覺皮層，並且與阿爾法腦波呈現反向相關關係以及時間上延遲的活動模式，特別是在P4這個電極位置的腦波活動。這個結果指出腦電波與fMRI訊號的反應分別代表了使用者自主控制阿爾法腦波的這個動作在電神經生理上的高頻反應與血流動力學上低頻反應。分析訓練前後靜習態功能連結的變化發現右背側後扣帶皮層與右內側顳葉的功能連結以及左側視覺皮層與額葉眼動區的功能連結有顯著改變。這些連結剛好各別位於大腦預設網路與視覺注意力網路。
這些研究發現對於神經回饋訓練的運作提供了神經生理上的證據。並解釋了阿爾法腦波的自主調控的電訊號來源位置，活化的腦區，進而調節控制注意力與認知功能的兩個關鍵靜習態網路，為改善注意力與相關認知功能的應用提供了其可能性的神經基礎。有了這些證據與相關了解，本研究可以協助相關研究人員進一步了解與預測訓練的有效性，以及據此採取適當的訓練策略以提高訓練成效。

Neurofeedback training (NFT) enables users to learn self-control of measured neural activity in real time to facilitate self-regulation of the putative substrates that underline a specific behavior or pathology. Electroencephalogram (EEG) NFT is preferred in this paper as a safe, inexpensive, and accessible technology. This technology has long been successfully used in clinical practice and performance enhancement. The performance changes that have been observed to result from self-manipulation of neural activation indicate that the neurophysiological consequences of NFT may be considered to be a form of endogenous neural stimulation. Most NFT involves multiple sessions repeated on at least a weekly basis, whose effects generally accumulate over time, reputedly as a result of long-term modulation in behaviorally relevant resting-state networks (RSNs). Alpha (8–12 Hz) activity has been proposed as a target for NFT based on its prevalence in the human EEG, its critical role in cognition function, and previous findings that its amplitude can be readily regulated after NFT. Our published report reveals that learning performance of alpha upregulation NFT can be exclusively predicted by eyes-closed resting state alpha activity before training, suggesting that neurophysiological features in resting state underline the generation of neural activity pattern by NFT of alpha upregulation. Assuming that alpha-upregulation NFT shapes neurophysiological features in resting, we wonder whether it modulates resting-state functional connectivity with neural regulation at specific regions of interests (ROIs). Understanding and clarifying how alpha NFT induces neurophysiological consequences and thus modulates RSNs is critical for the science of neurofeedback. However, there is no clear and convincing evidence for this issue. Specifically, there are two important questions we propose in this study: (1) what brain regions would be involved in alpha EEG NFT in terms of EEG source and BOLD fluctuations, respectively, (2) whether alpha NFT would modulate FC regarding these ROIs in either activation state or resting state. We conducted two experiments for answering these questions: (1) explore ROIs of EEG sources by equivalent current dipole (ECD) analysis and related changes of fMRI parameters in alpha upregulation; (2) investigate ROIs of blood-oxygen-level dependent (BOLD) responses and related FC modulation in resting state.
The result of the experiment 1 showed that ECDs were clustered mainly in the posterior cingulate cortex (PCC), precuneus (PCS), and middle temporal cortex (MTC) and that fMRI state dynamics were related to these dipole locations, particular at the PCC. The results of the experiment 2 detected significant downregulation at the right dorsal posterior cingulate cortex (rdPCC), right ventral posterior cingulate cortex (rvPCC), right visual cortex (rVC), and left visual cortex (lVC) as ROIs which exhibited anti-correlation with alpha activity particularly around the electrode P4 site. Cross-correlogram of a lag pattern with these BOLD responses and alpha activity suggests that EEG alpha activity and fMRI BOLD responses respectively present high- and low-frequency components of the same underlying cortical activity of self-regulation during the training mode of NFT. Resting-state functional connectivity (RSFC) analysis with these ROIs revealed that compared to random-control feedback, alpha feedback induced modulation in connectivity between the rdPCC and the right middle temporal cortex (rMTC) and between the lVC and frontal eye fields (FEF) three days after the termination of NFT. Crucially, we observed a causal dependence between NFT of alpha regulation and its subsequent change at resting state, not exhibited in the Ctrl group, indicating that two subsystems, i.e., the default mode network (DMN for the rdPCC and rMTC) and the visual attention network (VAN, for the lVC and FEF), were modulated by alpha upregulation NFT.
These findings provide evidence for linking previously missing answers in understanding how alpha NFT facilitates self-regulation of the putative neural substrates and thus modules resting-state FC. Furthermore, these findings would assist researchers in obtaining insight into the training efficacy of individuals and then adapting an efficient strategy in NFT success.

Aboitiz, F., Ossandon, T., Zamorano, F., Palma, B., Carrasco, X., 2014. Irrelevant stimulus processing in ADHD: Catecholamine dynamics and attentional networks.

Acar, Z.A., Makeig, S., 2010. Neuroelectromagnetic forward head modeling toolbox. Journal of neuroscience methods 190, 258-270.

Alitto, H.J., Dan, Y., 2010. Function of inhibition in visual cortical processing. Current Opinion in Neurobiology 20, 340-346.

Allen, P.J., Josephs, O., Turner, R., 2000. A method for removing imaging artifact from continuous EEG recorded during functional MRI. Neuroimage 12, 230-239.

Andrews-Hanna, J.R., Smallwood, J., Spreng, R.N., 2014. The default network and self-generated thought: component processes, dynamic control, and clinical relevance. Annals of the New York Academy of Sciences 1316, 29-52.

Arns, M., Heinrich, H., Strehl, U., 2014. Evaluation of neurofeedback in ADHD: The long and winding road. Biological psychology 95, 108-115.

Artoni, F., Delorme, A., Makeig, S., 2018. Applying dimension reduction to EEG data by Principal Component Analysis reduces the quality of its subsequent Independent Component decomposition. Neuroimage 175, 176-187.

Assaf, M., Jagannathan, K., Calhoun, V.D., Miller, L., Stevens, M.C., Sahl, R., O'Boyle, J.G., Schultz, R.T., Pearlson, G.D., 2010. Abnormal functional connectivity of default mode sub-networks in autism spectrum disorder patients. Neuroimage 53, 247-256.

Babiloni, C., Vecchio, F., Bultrini, A., Romani, G.L., Rossini, P.M., 2006. Pre- and poststimulus alpha rhythms are related to conscious visual perception: A high-resolution EEG study. Cerebral cortex 16, 1690-1700.

Baria, A.T., Baliki, M.N., Parrish, T., Apkarian, A.V., 2011. Anatomical and functional assemblies of brain BOLD oscillations. J Neurosci 31, 7910-7919.

Başar, E., 2012. A review of alpha activity in integrative brain function: Fundamental physiology, sensory coding, cognition and pathology. International Journal of Psychophysiology 86, 1-24.

Bauer, C.C.C., Okano, K., Gosh, S.S., Lee, Y.J., Melero, H., Angeles, C.L., Nestor, P.G., Del Re, E.C., Northoff, G., Niznikiewicz, M.A., Whitfield-Gabrieli, S., 2020. Real-time fMRI neurofeedback reduces auditory hallucinations and modulates resting state connectivity of involved brain regions: Part 2: Default mode network -preliminary evidence. Psychiatry Res 284, 112770.

Bays, B.C., Visscher, K.M., Le Dantec, C.C., Seitz, A.R., 2015. Alpha-band EEG activity in perceptual learning. J Vis 15, 7.

Becker, R., Reinacher, M., Freyer, F., Villringer, A., Ritter, P., 2011. How Ongoing Neuronal Oscillations Account for Evoked fMRI Variability. The Journal of Neuroscience 31, 11016-11027.

Ben-Simon, E., Podlipsky, I., Arieli, A., Zhdanov, A., Hendler, T., 2008. Never Resting Brain: Simultaneous Representation of Two Alpha Related Processes in Humans. PLoS One 3, e3984.

Berger, H., 1929. Über das elektrenkephalogramm des menschen. European Archives of Psychiatry and Clinical Neuroscience 87, 527-570.

Bird, C.M., Burgess, N., 2008. The hippocampus and memory: insights from spatial processing. Nature reviews neuroscience 9, 182-194.

Brandmeyer, T., Delorme, A., 2020. Closed-Loop Frontal Midlineθ Neurofeedback: A Novel Approach for Training Focused-Attention Meditation. Frontiers in Human Neuroscience 14, 246-246.

Brewer, J.A., Garrison, K.A., Whitfield-Gabrieli, S., 2013. What about the "Self" is Processed in the Posterior Cingulate Cortex? Front Hum Neurosci 7, 647.

Buxton, R.B., 2013. The physics of functional magnetic resonance imaging (fMRI). Reports on progress in physics. Physical Society (Great Britain) 76, 096601-096601.

Cavanna, A.E., Trimble, M.R., 2006. The precuneus: a review of its functional anatomy and behavioural correlates. Brain 129, 564-583.

Choi, S.W., Chi, S.E., Chung, S.Y., Kim, J.W., Ahn, C.Y., Kim, H.T., 2011. Is alpha wave neurofeedback effective with randomized clinical trials in depression? A pilot study. Neuropsychobiology 63, 43-51.

Cohen, J., 2013. Statistical power analysis for the behavioral sciences. Academic press.

Cojan, Y., Waber, L., Schwartz, S., Rossier, L., Forster, A., Vuilleumier, P., 2009. The Brain under Self-Control: Modulation of Inhibitory and Monitoring Cortical Networks during Hypnotic Paralysis. Neuron 62, 862-875.

Compas, B.E., Jaser, S.S., Bettis, A.H., Watson, K.H., Gruhn, M.A., Dunbar, J.P., Williams, E., Thigpen, J.C., 2017. Coping, emotion regulation, and psychopathology in childhood and adolescence: A meta-analysis and narrative review. Psychological bulletin 143, 939-991.

de Munck, J.C., Gonçalves, S.I., Faes, T.J.C., Kuijer, J.P.A., Pouwels, P.J.W., Heethaar, R.M., Lopes da Silva, F.H., 2008. A study of the brain's resting state based on alpha band power, heart rate and fMRI. Neuroimage 42, 112-121.

de Munck, J.C., Gonçalves, S.I., Huijboom, L., Kuijer, J.P.A., Pouwels, P.J.W., Heethaar, R.M., Lopes da Silva, F.H., 2007. The hemodynamic response of the alpha rhythm: An EEG/fMRI study. Neuroimage 35, 1142-1151.

Deco, G., Ponce-Alvarez, A., Mantini, D., Romani, G.L., Hagmann, P., Corbetta, M., 2013. Resting-state functional connectivity emerges from structurally and dynamically shaped slow linear fluctuations. J Neurosci 33, 11239-11252.

Delorme, A., Makeig, S., 2004. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of neuroscience methods 134, 9-21.

Delorme, A., Palmer, J., Onton, J., Oostenveld, R., Makeig, S., 2012. Independent EEG sources are dipolar. PLoS One 7, e30135.

DeSerisy, M., Ramphal, B., Pagliaccio, D., Raffanello, E., Tau, G., Marsh, R., Posner, J., Margolis, A.E., 2021. Frontoparietal and default mode network connectivity varies with age and intelligence. Developmental Cognitive Neuroscience 48, 100928.

Dickerson, B.C., Eichenbaum, H., 2010. The episodic memory system: neurocircuitry and disorders. Neuropsychopharmacology 35, 86-104.

Dijkstra, N., Bosch, S.E., van Gerven, M.A.J., 2017. Vividness of Visual Imagery Depends on the Neural Overlap with Perception in Visual Areas. The Journal of Neuroscience 37, 1367-1373.

Doren, J.V., Arns, M., Heinrich, H., Vollebregt, M., Strehl, U., Loo, S.K., 2019. Sustained effects of neurofeedback in ADHD: a systematic review and meta-analysis. European Child & Adolescent Psychiatry 28, 293-305.

Dray, S., 2008. On the number of principal components: A test of dimensionality based on measurements of similarity between matrices. Computational Statistics & Data Analysis 52, 2228-2237.

Enriquez-Geppert, S., Huster, R.J., Herrmann, C.S., 2017. EEG-Neurofeedback as a Tool to Modulate Cognition and Behavior: A Review Tutorial. Frontiers in Human Neuroscience 11, 51-51.

Enriquez-Geppert, S., Smit, D., Pimenta, M.G., Arns, M., 2019. Neurofeedback as a Treatment Intervention in ADHD: Current Evidence and Practice. Current Psychiatry Reports 21, 46.

Escolano, C., Aguilar, M., Minguez, J., 2011. EEG-based upper alpha neurofeedback training improves working memory performance. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2327-2330.

Escolano, C., Navarro-Gil, M., Garcia-Campayo, J., Congedo, M., De Ridder, D., Minguez, J., 2014. A controlled study on the cognitive effect of alpha neurofeedback training in patients with major depressive disorder. Frontiers in Behavioral Neuroscience 8.

Feige, B., Scheffler, K., Esposito, F., Di Salle, F., Hennig, J., Seifritz, E., 2005. Cortical and subcortical correlates of electroencephalographic alpha rhythm modulation. J Neurophysiol 93, 2864-2872.

Freyer, F., Becker, R., Dinse, H.R., Ritter, P., 2013. State-Dependent Perceptual Learning. The Journal of Neuroscience 33, 2900-2907.

Frishkoff, G.A., Tucker, D.M., Davey, C., Scherg, M., 2004. Frontal and posterior sources of event-related potentials in semantic comprehension. Brain Res Cogn Brain Res 20, 329-354.

Fuchs, M., Kastner, J., Wagner, M., Hawes, S., Ebersole, J.S., 2002. A standardized boundary element method volume conductor model. Clin Neurophysiol 113, 702-712.

Ganis, G., Thompson, W.L., Kosslyn, S.M., 2004. Brain areas underlying visual mental imagery and visual perception: an fMRI study. Cognitive Brain Research 20, 226-241.

Geerligs, L., Rubinov, M., Cam, C., Henson, R.N., 2015. State and Trait Components of Functional Connectivity: Individual Differences Vary with Mental State. J Neurosci 35, 13949-13961.

Goldman, R.I., Stern, J.M., Engel, J., Cohen, M.S., 2002. Simultaneous EEG and fMRI of the alpha rhythm. Neuroreport 13, 2487-2492.

Gonçalves, S.I., de Munck, J.C., Pouwels, P.J.W., Schoonhoven, R., Kuijer, J.P.A., Maurits, N.M., Hoogduin, J.M., Van Someren, E.J.W., Heethaar, R.M., Lopes da Silva, F.H., 2006. Correlating the alpha rhythm to BOLD using simultaneous EEG/fMRI: Inter-subject variability. Neuroimage 30, 203-213.

Grech, R., Cassar, T., Muscat, J., Camilleri, K.P., Fabri, S.G., Zervakis, M., Xanthopoulos, P., Sakkalis, V., Vanrumste, B., 2008. Review on solving the inverse problem in EEG source analysis. Journal of NeuroEngineering and Rehabilitation 5, 25.

Greicius, M.D., Krasnow, B., Reiss, A.L., Menon, V., 2003. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci U S A 100, 253-258.

Gruzelier, J.H., 2014a. EEG-neurofeedback for optimising performance. I: A review of cognitive and affective outcome in healthy participants. Neuroscience & Biobehavioral Reviews 44, 124-141.

Gruzelier, J.H., 2014b. EEG-neurofeedback for optimising performance. III: a review of methodological and theoretical considerations. Neurosci Biobehav Rev 44, 159-182.

Gusnard, D.A., Raichle, M.E., Raichle, M.E., 2001. Searching for a baseline: functional imaging and the resting human brain. Nat Rev Neurosci 2, 685-694.

Hallett, M., 2007. Transcranial Magnetic Stimulation: A Primer. Neuron 55, 187-199.

Hallez, H., Vanrumste, B., Grech, R., Muscat, J., De Clercq, W., Vergult, A., D'Asseler, Y., Camilleri, K.P., Fabri, S.G., Van Huffel, S., Lemahieu, I., 2007. Review on solving the forward problem in EEG source analysis. Journal of NeuroEngineering and Rehabilitation 4, 46.

Hamani, C., Moro, E., 2012. Chapter One - Neuromodulation: A More Comprehensive Concept Beyond Deep Brain Stimulation. In: Hamani, C., Moro, E. (Eds.), International Review of Neurobiology. Academic Press, pp. 1-3.

Hammond, D.C., 2005. Neurofeedback treatment of depression and anxiety. Journal of Adult Development 12, 131-137.

Heinrich, H., Gevensleben, H., Strehl, U., 2007. Annotation: neurofeedback - train your brain to train behaviour. J Child Psychol Psychiatry 48, 3-16.

Holmes, G.L., Khazipov, R., 2007. Basic Neurophysiology and the Cortical Basis of EEG. In: Blum, A.S., Rutkove, S.B. (Eds.), The Clinical Neurophysiology Primer. Humana Press, Totowa, NJ, pp. 19-33.

Händel, B.F., Haarmeier, T., Jensen, O., 2011. Alpha Oscillations Correlate with the Successful Inhibition of Unattended Stimuli. Journal of cognitive neuroscience 23, 2494-2502.

Hsueh, J.J., Chen, T.S., Chen, J.J., Shaw, F.Z., 2016. Neurofeedback training of EEG alpha rhythm enhances episodic and working memory. Human Brain Mapping 37, 2662-2675.

Jann, K., Dierks, T., Boesch, C., Kottlow, M., Strik, W., Koenig, T., 2009. BOLD correlates of EEG alpha phase-locking and the fMRI default mode network. Neuroimage 45, 903-916.

Jutten, C., Herault, J., 1991. Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture. Signal Processing 24, 1-10.

Kaiboriboon, K., Lüders, H.O., Hamaneh, M., Turnbull, J., Lhatoo, S.D., 2012. EEG source imaging in epilepsy—practicalities and pitfalls. Nature Reviews Neurology 8, 498-507.

Kamiya, J., 2011. The First Communications About Operant Conditioning of the EEG. Journal of Neurotherapy 15, 65-73.

Kelley, N.J., Gallucci, A., Riva, P., Romero Lauro, L.J., Schmeichel, B.J., 2019. Stimulating Self-Regulation: A Review of Non-invasive Brain Stimulation Studies of Goal-Directed Behavior. Frontiers in Behavioral Neuroscience 12.

Keng, S.-L., Smoski, M.J., Robins, C.J., 2011. Effects of mindfulness on psychological health: a review of empirical studies. Clinical psychology review 31, 1041-1056.

Klimesch, W., 1999. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Rev 29, 169-195.

Kluetsch, R.C., Ros, T., Théberge, J., Frewen, P.A., Calhoun, V.D., Schmahl, C., Jetly, R., Lanius, R.A., 2014. Plastic modulation of PTSD resting-state networks and subjective wellbeing by EEG neurofeedback. Acta Psychiatrica Scandinavica 130, 123-136.

Knyazev, G., 2013. EEG Correlates of Self-Referential Processing. Frontiers in Human Neuroscience 7.

Knyazev, G.G., Slobodskoj-Plusnin, J.Y., Bocharov, A.V., Pylkova, L.V., 2011. The default mode network and EEG alpha oscillations: an independent component analysis. Brain Res 1402, 67-79.

Kozlova, L.I., Petrovskii, E.D., Verevkin, E.G., Mel’nikov, M.E., Savelov, A.A., Shtark, M.B., 2019. EEG Alpha-Rhythm-Related Changes in BOLD fMRI Signal in Neurofeedback Training. Bulletin of Experimental Biology and Medicine 168, 199-204.

Kozlova, L.I., Shtark, M.B., Mel'nikov, M.E., Verevkin, E.G., Savelov, A.A., Petrovskii, E.D., 2016. EEG-fMRI Study of Alpha-Stimulation Neurobiofeedback Training Course. Bull Exp Biol Med 161, 623-628.

Laufs, H., Kleinschmidt, A., Beyerle, A., Eger, E., Salek-Haddadi, A., Preibisch, C., Krakow, K., 2003. EEG-correlated fMRI of human alpha activity. Neuroimage 19, 1463-1476.

Leech, R., Sharp, D.J., 2014. The role of the posterior cingulate cortex in cognition and disease. Brain 137, 12-32.

Lin, F.-H., Witzel, T., Raij, T., Ahveninen, J., Tsai, K.W.-K., Chu, Y.-H., Chang, W.-T., Nummenmaa, A., Polimeni, J.R., Kuo, W.-J., Hsieh, J.-C., Rosen, B.R., Belliveau, J.W., 2013. fMRI hemodynamics accurately reflects neuronal timing in the human brain measured by MEG. Neuroimage 78, 372-384.

Lopes da Silva, F., 2004. Functional localization of brain sources using EEG and/or MEG data: volume conductor and source models. Magn Reson Imaging 22, 1533-1538.

Lopes da Silva, F.H., van Lierop, T.H., Schrijer, C.F., van Leeuwen, W.S., 1973a. Essential differences between alpha rhythms and barbiturate spindles: spectra and thalamo-cortical coherences. Electroencephalogr Clin Neurophysiol 35, 641-645.

Lopes da Silva, F.H., van Lierop, T.H.M.T., Schrijer, C.F., Storm van Leeuwen, W., 1973b. Organization of thalamic and cortical alpha rhythms: Spectra and coherences. Electroencephalography and clinical neurophysiology 35, 627-639.

Lozano, A.M., Lipsman, N., Bergman, H., Brown, P., Chabardes, S., Chang, J.W., Matthews, K., McIntyre, C.C., Schlaepfer, T.E., Schulder, M., Temel, Y., Volkmann, J., Krauss, J.K., 2019. Deep brain stimulation: current challenges and future directions. Nature reviews. Neurology 15, 148-160.

Ma, N., Liu, Y., Fu, X.-M., Li, N., Wang, C.-X., Zhang, H., Qian, R.-B., Xu, H.-S., Hu, X., Zhang, D.-R., 2011. Abnormal Brain Default-Mode Network Functional Connectivity in Drug Addicts. PLoS One 6, e16560.

Magosso, E., De Crescenzio, F., Ricci, G., Piastra, S., Ursino, M., 2019. EEG Alpha Power Is Modulated by Attentional Changes during Cognitive Tasks and Virtual Reality Immersion. Computational Intelligence and Neuroscience 2019, 7051079.

Manshanden, I., De Munck, J.C., Simon, N.R., Lopes da Silva, F.H., 2002. Source localization of MEG sleep spindles and the relation to sources of alpha band rhythms. Clinical Neurophysiology 113, 1937-1947.

Mantini, D., Perrucci, M.G., Del Gratta, C., Romani, G.L., Corbetta, M., 2007. Electrophysiological signatures of resting state networks in the human brain. Proceedings of the National Academy of Sciences 104, 13170-13175.

Marder, E., 2012. Neuromodulation of neuronal circuits: back to the future. Neuron 76, 1-11.

Marino, M., Liu, Q., Koudelka, V., Porcaro, C., Hlinka, J., Wenderoth, N., Mantini, D., 2018. Adaptive optimal basis set for BCG artifact removal in simultaneous EEG-fMRI. Scientific Reports 8, 8902.

Mayeli, A., Misaki, M., Zotev, V., Tsuchiyagaito, A., Al Zoubi, O., Phillips, R., Smith, J., Stewart, J.L., Refai, H., Paulus, M.P., Bodurka, J., 2020. Self-regulation of ventromedial prefrontal cortex activation using real-time fMRI neurofeedback-Influence of default mode network. Hum Brain Mapp 41, 342-352.

Mayhew, S.D., Bagshaw, A.P., 2017. Dynamic spatiotemporal variability of alpha-BOLD relationships during the resting-state and task-evoked responses. Neuroimage 155, 120-137.

Mayhew, S.D., Ostwald, D., Porcaro, C., Bagshaw, A.P., 2013. Spontaneous EEG alpha oscillation interacts with positive and negative BOLD responses in the visual–auditory cortices and default-mode network. Neuroimage 76, 362-372.

Mideksa, K.G., Hellriegel, H., Hoogenboom, N., Krause, H., Schnitzler, A., Deuschl, G., Raethjen, J., Heute, U., Muthuraman, M., 2012. Source analysis of median nerve stimulated somatosensory evoked potentials and fields using simultaneously measured EEG and MEG signals. 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4903-4906.

Miller, K.L., Luh, W.-M., Liu, T.T., Martinez, A., Obata, T., Wong, E.C., Frank, L.R., Buxton, R.B., 2001. Nonlinear temporal dynamics of the cerebral blood flow response. Human Brain Mapping 13, 1-12.

Mo, J., Liu, Y., Huang, H., Ding, M., 2013. Coupling between visual alpha oscillations and default mode activity. Neuroimage 68, 112-118.

Mohagheghi, A., Amiri, S., Moghaddasi Bonab, N., Chalabianloo, G., Noorazar, S.G., Tabatabaei, S.M., Farhang, S., 2017. A Randomized Trial of Comparing the Efficacy of Two Neurofeedback Protocols for Treatment of Clinical and Cognitive Symptoms of ADHD: Theta Suppression/Beta Enhancement and Theta Suppression/Alpha Enhancement. BioMed Research International 2017, 3513281.

Mohan, A., Roberto, A.J., Mohan, A., Lorenzo, A., Jones, K., Carney, M.J., Liogier-Weyback, L., Hwang, S., Lapidus, K.A., 2016. The Significance of the Default Mode Network (DMN) in Neurological and Neuropsychiatric Disorders: A Review. Yale J Biol Med 89, 49-57.

Monastra, V.J., Lynn, S., Linden, M., Lubar, J.F., Gruzelier, J., LaVaque, T.J., 2005. Electroencephalographic biofeedback in the treatment of attention-deficit/hyperactivity disorder. Appl Psychophysiol Biofeedback 30, 95-114.

Moosmann, M., Ritter, P., Krastel, I., Brink, A., Thees, S., Blankenburg, F., Taskin, B., Obrig, H., Villringer, A., 2003. Correlates of alpha rhythm in functional magnetic resonance imaging and near infrared spectroscopy. Neuroimage 20, 145-158.

Naas, A., Rodrigues, J., Knirsch, J.P., Sonderegger, A., 2019. Neurofeedback training with a low-priced EEG device leads to faster alpha enhancement but shows no effect on cognitive performance: A single-blind, sham-feedback study. PLoS One 14, e0211668.

Nan, W., Wan, F., Tang, Q., Wong, C.M., Wang, B., Rosa, A., 2018. Eyes-closed resting EEG predicts the learning of alpha down-regulation in neurofeedback training. Frontiers in Psychology 9.

Natu, V.S., Lin, J.-J., Burks, A., Arora, A., Rugg, M.D., Lega, B., 2019. Stimulation of the Posterior Cingulate Cortex Impairs Episodic Memory Encoding. The Journal of Neuroscience 39, 7173-7182.

Niazy, R.K., Beckmann, C.F., Iannetti, G.D., Brady, J.M., Smith, S.M., 2005. Removal of FMRI environment artifacts from EEG data using optimal basis sets. Neuroimage 28, 720-737.

Niedermeyer, E., 2005. The normal EEG of the waking adult. Electroencephalography: Basic principles, clinical applications, and related fields 167, 155-164.

Northoff, G., Bermpohl, F., 2004. Cortical midline structures and the self. Trends Cogn Sci 8, 102-107.

Northoff, G., Heinzel, A., de Greck, M., Bermpohl, F., Dobrowolny, H., Panksepp, J., 2006. Self-referential processing in our brain--a meta-analysis of imaging studies on the self. Neuroimage 31, 440-457.

Nowlis, D.P., Kamiya, J., 1970. The control of electroencephalographic alpha rhythms through auditory feedback and the associated mental activity. Psychophysiology 6, 476-484.

Nunez, P.L., Wingeier, B.M., Silberstein, R.B., 2001. Spatial‐temporal structures of human alpha rhythms: Theory, microcurrent sources, multiscale measurements, and global binding of local networks. Human Brain Mapping 13, 125-164.

Okuda, J., Fujii, T., Ohtake, H., Tsukiura, T., Tanji, K., Suzuki, K., Kawashima, R., Fukuda, H., Itoh, M., Yamadori, A., 2003. Thinking of the future and past: the roles of the frontal pole and the medial temporal lobes. Neuroimage 19, 1369-1380.

Olbrich, S., Mulert, C., Karch, S., Trenner, M., Leicht, G., Pogarell, O., Hegerl, U., 2009. EEG-vigilance and BOLD effect during simultaneous EEG/fMRI measurement. Neuroimage 45, 319-332.

Omata, K., Hanakawa, T., Morimoto, M., Honda, M., 2013. Spontaneous Slow Fluctuation of EEG Alpha Rhythm Reflects Activity in Deep-Brain Structures: A Simultaneous EEG-fMRI Study. PLoS One 8, e66869.

Palva, S., Palva, J.M., 2007. New vistas for α-frequency band oscillations. Trends in Neurosciences 30, 150-158.

Pang, J.C., Robinson, P.A., 2018. Neural mechanisms of the EEG alpha-BOLD anticorrelation. Neuroimage 181, 461-470.

Papma, J.M., Smits, M., De Groot, M., Raso, F.U.M., van der Lugt, A., Vrooman, H.A., Niessen, W.J., Koudstaal, P.J., van Swieten, J.C., van der Veen, F.M., 2017. The effect of hippocampal function, volume and connectivity on posterior cingulate cortex functioning during episodic memory fMRI in mild cognitive impairment. Eur Radiol 27, 3716-3724.

Penny, W.D., Friston, K.J., Ashburner, J.T., Kiebel, S.J., Nichols, T.E., 2011. Statistical parametric mapping: the analysis of functional brain images. Elsevier.

Portillo-Lara, R., Tahirbegi, B., Chapman, C.A.R., Goding, J.A., Green, R.A., 2021. Mind the gap: State-of-the-art technologies and applications for EEG-based brain–computer interfaces. APL Bioengineering 5, 031507.

Raichle, M.E., MacLeod, A.M., Snyder, A.Z., Powers, W.J., Gusnard, D.A., Shulman, G.L., 2001. A default mode of brain function. Proceedings of the National Academy of Sciences 98, 676-682.

Rihs, T.A., Michel, C.M., Thut, G., 2007. Mechanisms of selective inhibition in visual spatial attention are indexed by α-band EEG synchronization. European Journal of Neuroscience 25, 603-610.

Rodin, E.A., Rodin, M.J., 1995. Dipole sources of the human alpha rhythm. Brain Topography 7, 201-208.

Ros, T., Theberge, J., Frewen, P.A., Kluetsch, R., Densmore, M., Calhoun, V.D., Lanius, R.A., 2013. Mind over chatter: plastic up-regulation of the fMRI salience network directly after EEG neurofeedback. Neuroimage 65, 324-335.

Royer, J.M., 1979. Theories of the transfer of learning. Educational Psychologist 14, 53-69.

Rusiniak, M., Wróbel, A., Cieśla, K., Pluta, A., Lewandowska, M., Wójcik, J., Skarżyński, P.H., Wolak, T., 2018. The relationship between alpha burst activity and the default mode network. Acta Neurobiol Exp (Wars) 78, 92-113.

Sadaghiani, S., Scheeringa, R., Lehongre, K., Morillon, B., Giraud, A.L., Kleinschmidt, A., 2010. Intrinsic connectivity networks, alpha oscillations, and tonic alertness: a simultaneous electroencephalography/functional magnetic resonance imaging study. J Neurosci 30, 10243-10250.

Sadtler, P.T., Quick, K.M., Golub, M.D., Chase, S.M., Ryu, S.I., Tyler-Kabara, E.C., Yu, B.M., Batista, A.P., 2014. Neural constraints on learning. Nature 512, 423-426.

Schabus, M., Heib, D.P., Lechinger, J., Griessenberger, H., Klimesch, W., Pawlizki, A., Kunz, A.B., Sterman, B.M., Hoedlmoser, K., 2014. Enhancing sleep quality and memory in insomnia using instrumental sensorimotor rhythm conditioning. Biol Psychol 95, 126-134.

Scheeringa, R., Petersson, K.M., Kleinschmidt, A., Jensen, O., Bastiaansen, M.C., 2012. EEG alpha power modulation of FMRI resting-state connectivity. Brain connectivity 2, 254-264.

Scherg, M., Berg, P., Nakasato, N., Beniczky, S., 2019. Taking the EEG Back Into the Brain: The Power of Multiple Discrete Sources. Frontiers in Neurology 10.

Scherg, M., Ebersole, J., 1993. Models of brain sources. Brain Topography 5, 419-423.

Schneider, F., Bermpohl, F., Heinzel, A., Rotte, M., Walter, M., Tempelmann, C., Wiebking, C., Dobrowolny, H., Heinze, H.J., Northoff, G., 2008. The resting brain and our self: self-relatedness modulates resting state neural activity in cortical midline structures. Neuroscience 157, 120-131.

Schomer, D.L., Da Silva, F.L., 2012. Niedermeyer's electroencephalography: basic principles, clinical applications, and related fields. Lippincott Williams & Wilkins.

Shaw, F.-Z., Lai, C.J., Chiu, T.H., 2002. A low-noise flexible integrated system for recording and analysis of multiple electrical signals during sleep–wake states in rats. Journal of neuroscience methods 118, 77-87.

Sheffield, J.M., Barch, D.M., 2016. Cognition and resting-state functional connectivity in schizophrenia. Neurosci Biobehav Rev 61, 108-120.

Shibata, K., Watanabe, T., Sasaki, Y., Kawato, M., 2011. Perceptual learning incepted by decoded fMRI neurofeedback without stimulus presentation. Science 334, 1413-1415.

Shtark, M.B., Kozlova, L.I., Bezmaternykh, D.D., Mikhail Ye, M.n., Savelov, A.A., Sokhadze, E.M., 2018. Neuroimaging Study of Alpha and Beta EEG Biofeedback Effects on Neural Networks. Applied Psychophysiology and Biofeedback 43, 169-178.

Shulman, G.L., Fiez, J.A., Corbetta, M., Buckner, R.L., Miezin, F.M., Raichle, M.E., Petersen, S.E., 1997. Common blood flow changes across visual tasks: II. Decreases in cerebral cortex. Journal of cognitive neuroscience 9, 648-663.

Sigala, R., Haufe, S., Roy, D., Dinse, H.R., Ritter, P., 2014. The role of alpha-rhythm states in perceptual learning: insights from experiments and computational models. Frontiers in computational neuroscience 8, 36-36.

Sitaram, R., Ros, T., Stoeckel, L., Haller, S., Scharnowski, F., Lewis-Peacock, J., Weiskopf, N., Blefari, M.L., Rana, M., Oblak, E., Birbaumer, N., Sulzer, J., 2017. Closed-loop brain training: the science of neurofeedback. Nat Rev Neurosci 18, 86-100.

Skottnik, L., Linden, D.E.J., 2019. Mental Imagery and Brain Regulation—New Links Between Psychotherapy and Neuroscience. Frontiers in Psychiatry 10.

Sporns, O., 2013. Structure and function of complex brain networks. Dialogues in clinical neuroscience 15, 247-262.

Srinivasan, R., Winter, W.R., Nunez, P.L., 2006. Source analysis of EEG oscillations using high-resolution EEG and MEG. Prog Brain Res 159, 29-42.

Stawarczyk, D., Majerus, S., Maquet, P., D'Argembeau, A., 2011. Neural Correlates of Ongoing Conscious Experience: Both Task-Unrelatedness and Stimulus-Independence Are Related to Default Network Activity. PLoS One 6, e16997.

Sterman, M.B., Howe, R.C., Macdonald, L.R., 1970. Facilitation of spindle-burst sleep by conditioning of electroencephalographic activity while awake. Science 167, 1146-1148.

Su, K.-H., Hsueh, J.-J., Chen, T., Shaw, F.-Z., 2021. Validation of eyes-closed resting alpha amplitude predicting neurofeedback learning of upregulation alpha activity. Scientific Reports 11, 19615.

Sun, L., Cao, Q., Long, X., Sui, M., Cao, X., Zhu, C., Zuo, X., An, L., Song, Y., Zang, Y., Wang, Y., 2012. Abnormal functional connectivity between the anterior cingulate and the default mode network in drug-naïve boys with attention deficit hyperactivity disorder. Psychiatry Research: Neuroimaging 201, 120-127.

Tarkka, I.M., Treede, R.D., 1993. Equivalent electrical source analysis of pain-related somatosensory evoked potentials elicited by a CO2 laser. J Clin Neurophysiol 10, 513-519.

Tommasin, S., Mascali, D., Gili, T., Eid Assan, I., Moraschi, M., Fratini, M., Wise, R.G., Macaluso, E., Mangia, S., Giove, F., 2017. Task-Related Modulations of BOLD Low-Frequency Fluctuations within the Default Mode Network. Frontiers in Physics 5.

Uludag, K., Dubowitz, D.J., Yoder, E.J., Restom, K., Liu, T.T., Buxton, R.B., 2004. Coupling of cerebral blood flow and oxygen consumption during physiological activation and deactivation measured with fMRI. Neuroimage 23, 148-155.

Uludağ, K., Roebroeck, A., 2014. General overview on the merits of multimodal neuroimaging data fusion. Neuroimage 102, 3-10.

Van Den Heuvel, M.P., Pol, H.E.H., 2010. Exploring the brain network: a review on resting-state fMRI functional connectivity. European Neuropsychopharmacology 20, 519-534.

van Vliet, M., Chumerin, N., De Deyne, S., Wiersema, J.R., Fias, W., Storms, G., Van Hulle, M.M., 2016. Single-Trial ERP Component Analysis Using a Spatiotemporal LCMV Beamformer. IEEE Trans Biomed Eng 63, 55-66.

Vanneste, S., Joos, K., Ost, J., De Ridder, D., 2018. Influencing connectivity and cross-frequency coupling by real-time source localized neurofeedback of the posterior cingulate cortex reduces tinnitus related distress. Neurobiology of Stress 8, 211-224.

Vernon, D., Dempster, T., Bazanova, O., Rutterford, N., Pasqualini, M., Andersen, S., 2009. Alpha neurofeedback training for performance enhancement: reviewing the methodology. Journal of Neurotherapy 13, 214-227.

Wan, F., Nan, W., Vai, M.I., Rosa, A., 2014. Resting alpha activity predicts learning ability in alpha neurofeedback. Frontiers in Human Neuroscience 8, 500-500.

Wang, J.-R., Hsieh, S., 2013. Neurofeedback training improves attention and working memory performance. Clinical Neurophysiology 124, 2406-2420.

Wang, M., Hu, Z., Liu, L., Li, H., Qian, Q., Niu, H., 2020. Disrupted functional brain connectivity networks in children with attention-deficit/hyperactivity disorder: evidence from resting-state functional near-infrared spectroscopy. Neurophotonics 7, 015012.

Wang, R.W.Y., Chang, W.-L., Chuang, S.-W., Liu, I.N., 2019a. Posterior cingulate cortex can be a regulatory modulator of the default mode network in task-negative state. Scientific Reports 9, 7565-7565.

Wang, S.-Y., Lin, I.M., Fan, S.-Y., Tsai, Y.-C., Yen, C.-F., Yeh, Y.-C., Huang, M.-F., Lee, Y., Chiu, N.-M., Hung, C.-F., Wang, P.-W., Liu, T.-L., Lin, H.-C., 2019b. The effects of alpha asymmetry and high-beta down-training neurofeedback for patients with the major depressive disorder and anxiety symptoms. Journal of Affective Disorders 257, 287-296.

Wang, Y.-F., Liu, F., Long, Z.-L., Duan, X.-J., Cui, Q., Yan, J.H., Chen, H.-F., 2014. Steady-State BOLD Response Modulates Low Frequency Neural Oscillations. Scientific Reports 4, 7376.

Wei, T.-Y., Chang, D.-W., Liu, Y.-D., Liu, C.-W., Young, C.-P., Liang, S.-F., Shaw, F.-Z., 2017. Portable wireless neurofeedback system of EEG alpha rhythm enhances memory. BioMedical Engineering OnLine 16, 128.

Wojtczak-Kwaśniewska, M., Przekoracka-Krawczyk, A., Van der Lubbe, R.H.J., 2018. The engagement of cortical areas preceding exogenous vergence eye movements. PLoS One 13, e0198405-e0198405.

Woo, C.W., Krishnan, A., Wager, T.D., 2014. Cluster-extent based thresholding in fMRI analyses: pitfalls and recommendations. Neuroimage 91, 412-419.

Wu, L., Eichele, T., Calhoun, V.D., 2010. Reactivity of hemodynamic responses and functional connectivity to different states of alpha synchrony: a concurrent EEG-fMRI study. Neuroimage 52, 1252-1260.

Yan, C.-G., Cheung, B., Kelly, C., Colcombe, S., Craddock, R.C., Di Martino, A., Li, Q., Zuo, X.-N., Castellanos, F.X., Milham, M.P., 2013. A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage 76, 183-201.

Yan, C.-G., Wang, X.-D., Zuo, X.-N., Zang, Y.-F., 2016. DPABI: data processing & analysis for (resting-state) brain imaging. Neuroinformatics 14, 339-351.

Yan, C.-G., Zang, Y.-F., 2010. DPARSF: A MATLAB Toolbox for "Pipeline" Data Analysis of Resting-State fMRI. Front Syst Neurosci 4, 13.

Yang, H., Long, X.-Y., Yang, Y., Yan, H., Zhu, C.-Z., Zhou, X.-P., Zang, Y.-F., Gong, Q.-Y., 2007. Amplitude of low frequency fluctuation within visual areas revealed by resting-state functional MRI. Neuroimage 36, 144-152.

Yu, S.H., Tseng, C.Y., Lin, W.L., 2020. A Neurofeedback Protocol for Executive Function to Reduce Depression and Rumination: A Controlled Study. Clin Psychopharmacol Neurosci 18, 375-385.

Yuan, B.-K., Wang, J., Zang, Y.-F., Liu, D.-Q., 2014. Amplitude differences in high-frequency fMRI signals between eyes open and eyes closed resting states. Frontiers in Human Neuroscience 8.

Zang, Y.F., He, Y., Zhu, C.Z., Cao, Q.J., Sui, M.Q., Liang, M., Tian, L.X., Jiang, T.Z., Wang, Y.F., 2007. Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev 29, 83-91.

Zhan, Z., Xu, L., Zuo, T., Xie, D., Zhang, J., Yao, L., Wu, X., 2014. The contribution of different frequency bands of fMRI data to the correlation with EEG alpha rhythm. Brain Res 1543, 235-243.

Zoefel, B., Huster, R.J., Herrmann, C.S., 2011. Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance. Neuroimage 54, 1427-1431.

Zou, Q.H., Zhu, C.Z., Yang, Y., Zuo, X.N., Long, X.Y., Cao, Q.J., Wang, Y.F., Zang, Y.F., 2008. An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J Neurosci Methods 172, 137-141.

Zumer, J.M., Scheeringa, R., Schoffelen, J.-M., Norris, D.G., Jensen, O., 2014. Occipital Alpha Activity during Stimulus Processing Gates the Information Flow to Object-Selective Cortex. PLOS Biology 12, e1001965.