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Here you will be able to find all references for the original pieces, literature thesis and articles published in the ABC Journal from issue 10 onward.
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Table of Contents
Issue 11
At the Intersection of Art and Neuroscience
— Original Piece
Dikker, S., Montgomery, S., & Tunca, S. (2019). Using Synchrony-Based Neurofeedback in Search of Human Connectedness. Brain Art, 161–206. doi:10.1007/978-3-030-14323-7_6
Dikker, Suzanne; Michalareas, Georgios; Oostrik, Matthias; Serafimaki, Amalia; Kahraman, Hasibe Melda; Struiksma, Marijn E.; Poeppel, David (2020). Crowdsourcing neuroscience: inter-brain coupling during face-to-face interactions outside the laboratory. NeuroImage, (), 117436–.doi:10.1016/j.neuroimage.2020.117436
http://www.suzannedikker.net/mutualwavemachine#mutualwavemachine
Use of prosody to mark information structure in autistic female and male adults with high-level language ability.
— Research Article
Attwood, T. (2007). The complete guide to Asperger’s syndrome. London, UK: Jessica Kingsley Publishers.
Baron-Cohen, S. (2002) The extreme male brain theory of autism. Trends Cogn Sci 6: 248–254.
Baron-Cohen, S., & Wheelwright, S. (2004). The empathy quotient: An investigation of adults with Asperger syndrome or high functioning autism, and normal sex differences. J Autism Dev Discord, 34. 163-175.
Boersma, P., & Weenink, D. (2009). Praat: Doing Phonetics by Computer [Computer Program]. Version 5.1.07. Available at: http://www.praat.org/
Chen, A. (2009). “The phonetics of sentence-initial topic and focus in adult and child Dutch,” in Phonetics and Phonology: Interactions and Interrelations, eds M. Vigário, S. Frota, and M. J. Freitas (Amsterdam: Benjamins), 91–106.
Chen, A. (2011). Tuning information packaging: Intonational realization of topic and focus in child Dutch. Journal of Child Language, 38, 1055–1083.
DePape, A. M., Chen, A., Hall, G. B., & Trainor, L. J. (2012). Use of prosody and information structure in high functioning adults with autism in relation to language ability. Frontiers in psychology, 3, 72.
Diehl, J. J., Bennetto, L., Watson, D., Gunlogson, C., & McDonough, J. (2008). Resolving ambiguity: A psycholinguistic approach to understanding prosody processing in high-functioning autism. Brain and Language, 106, 144–152.
Doherty, R., Orimoto, W., Singelis, L., Theodore, M., Hatfield, E., & Hebb, J. (1995). Emotional contagion: gender and occupational differences. Psychology of Women Quarterly, 19, 355–371.
Dunn, L. M., & Dunn, L. M. (1997). Peabody Picture Vocabulary Test, 3rd Edn. Circle Pines, MN: American Guidance Services.
Eigsti, I-M., de Merchena, A. B., Schuh, J. M. & Kelley, E. (2011). Language acquisition in autism spectrum disorders: A developmental review. Research in Autism Spectrum Disorders, 5, 681 – 191.
Elie, B & Chardon, G. (2018). Glottal/Supraglottal Source Separation in Fricatives Based on Non- Stationary Signal Subspace Estimation.
Gotham, K., Risi, S., Pickles, A., & Lord, C. (2006). The Autism Diagnostic Observation Schedule (ADOS). Journal of Autism and Developmental Disorders.
Green, H., & Tobin, Y. (2009). Prosodic analysis is difficult. . .but worth it: a study in high functioning autism. Int. J. Speech Lang. Pathol. 11, 308–315.
Head, A. M., McGillivray, J.A. & Stokes, M.A., (2014). Gender differences in emotionality and sociability in children with autism spectrum disorders. Mol Autism. 5:19.
Healthed Pty Ltd. (2018, August 25). Tony Attwood – Asperger Syndrome in Females, Autism Spectrum Disorder in Females. Retrieved from: https://vimeo.com/122940958
Kenny, L., Hattersley, C., Molins, B., Buckley, C., Povey, C., & Pellicano, E. (2015). Which terms should be used to describe autism? Perspectives from the UK autism community. Autism, 20(4), 442–462.
Kjelgaard, M., & Tager-Flusberg, H. (2001). An investigation of language impairment in autism: implications for genetic subgroups. Lang. Cogn. Process. 16, 287–308.
Lai, M. C., Lombardo, M. V., Pasdo, G., Ruigrok, A.N.V., Wheelwright, S.J. & Sadek, S.A. (2011). A Behavioral Comparison of Male and Female Adults with High Functioning Autism Spectrum Conditions. PLoS ONE 6(6): e20835.
Leekam, S. R., Libby, S.J., Wing, L. et al. (2002). The Diagnostic Interview for Social and Communication Disorders: Algorithms for ICD-10 childhood autism and Wing and Gould autistic spectrum disorders.Journal of Child Psychology and Psychiatry and Allied Disciplines. 43(3):327–42.
Lord, C., Rutter, M., & Le Couteur, A. (1994). Autism diagnostic interview-revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J. Autism Dev. Disord. 24, 659–685.
Lord, C., Rutter, M., Goode, S., Heems-bergen, J., Jordan, H., Mawhood, L., and Schopler, E. (1989). Autism diagnostic observation schedule: a standardized observation of communicative and social behavior. J. Autism Dev. Disord. 19, 185–212.
McCann, J. & Peppé, S. (2003), Prosody in autism spectrum disorders: a critical review. International Journal of Language & Communication Disorders, 38: 325-350.
Nadig, A., & Shaw, H. (2011). Expressive prosody in high-functioning autism: increased pitch range and what it means to listeners. J. Autism Dev. Disord. 42: 499.
National Autistic Society (2015). What is autism? – | autism | Asperger syndrome |. [online] Autism.org.uk. Available at: http://www.autism.org.uk/about-autism/autism-and-asperger- syndrome-an-introduction/what-is-autism.aspx [Accessed 20 Jun. 2019].
Parish-Morris, J., Liberman, M. Y., Cieri, C., Herrington, J. D., Yerys, B. E., Bateman, L., … Schultz, R. T. (2017). Linguistic camouflage in girls with autism spectrum disorder. Molecular Autism, 8(1).
Paul, R., Augustyn, A., Klin, A. & Volkmar, F. R. (2005). Perception and production of prosody by speakers with autism spectrum disorders. Journal of Autism and Developmental Disorders. 205–220.
Paul, R., Shriberg, L. D., McSweeny, J., Cicchetti, D., Klin, A. & Volkmar, F. (2005). Brief report: Relations between prosodic performance and communication and socialization ratings in high functioning speakers with autism spectrum disorders. Journal of Autism and Developmental Disorders. 861–869.
Peppé, S., McCann, J., Gibbon, F. E., O’Hare, A., & Rutherford, M. (2006) Assessing prosodic and pragmatic ability in children with high-functioning autism. QMU Speech Science Research Centre Working Papers, WP-4.
Roach, P. (2000). Techniques for the Phonetic Description of Emotional Speech. In Proceedings of the ISCA Workshop on Speech and Emotion. Newcastle, Northern Ireland. September 2000 (pp 53-59).
The Speech and Language Store LLP. (2018). Receptive Language Assessment with Splingo (Version 4.5.5) [iPad Application Software]. Retrieved from http://itunes.apple.com
Wichmann, A., Dehé, N. & Barth-Weingarten, D., (2009). Where prosody meets pragmatics: research at the interface. Where Prosody meets Pragmatics. Bingley: Emerald, pp. 1-20.
Wing, L. & Gould, J. (1979). Severe impairments of social interaction and associated abnormalities in children: epidemiology and classification.Journal of autism and developmental disorders. 9(1):11–29.
Wynn, C. J., Borrie, S. A., & Sellers, T. P. (2018). Speech Rate Entrainment in Children and Adults With and Without Autism Spectrum Disorder. American Journal of Speech-Language Pathology, 27(3), 965.
Young, E.C., Diehl, J.J., Morris, D., Hyman, S.L. & Bennetto, L. (2005). The use of two language tests to identify pragmatic language problems in children with autism spectrum disorders. Language, Speech, and Hearing Services in Schools. 62–72.
Neuroaesthetics: Grounded in Science or Merely Aesthetically Pleasing?
— Original Piece
Armony, J., & Dolan, R. J. (2002). Modulation of spatial attention by fear-conditioned stimuli: an event-related fMRI study. Neuropsychologia, 40(7), 817–826. https://doi.org/10.1016/s0028-3932(01)00178-6
Capó, M. À., Cela-Conde, C. J., Munar, E., Rosselló, J., & Nadal, M. (2008). Towards a framework for the study of the neural correlates of aesthetic preference. Spatial Vision, 21(3–5), 379–396. https://doi.org/10.1163/156856808784532653
Cela-Conde, C. J., Marty, G., Munar, E., Nadal, M., & Burges, L. (2002). The “Style Scheme” Grounds Perception of Paintings. Perceptual and Motor Skills, 95(1), 91–100. https://doi.org/10.2466/pms.2002.95.1.91
Dougherty, D. D., Shin, L. M., Alpert, N. M., Pitman, R. K., Orr, S. P., Lasko, M., Macklin, M. L., Fischman, A. J., & Rauch, S. L. (1999). Anger in healthy men: a PET study using script-driven imagery. Biological Psychiatry, 46(4), 466–472. https://doi.org/10.1016/s0006-3223(99)00063
Gallace, A., & Spence, C. (2011). Tactile aesthetics: towards a definition of its characteristics and neural correlates. Social Semiotics, 21(4), 569–589. https://doi.org/10.1080/10350330.2011.591998
Jacobs, R. H. A. H., Renken, R., & Cornelissen, F. W. (2012). Neural Correlates of Visual Aesthetics – Beauty as the Coalescence of Stimulus and Internal State. PLoS ONE, 7(2), e31248. https://doi.org/10.1371/journal.pone.0031248
Kawabata, H., & Zeki, S. (2004). Neural Correlates of Beauty. Journal of Neurophysiology, 91(4), 1699–1705. https://doi.org/10.1152/jn.00696.2003
Kedia, G., Mussweiler, T., Mullins, P., & Linden, D. E. J. (2013). The neural correlates of beauty comparison. Social Cognitive and Affective Neuroscience, 9(5), 681–688. https://doi.org/10.1093/scan/nst026
Monet, C. (1900). The Artist’s Garden at Giverny [Painting]. https://www.claude-monet.com/the-artists-garden-at-giverny.jsp#prettyPhoto[image2]/0/
Nadal, M., Marty, G., & Munar, E. (2006). The Search for Objective Measures of Aesthetic Judgment: The Case of Memory Traces. Empirical Studies of the Arts, 24(1), 95–106. https://doi.org/10.2190/5nj2-7f9j-487p-dcpw
The neurobiology of beauty | Semir Zeki | TEDxUCL. (2012, July 2). [Video]. YouTube. https://www.youtube.com/watch?v=NlzanAw0RP4
Zeki, S., & Chén, O. Y. (2020). The Bayesian‐Laplacian brain. European Journal of Neuroscience, 51(6), 1441–1462. https://doi.org/10.1111/ejn.14540
Zeki, S., Chén, O. Y., & Romaya, J. P. (2018). The Biological Basis of Mathematical Beauty. Frontiers in Human Neuroscience, 12, 23–50. https://doi.org/10.3389/fnhum.2018.00467
Zeki, S., Romaya, J. P., Benincasa, D. M. T., & Atiyah, M. F. (2014). The experience of mathematical beauty and its neural correlates. Frontiers in Human Neuroscience, 8, 34–55. https://doi.org/10.3389/fnhum.2014.00068
Psychedelics and the predictive mind: A review of the potential mechanisms that underpin the efficacy of psilocybin to treat depressive disorders.
— Literature Thesis
Beck, A.T., 1979. Cognitive therapy of depression. Guilford press.
Nichols, D.E., 2016. Psychedelics. Pharmacol. Rev. 68, 264–355. doi:10.1124/pr.115.011478
Pahnke, W.N., 1969. Psychedelic drugs and mystical experience. Int. Psychiatry Clin. 5, 149–162.
Segal, Z.J., Williams, M.G., Teasdale, J.D., 2002. Mindfulness based cognitive therapy for depression: a new approach to preventing relapses. Guildford Press, New York.
Stace, W.T., 1960. Mysticism and philosophy. Research in politics and society.
Painting Hypotheses, Cooking Methods, and Composing Results: A Review on “Proust was a Neuroscientist”
— Original Piece
Lehrer, J. (2012). Proust was a neuroscientist. Edinburgh: Canongate.
Time Distortions: A Review
— Literature Thesis
Abe, K., Oda, N., Araki, R., & Igata, M. (1989). Macropsia, Micropsia, and Episodic Illusions in Japanese Adolescents. Journal of the American Academy of Child and Adolescent Psychiatry, 28(4), 493–496. https://doi.org/10.1097/00004583-198907000-00004
Allman, M. J., & Meck, W. H. (2012). Pathophysiological distortions in time perception and timed performance. Brain, 135(3), 656–677. https://doi.org/10.1093/brain/awr210
Bech, P. (1975). Depression: Influence on time estimation and time experience, 42–50.
Binswanger, L. (1960). Melancholie und Manie; phänomenologische Studien.
Blewett, A. E. (1992). Abnormal subjective time experience in depression. British Journal of Psychiatry, 161(AUG.), 195–200. https://doi.org/10.1192/bjp.161.2.195
Blom, J. D. (2016). Alice in Wonderland syndrome A systematic review. Neurology: Clinical Practice. Lippincott Williams and Wilkins. https://doi.org/10.1212/CPJ.0000000000000251
Bschor, T., Ising, M., Bauer, M., Lewitzka, U., Skerstupeit, M., Müller-Oerlinghausen, B., & Baethge, C. (2004). Time experience and time judgment in major depression, mania and healthy subjects. A controlled study of 93 subjects. Acta Psychiatrica Scandinavica, 109(3), 222–229. https://doi.org/10.1046/j.0001-690X.2003.00244.x
Carroll L. (1865). Alice’s Adventures in Wonderland (London: Ma). London: MacMillan and Co.
Clarke, S., Ivry, R., Grinband, J., Roberts, S., & Shimizu, N. (1996). Exploring the domain of the cerebellar timing system. Advances in Psychology,115(C), 257–280. https://doi.org/10.1016/S0166-4115(96)80063-X
Craig, A. D. (2009). Emotional moments across time: A possible neural basis for time perception in the anterior insula. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1525), 1933–1942. https://doi.org/10.1098/rstb.2009.0008
Davalos, D. B., Kisley, M. A., & Ross, R. G. (2003). Effects of interval duration on temporal processing in schizophrenia. Brain and Cognition, 52(3), 295–301. https://doi.org/10.1016/S0278-2626(03)00157-X
Elvevåg, B., McCormack, T., Gilbert, A., Brown, G. D. A., Weinberger, D. R., & Goldberg, T. E. (2003). Duration judgements in patients with schizophrenia. Psychological Medicine, 33(7), 1249–1261. https://doi.org/10.1017/S0033291703008122
Freedman, B. J. (1974). The Subjective Experience of Perceptual and Cognitive Disturbances in Schizophrenia. Archives of General Psychiatry, 30(3), 333. https://doi.org/10.1001/archpsyc.1974.01760090047008
Fuchs, T., & Van Duppen, Z. (2017). Time and Events: On the Phenomenology of Temporal Experience in Schizophrenia (Ancillary Article to EAWE Domain 2). Psychopathology, 50(1), 68–74. https://doi.org/10.1159/000452768
Ghaemi, S. N. (2007). Feeling and time: The phenomenology of mood disorders, depressive realism, and existential psychotherapy. Schizophrenia Bulletin, 33(1), 122–130. https://doi.org/10.1093/schbul/sbl061
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Gibbon, J., Malapanits, C., Dale, C. L., & Gallistep, C. R. (1997). Toward a neurobiology of temporal cognition: advances and challenges. Current Opinion in Neurobiology, 7, 170–184.
Jaspers, K. (1997). General Psychopathology, Vol. 1. Johns Hopkins University Press.
Jia, Y., & Miao, Y. (2018). Evidence for the perception of time distortion during episodes of Alice in wonderland syndrome. Journal of Nervous and Mental Disease, 206(6), 473–475. https://doi.org/10.1097/NMD.0000000000000825
Jones, C. R. G., & Jahanshahi, M. (2011). Dopamine Modulates Striato-Frontal Functioning during Temporal Processing. Frontiers in Integrative Neuroscience, 5, 70. https://doi.org/10.3389/fnint.2011.00070
Kitamura, T., & Kumar, R. (1982). Time passes slowly for patients with depressive state. Acta Psychiatrica Scandinavica, 65(6), 415–420. https://doi.org/10.1111/j.1600-0447.1982.tb00865.x
Kuhs, H. (1991). Time experience in melancholia: A comparison between findings based on phenomenology and experimental psychology. Comprehensive Psychiatry, 32(4), 324–329. https://doi.org/10.1016/0010-440X(91)90081-M
Lake, J. I. (2016). Recent advances in understanding emotion-driven temporal distortions. Current Opinion in Behavioral Sciences, 8, 214–219. https://doi.org/10.1016/j.cobeha.2016.02.009
Liu, A. M., Liu, J. G., Liu, G. W., & Liu, G. T. (2014). “Alice in wonderland” syndrome: Presenting and follow-up characteristics. Pediatric Neurology, 51(3), 317–320. https://doi.org/10.1016/j.pediatrneurol.2014.04.007
Mangels, J. A., Ivry, R. B., & Shimizu, N. (1998). Dissociable contributions of the prefrontal and neocerebellar cortex to time perception. Cognitive Brain Research, 7(1), 15–39. https://doi.org/10.1016/S0926-6410(98)00005-6
Mizuno, M., Kashima, H., Chiba, H., Murakami, M., & Asai, M. (1998). “Alice in Wonderland” syndrome as a precursor of depressive disorder. Psychopathology, 31(2), 85–89. https://doi.org/10.1159/000029027
Naarden, T., Ter Meulen, B. C., Van Der Weele, S. I., & Blom, J. Di. (2019). Alice in wonderland syndrome as a presenting manifestation of Creutzfeldt-Jakob disease. Frontiers in Neurology, 10(MAY), 1–6. https://doi.org/10.3389/fneur.2019.00473
O’Toole, P., & Modestino, E. J. (2017). Alice in Wonderland Syndrome: A real life version of Lewis Carroll’s novel. Brain and Development, 39(6), 470–474. https://doi.org/10.1016/j.braindev.2017.01.004
Perdices, M. (2018). The Alice in Worderland Syndrome. Neuropsychological Rehabilitation, 28(2), 189–198. https://doi.org/10.1080/09602011.2016.1224191
Rao, S. M., Mayer, A. R., & Harrington, D. L. (2001). The evolution of brain activation during temporal processing. Nature Neuroscience. https://doi.org/10.1038/85191
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Sackett, A. M., Meyvis, T., Nelson, L. D., Converse, B. A., & Sackett, A. L. (2010). You’re having fun when time flies: The hedonic consequences of subjective time progression. Psychological Science, 21(1), 111–117. https://doi.org/10.1177/0956797609354832
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Shammas, M. K. (2020). The Curious Case of the Fast Feelers: A Reflection on Alice in Wonderland Syndrome. Pediatric Neurology. https://doi.org/10.1016/j.pediatrneurol.2020.06.004
Stanghellini, G., Ballerini, M., Presenza, S., Mancini, M., Northoff, G., & Cutting, J. (2017). Abnormal Time Experiences in Major Depression: An Empirical Qualitative Study. Psychopathology, 50(2), 125–140. https://doi.org/10.1159/000452892
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Teixeira, S., Machado, S., Paes, F., Velasques, B., Silva, J., Sanfim, A., … Arias-Carrion, O. (2013). Time Perception Distortion in Neuropsychiatric and Neurological Disorders. CNS & Neurological Disorders – Drug Targets, 12(5), 567–582. https://doi.org/10.2174/18715273113129990080
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Üstün, S., Kale, E. H., & Çiçek, M. (2017). Neural networks for time perception and working memory. Frontiers in Human Neuroscience. https://doi.org/10.3389/fnhum.2017.00083
Vogel, D.H.V., Beeker, T., Haidl, T., Kupke, C., Heinze, M., & Vogeley, K. (2019). Disturbed time experience during and after psychosis. Schizophrenia Research: Cognition, 17(January), 100136. https://doi.org/10.1016/j.scog.2019.100136
Vogel, D.H.V., Falter-Wagner, C. M., Schoofs, T., Krämer, K., Kupke, C., & Vogeley, K. (2018). Flow and structure of time experience – concept, empirical validation and implications for psychopathology. Phenomenology and the Cognitive Sciences, 1–24. https://doi.org/10.1007/s11097-018-9573-z
Vogel, H.V., Krämer, K., Schoofs, T., Kupke, C., & Vogeley, K. (2018). Disturbed experience of time in depression—evidence from content analysis. Frontiers in Human Neuroscience, 12(February), 1–10. https://doi.org/10.3389/fnhum.2018.00066
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Better Research Practices: Own Your Research Decisions
— Original Piece
Image reference:
An instance of Open Science principles. This specific set of principles guides the Open Traits Network — a global initiative for sharing and integrating trait data across organisms. Adapted with permission from “Open Science principles for accelerating trait-based science across the Tree of Life,” by Gallagher, R. V., Falster, D. S., Maitner, B. et al., 2020, Nature ecology & evolution, 4(3), 294-303.
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Dissociating contributions of periodic and aperiodic neural activity in human visual working memory.
— Research Project
1 - Supplementary Material
1.1 – Single trial power spectra
Single trial power spectra are in general quite noisy. Especially in this research, since there were limited time points due to the task design. Even though, the model was still able to fit the data with an average error of 0.237 over the participants that exhibit theta power. Furthermore, the error is not significantly different between the baseline period or the retention period, nor between good and poor performance, excluding that this is driving the results. Supplementary figure 1 & 2 show the fit of six single trials from a random participant for the retention period and the baseline period. The top row shows the three worst fit (highest error), and the bottom row the three best fits (lowest error). The error is calculated over the whole frequency range 2 to 40 Hz, and thus is not very informative to determine whether the model was able to capture a peak in the theta frequency range (4 – 7 Hz), since the theta frequency range is such a small portion of the whole frequency range. This becomes clear when comparing the top and bottom rows (supplementary figure 1 & 2).
Supplementary figure 1: Six single trials of good performance during the baseline period. These six single trials are from one random participant. The top row are the three worst fits, and in the bottom row are the three best fits. The black line is the single trial power spectrum. The blue line is the aperiodic fit and in red is the full model fit.
Supplementary figure 2: Six single trials of good performance during the retention period. These six single trials are from one random participant. The top row are the three worst fits, and in the bottom row are the three best fits. The black line is the single trial power spectrum. The blue line is the aperiodic fit and in red is the full model fit.
1.2 – Theta frequency ranges
As stated in the discussion (section 4.3), theta power has been described in a variety of different frequency ranges (Adam et al., 2015, 2018; Brzezicka et al., 2015; Jensen & Tesche, 2002). Using a frequency range from 4 to 7 Hz for finding peaks with FOOOF has a harder cut-off frequency than when using a band width filter with the same range. Because filters will pick up power from neighboring frequencies as well, whereas this is not the case with FOOOF. When using a range of 4 to 7 Hz with FOOOF, there was no significant difference in relative theta power between good and poor performance. However, expanding the theta range 1 Hz (4 – 8 Hz) gives different results (supplementary figure 3). Here, the relative power is higher for good performance, compared to poor performance (W = 122, p = 0.031, d = 0.595). Also, relative power is significantly increased from baseline during good performance (t(16) = 2.549, p = 0.021, d = 0.618), but not during poor performance. Thus, it seems most of the theta activity that explains behavior is in the higher frequencies within that range. So perhaps it would be better to define a frequency band based on the data (Jensen & Tesche, 2002).
Supplementary figure 3: Theta power measured between 4 and 8 Hz predicts performance. Participants in this group all exhibited some degree of theta power. A) Power spectra of the baseline period and good and poor performance during the retention period. B) Relative theta power is significantly increased compared to baseline. And is significantly higher than during poor performance.
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Issue 10
Structural Magnetic Resonance Imaging (MRI) and neuropsychological correlates in an elderly non-Western immigrant population
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What happens to a troll in daylight? Interpersonal affect regulation and empathic
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