{"id":24,"date":"2015-11-19T15:10:46","date_gmt":"2015-11-19T06:10:46","guid":{"rendered":"http:\/\/www.cns.atr.jp\/decnefpro\/?page_id=24"},"modified":"2023-01-13T16:17:05","modified_gmt":"2023-01-13T07:17:05","slug":"mieko-2","status":"publish","type":"page","link":"https:\/\/bicr.atr.jp\/decnefpro\/","title":{"rendered":"Top"},"content":{"rendered":"

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Decoded Neurofeedback Project
\nwithin Strategic Research Program for Brain Sciences (SRPBS),
\nBMI Technology Application of DecNef for development of diagnostic and care system for mental disorders and construction of clinical application bases<\/h2>\n

Consortium<\/span><\/h2>\n

In 2013, around 60 neuroscientists, neuropsychiatrists, engineers, computational scientists, and psychologists from eight Japanese universities and institutes formed a consortium as part of the Japanese Strategic Research Program for the Promotion of Brain Science (SRPBS) for big data applications, machine-learning algorithms, and sophisticated fMRI neurofeedback methods1-3<\/sup> for the diagnosis and treatment of multiple psychiatric disorders. Many of these techniques are related to brain machine interfaces. SRPBS,4<\/sup>\u00a0 a nation-wide research program for brain science is supported by the Japanese Advanced Research and Development Programs for Medical Innovation (AMED). Here, we explain the consortium’s organization, its data collection and sharing projects, its sophisticated neurofeedback projects, the construction of biomarkers for multiple psychiatric disorders, therapy based on sophisticated neurofeedback methods, and the biological dimensions for the spectrum of multiple disorders.<\/span><\/p>\n

Organization chart
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The consortium consists of eight Japanese universities and institutes (Figs. 1 and 2), and approximately 60 researchers participate (<\/span>see list of participants<\/a><\/span>).<\/span><\/p>\n

Group Leader: Mitsuo Kawato (ATR Brain Information Communication Research Laboratory Group),<\/span> members<\/span>
\n<\/a>Sub-Group Leaders:<\/span><\/span>
\n Noriaki Yahata, Tsuyoshi Araki (Tokyo University),<\/span> members<\/a><\/span><\/span>
\n Masamichi Sakagami\u00a0 (Tamagawa University),<\/span> members<\/a><\/span><\/span>
\n Hidehiko Takahashi (Kyoto University),<\/span> members<\/a><\/span><\/span>
\n Youichi Saitoh (Osaka University),<\/span> members<\/a><\/span><\/span>
\n Yasumasa Okamoto (Hiroshima University),<\/span> members<\/a><\/span><\/span>
\n Nobumasa Kato (Showa University),<\/span> members<\/a><\/span><\/span><\/p>\n

From 2013\u301c (MEXT and AMED)<\/span>
\n\"Figure1_0123\"<\/a>
\nFigure 1<\/span><\/span><\/p>\n

\"Figure2_0123\"<\/a>
\nFigure 2<\/span><\/span><\/p>\n

Data collection and sharing
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The neuroimaging data of the patients and the control participants were collected at the sites shown in Figure 3. A coherent protocol was designed and used for the resting-state functional magnetic resonance imaging (rs-fMRI). 5 \u00a0<\/sup> The following table 1 shows the number of samples collected for each disorder at each site. We used altogether 14 scanners from three manufacturers. The data is available from this site<\/span><\/a>. More than 400 scans were obtained for 9 traveling subjects who visited 12 scanners as shown in Table 2.<\/span><\/p>\n

\"26348555_draw_lzw\"<\/a>Figure 3
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\"\"Table 1<\/span><\/p>\n

\"\"Table 2<\/span><\/p>\n

DecNef and FCNef<\/span><\/h2>\n

The brain is not merely an input-output information transformation system; it’s also a dynamical system that generates spontaneous spatiotemporal patterns even without sensory inputs, executed movements, or cognitive tasks. These spontaneously generated patterns by brain dynamics are called spontaneous brain activities in experimental animals and resting-state brain activities in humans. The resting-state brain activity of an individual contains rich information about age, cognitive capability, mental disorders, etc. By combining the information decoded from brain activity and its neurofeedback in reinforcement learning paradigms, we can unconsciously control brain activity patterns that correspond to specific information. This leads to therapies for psychiatric disorders3<\/sup>, unconscious manipulation of facial preferences6<\/sup>, color-orientation association7<\/sup>, fear memory reduction8<\/sup>, confidence in decision making9,10<\/sup>, and an increase of cognitive capability. Decoded neurofeedback (DecNef)1 <\/sup>has been applied to obsessive compulsive disorder (OCD) and central chronic pain11<\/sup> (Fig. 4). Functional connectivity neurofeedback (FCNef)2<\/sup> has been applied to the autism spectrum disorder (ASD), major depressive disorder (MDD)12<\/sup>, and schizophrenia (SCZ) (Fig. 5), (Table 3).<\/span><\/p>\n

\"Table2-2\"<\/a>Figure 4<\/span><\/p>\n

\"Figure4-2\"<\/a>Figure 5<\/span><\/p>\n

\"Table2_0123\"<\/a>
\n<\/a>Table 3<\/span><\/p>\n

Effect sizes in DecNef and FCNef studies<\/span><\/h2>\n

Here, effect sizes in our 9 DecNef and FCNef studies are described. For a review, please see Watanabe et al., Trends in Cognitive Sciences<\/em>, 2017. Note that in previously published version of datasets in the review Cohen’s d<\/em> or Cohen’s dz<\/sub><\/em> was shown as an effect size of within-subject changes for each study. Here we show only Cohen’s dz<\/sub><\/em>. The reason that Hedges’ g<\/em> is shown for behavioral data in Yamashita et al., Cereb Cortex<\/em>, 2017 is that the numbers of subjects from the groups compared were not the same.<\/span><\/p>\n

Calculation of effect sizes<\/h5>\n

Table 4 (below) summarizes types of effect sizes used for each of the 9 DecNef\/FCNef studies shown in Table 5. In the following sections, we show specific formulas for the calculation of effect sizes. Please refer to Lakens, Front Psychol<\/em>, 2013 for further details of the calculation of the effect sizes.<\/span><\/p>\n

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a<\/sup> The ogirinal paper used Cohen’s d<\/em> (corrected for dependence between the means). Here Cohen’s dz<\/sub><\/em> is used to preserve consistency with other studies shown on this table.<\/span><\/p>\n

Table 4<\/span><\/span><\/p>\n

Table 4<\/strong>. Methods for calculation of effect sizes in each study. Each row corresponds to each study using DecNef or FCNef shown in Table 5. The left column shows the name of the first author and published year for each study. The right 2 columns indicate types of effect sizes for changes in neurofeedback scores and behaviors.<\/span><\/p>\n

Cohen’s dz<\/sub><\/em> was defined as:<\/span><\/p>\n

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where the numerator represents the difference between the mean (Mdiff<\/sub><\/em>) of the difference scores (Xdiff<\/sub><\/em>) and the comparison value (e.g., 0), and the denominator represents the standard deviation of the difference scores. N<\/em> indicates the number of samples.<\/span><\/p>\n

Hedges’ g<\/em> was defined as:<\/span><\/p>\n

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where n1<\/sub><\/em> and n2<\/sub><\/em> represent the numbers of samples for 2 groups, respectively. ds<\/sub><\/em> is a type of Cohen’s d<\/em>, defined as:<\/span><\/p>\n

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In this formula, the numerator represents the difference between mean of the 2 groups of observations. The denominator represents the pooled standard deviation.<\/span><\/p>\n

Raw datasets used for the calculation of effect sizes<\/h5>\n

From the following button, one can download an .zip file that contains raw numbers used for the calculation of effect sizes for each of 9 DecNef\/FCNef studies.<\/span><\/p>\n

\"\"\u00a0\u00a0Download ZIP<\/a><\/span><\/p>\n\n\n\n
\"\"<\/td>\nThis work is licensed under a Creative Commons Attribution 2.5 License<\/a><\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
Table 5. Development and applications of DecNef and FCNef in chronological order.<\/h5>\n

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a<\/sup> N\/A, not available
\nb<\/sup> The authors published another paper (Cortese et al., NeuroImage<\/em>, 2017) by a different data analysis with a different purpose.<\/span><\/p>\n

Table 5<\/span><\/span><\/p>\n

    \n
  1. Shibata, K. et al. (2011) Perceptual learning incepted by decoded fMRI neurofeedback without stimulus presentation. Science<\/em> 334<\/strong>, 1413\u20131415<\/span><\/li>\n
  2. Megumi, F. et al. (2015) Functional MRI neurofeedback training on connectivity between two regions induces long-lasting changes in intrinsic functional network. Front. Hum. Neurosci.<\/em> 9<\/strong>, 160<\/span><\/li>\n
  3. Amano, K. et al. (2016) Learning to associate orientation with color in early visual areas by associative decoded fMRI neurofeedback. Curr. Biol.<\/em> 26<\/strong>, 1861\u20131866<\/span><\/li>\n
  4. Shibata, K. et al. (2016) Differential activation patterns in the same brain region led to opposite emotional states. PLoS Biol.<\/em> 14<\/strong>, e1002546<\/span><\/li>\n
  5. Koizumi, A. et al. (2016) Fear reduction without fear through reinforcement of neural activity that bypasses conscious expo- sure. Nat. Hum. Behav.<\/em> 1<\/strong>, 0006<\/span><\/li>\n
  6. Cortese, A. et al. (2016) Multivoxel neurofeedback selectively modulates confidence without changing perceptual performance. Nat. Commun.<\/em> 7<\/strong>, 13669<\/span><\/li>\n
  7. Yamada, T. et al. (2017) Resting-state functional connectivity- based biomarkers and functional MRI-based neurofeedback for psychiatric disorders: a challenge for developing theranostic biomarkers. Int. J. Neuropsychopharmacol.<\/em> 20<\/strong>, 769\u2013781<\/span><\/li>\n
  8. Yamashita, A. et al. (2017) Connectivity neurofeedback training can differentially change functional connectivity and cognitive performance. Cereb. Cortex.<\/em> 27<\/strong>, 4960\u20134970<\/span><\/li>\n
  9. Tascereau-Dumouchel, V. et al. (2018) Towards an unconscious neural-reinforcement intervention for common fears. Proc Nat Acad Sci USA.<\/em> 115<\/strong>, 3470-3475<\/span><\/li>\n<\/ol>\n
    How to download data from the DecNef Data Repository (DecNef DR)<\/b><\/h5>\n

    We invite investigators to apply for access to the DecNef DR. Before sending your application, please read the privacy statement. The use of data from the DecNef DR is allowed for noncommercial purpose only. Please note that we collect the number of applicants and size of the data downloaded from the database for reports to our funding agencies (ImPACT, ERATO, etc), without private information.
    \nWe kindly ask you to email us the following form of “Declaration of Consent”.<\/span><\/p>\n