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A dual-brain therapeutic approach using noninvasive brain stimulation based on two-person neuroscience: A perspective review


  • Received: 17 October 2023 Revised: 06 February 2024 Accepted: 19 February 2024 Published: 04 March 2024
  • Our actions and decisions in everyday life are heavily influenced by social interactions, which are dynamic feedback loops involving actions, reactions, and internal cognitive processes between individual agents. Social interactions induce interpersonal synchrony, which occurs at different biobehavioral levels and comprises behavioral, physiological, and neurological activities. Hyperscanning—a neuroimaging technique that simultaneously measures the activity of multiple brain regions—has provided a powerful second-person neuroscience tool for investigating the phase alignment of neural processes during interactive social behavior. Neural synchronization, revealed by hyperscanning, is a phenomenon called inter-brain synchrony- a process that purportedly facilitates social interactions by prompting appropriate anticipation of and responses to each other's social behaviors during ongoing shared interactions. In this review, I explored the therapeutic dual-brain approach using noninvasive brain stimulation to target inter-brain synchrony based on second-person neuroscience to modulate social interaction. Artificially inducing synchrony between the brains is a potential adjunct technique to physiotherapy, psychotherapy, and pain treatment- which are strongly influenced by the social interaction between the therapist and patient. Dual-brain approaches to personalize stimulation parameters must consider temporal, spatial, and oscillatory factors. Multiple data fusion analysis, the assessment of inter-brain plasticity, a closed-loop system, and a brain-to-brain interface can support personalized stimulation.

    Citation: Naoyuki Takeuchi. A dual-brain therapeutic approach using noninvasive brain stimulation based on two-person neuroscience: A perspective review[J]. Mathematical Biosciences and Engineering, 2024, 21(4): 5118-5137. doi: 10.3934/mbe.2024226

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  • Our actions and decisions in everyday life are heavily influenced by social interactions, which are dynamic feedback loops involving actions, reactions, and internal cognitive processes between individual agents. Social interactions induce interpersonal synchrony, which occurs at different biobehavioral levels and comprises behavioral, physiological, and neurological activities. Hyperscanning—a neuroimaging technique that simultaneously measures the activity of multiple brain regions—has provided a powerful second-person neuroscience tool for investigating the phase alignment of neural processes during interactive social behavior. Neural synchronization, revealed by hyperscanning, is a phenomenon called inter-brain synchrony- a process that purportedly facilitates social interactions by prompting appropriate anticipation of and responses to each other's social behaviors during ongoing shared interactions. In this review, I explored the therapeutic dual-brain approach using noninvasive brain stimulation to target inter-brain synchrony based on second-person neuroscience to modulate social interaction. Artificially inducing synchrony between the brains is a potential adjunct technique to physiotherapy, psychotherapy, and pain treatment- which are strongly influenced by the social interaction between the therapist and patient. Dual-brain approaches to personalize stimulation parameters must consider temporal, spatial, and oscillatory factors. Multiple data fusion analysis, the assessment of inter-brain plasticity, a closed-loop system, and a brain-to-brain interface can support personalized stimulation.



    Marine biology carbon sink is critical pathway to earned carbon neutrality object. Marine absorbing 1/3 anthropogenic CO2 emitted over the industrial period [1]. Marine CO2 carbon sinks volume gradually increased from 2008–2019 [2]. Greenhouse effect increased surface-ocean CO2 partial pressure. Marine carbon sink function influenced 10–40% carbon sinks capability variation on 1900–2010 [3]. Northern Indian Ocean evidence shown biological pump transforms CO2 partial to inorganic carbon storage in deep ocean, reduced greenhouse effect efficiently [4]. Marine carbon sink capability is susceptible and alterable. Mesoscale ocean fronts upwelling increased carbon particle vertical transport and sequestration capability [5]. Anthropogenic CO2 emitted to aggravate seawater acidulated. Seawater acidulated dissolution marine organism carbonates shell and also reduced marine organism carbonate storage carbon capability. Atmosphere-marine carbon cycles efficiently is critical factor of global ecological system sustainable operated [6]. Marine carbon sinks also will influence global political. Global marine carbon sinks are future international climate negotiations vital topic [7]. Marine carbon sinks process faced multiple challenges. Marine carbon sinks poor data limit relevant research. The Southern Ocean data-poor problem regions restrict air-ocean CO2 fluxes research [8]. As same time, marine carbon sinks research ought to attention region characteristic. Regional-scale studies is significantly in Continental shelf air-sea CO2 fluxes capability problem [9]. Literature review evidence has confirmed marine carbon sinks function. However marine carbon sinks capability influence by numerous external factors. Therefore, regional marine biological carbon sinks capability review is vital to alter warming trend.

    Carbon sinks fishery is a well-qualified ecological and economic value biological carbon sinks system. Algae and shellfish are important fishery carbon sinks resources. Phototrophic dinoflagellates, cryptomonads, diatoms, heterotrophic bacteria and heterotrophic dinoflagellates algae can efficiently absorb and storage carbon from atmosphere [10]. Shellfish absorb carbon and other nutrient with shell and biological tissue from marine ecosystem to reduce seawater carbon concentration. Seawater carbon negative pressure formed new carbon sink power. Shellfish rope farming model carbon sinks capability is considerably over bottom seeding breeding model [11]. Norway proof showed cod and algae fishery source management optimize bring ecological and economic win-win result [12]. Algae and shellfish polyculture method carbon sediment rate is obviously higher than other regions in Sanggou bay [13]. In marine ecosystem, small copepod ingests storage enormous carbon and transport to metazoan [14]. Mangroves and seagrass are important tool to combating greenhouse effect, reasonable accurately assess carbon sequestration rate has benefit to incent ecosystem sustainable develop [15]. Marine ecosystem pollution will lead biology carbon sinks performance decreasing. In estuary habitat, marine pollutant accumulated in black rockfish from food chain. Yaquina Bay estuary habitat environment change will have negative influence on ocean organism nursery function [16]. Coral ingests micro plastics cause corals feeding impairment, microbiome gene expression error and appearance construction changed [17]. Shellfish carbon sinks capability has seasonal variation characteristics [18]. Ecosystem relies on specific environment condition. Environmental change sensitivity heterogeneity caused marine organism carbon sinks capability changed on different regional [19]. Therefore, algae and shellfish carbon sinks capability study not only helpful for earned carbon neutrality object, but also have benefit of solving marine environment comprehensive management problem. Algae and shellfish carbon sinks capability is alterable. Algae and shellfish carbon sinks capability were more sensitive to environment. Hence algae and shellfish carbon sinks capability forecast is a difficulty problem.

    Algae and shellfish statistical data are limited. Algae and shellfish carbon sinks capability influence mechanism is not entirely identified. Hence Algae and shellfish carbon sinks capability forecast is a grey problem. Grey problem used in poor information environment and not entirely known problem typically. Deng definition grey problem concept means half known and half unknown problem [20]. Lei et al. built a training learned prediction grey model according to neural ordinary differential equations [21]. Kang et al. change tradition accumulation and derivative orders from constants to functions. Variable order fractional model can accurately predict complex system characteristic [22]. In complex network perspective, Xie et al. proposed generalized conformable fractional grey model and describe grey model physical meaning [23]. Jiang et al. improve a nonlinear grey Bernoulli model and use whale optimization algorithm calculate grey Bernoulli model parameters [24]. Grey model has widely used in environment source development and protection. Yu considers photovoltaic engineering has high flexibility time-delayed power effect characteristic. Grey model applies to photovoltaic have accurately prediction result [25]. Xu et al. proposed conformable fractional accumulation grey model to analyze energy consumption and carbon dioxide emission relationship [26]. Grey model also used in assessing PM2.5 seasonal fluctuation and marine fleets CO2 emissions [27,28]. Tu & Chen forecast pubic attention to air pollution according to designing a new unequal adjacent grey model [29]. Ding et al. [30,31,32] presented a series of grey models to analyzing nuclear energy consumption, photovoltaic power generation and new energy vehicles sale problem. The forecast accuracy results confirmed the new grey model has the highest forecasting precision, small results empirical volatility, and outcome generalizability characteristics. Wang et al. [33] presented a multivariate forecasting model to analyzed energy consumption influenced factor, the result showed new grey model had better forecast precision. Zeng et al. [34,35] applied improved grey system model to predicted China coalbed methane production, and realized shale China gas production scientific prediction based on the new information priority principle combined with grey buffer operator technology.

    Previous studies have provided algae and shellfish carbon sinks function is vital to control greenhouse effect. However, less researcher applies grey model to forecast fundamental fishery product like algae and shellfish carbon sinks capability alteration trend. Algae and shellfish carbon sinks capability influence factors are incomplete full known. Algae and shellfish carbon sinks capability correlation research data also in poor information situation. Therefore, the study applies FGM to analyze algae and shellfish carbon sinks capability. In the research, the major innovation as follow: 1) Algae and shellfish carbon sinks capability analyzes and forecast. Algae and shellfish marine carbon sinks are artificial enhance carbon sinks method. Algae and shellfish carbon sinks capability analyze will appraise algae and shellfish carbon sinks capability and develop trend. 2) Research result can provide policy advice for future marine carbon sinks fishery industry to enhance marine carbon sinks fishery policy scientific.

    This study is organized as follows: Section 2 analyzes algae and shellfish carbon sinks capability mechanism. Section 3 provides data detail and grey model method choice. Section 4 analyzes study result according to geographic position. Section 5 drew conclusion and implication from study result.

    Algae and shellfish are important marine economic merchandise. Previous research has testified algae and shellfish can sinks enormous carbon from atmosphere. Algae and shellfish merchandise are nutrient rich, is very important seafood of China. As Chinese traditional marine product, algae and shellfish have high market recognition degree. Algae and shellfish being farmed have a protracted history. Seawater breeding algal and shellfish technology are mature. Algae and shellfish breed may also achieve ecology benefit. Algae and shellfish breed and catch can remove marine ecology interior carbon source and create a sustainable marine carbon sinks system. Coastal area algae and shellfish breeding can achieve economic and ecological win-win result.

    Marine carbon sinks fishery is breed marine organisms carbon sinks function to extend marine carbon sinks capability. Marine carbon sinks fishery is considerably totally different to traditional fishery. Marine carbon sinks fishery aims to increasing marine carbon sinks capability. Algae is primary producer in marine ecosystem. Influence by photosynthesis, algae use inorganic carbon and nutrients in seawater to build biological tissues. Algae absorbs carbon from seawater can reduce surface-ocean CO2 partial pressure promote atmosphere carbon dioxide dissolved in marine. Additionally, algae breeding forestalls seawater acidification.

    Shellfish also play an important role in marine carbon sinks function. shellfish shells use carbon element to form calcium carbonate. Shellfish also requires carbon to create soft tissue [36]. Shellfish and algae unite breed will efficiency improve fishery economy and environment performance. Algae provides survival necessary bait to shellfish. Shellfish excretory product provides abundant nutrients for algae upgrowth. Algae and shellfish comprehensive carbon sink function significant. Algae carbon content is 20–35% of algae net weight. Shellfish soft tissue carbon content is 26–42% soft tissue netted weight. Shellfish shell carbon content is 11–13% of shell net weight [37]. Shellfish tissue forms process need to converge carbon in marine element. Shellfish soft tissue and shell storage huge carbon element from marine. Therefore, shellfish breed and catch are vital pathway to increase marine carbon sink capability.

    Marine carbon sinks fishery is increasing marine ecosystems carbon sinks capability vital anthropogenic. Marine organisms will storage carbon in internal tissue. Marine environment change has knock-on effect on Marine organism [38]. Algae and shellfish have crucial role in marine carbon sinks system. Photosynthesis dissolved CO2 to inorganic in seawater. Atmosphere CO2 transforms CO3 in seawater. Photosynthesis provides necessary energy for algae grow. Algae photosynthesis transforms H2O and CO2 into the carbohydrates and energy. Shellfish is feed by algae, plankton, and small copepod. Shellfish predation processes to shift other organism carbon in shellfish tissue internal. Algae and shellfish death and deposition cause organism tissue interior carbon form changed. Algae and shellfish death tissue carbon part transformed into particulate organic carbon form and permanently trapped in ocean floor. The other carbon transformed into dissolved organic form of storage carbon. Marine microbial carbon pumps transform dissolved organic carbon into refractory dissolved organic carbon form and transferred to deep-sea storage. Marine environment changes influence biological pumps transform and storage carbon efficient. Marine environment reason like temperature and climate affect algae and shellfish carbon sinks capability. Marine pollution and anthropogenic improper intervention inhibit ocean carbon sinks capability. Algae and shellfish carbon sinks mechanism is shown in Figure 1.

    Figure 1.  Algae and shellfish carbon sinks mechanism.

    As show in Figure 2, China 9 coastal provinces latitude span widely. China 9 coastal provinces climate different. Northernmost Liaoning province is medium latitudes monsoon climate. Liaoning province average temperature is higher than 0 ℃. Liaoning four seasons distinct, summer is hot and rainy, winter is cold and dry. Southernmost Hainan province is tropical monsoon climate. Tropical monsoon climate area has high temperature throughout year, average temperature above 22 ℃, the coldest month above 18 ℃. Other provinces climate characterizes also different. Climate influences coastal provinces temperature, precipitation and marine environment significantly. China coastal province is the busiest economic belt. Coastal area marine traffic, urbanization, export-oriented economic, anthropogenic activity intimate related to marine environmental quality. China has achieved great success in economy development. But China 9 coastal provinces industry, environment and climate reasons lead algae and shellfish carbon sinks capability different significant. Hence, it is necessary to analyze and forecast China 9 coastal provinces carbon sinks capability respectively.

    Figure 2.  China 9 coastal province geographical location.

    The research study China 9 coastal provinces algae and shellfish carbon sinks capability trend for three reasons:

    1) Geographical location leads marine biological community China 9 coastal provinces distinction considerably. Biological community and fishery industry pattern decide marine carbon sinks capability. Temperature and precipitation impact to surface ocean CO2 partial pressure and seawater dissolved CO2 in atmosphere efficiency. Temperature and illumination also impact organic carbon particulate dissolved, transform and storage. China 9 coastal provinces algae and shellfish carbon sinks capability study has benefit to marine ecology producer and consumer carbon sinks capability research.

    2) China 9 coastal provinces industry pathway is different. Anthropogenic industrial behavior affects marine environment. Marine natural environment and anthropogenic effect marine carbon sinks capability. China 9 coastal provinces algae and shellfish carbon sinks capability study will have benefit to know algae and shellfish in differently environment regional. The study will also assess industrial pattern influence algae and shellfish carbon sinks capability.

    3) Coastal province marine carbon sinks capability studies are important to enhance regional environment policy rationality. Algae and shellfish carbon sinks capability study can help to quantify analyze algae and shellfish carbon sinks prospect. Government administrator can formulate carbon discharge task in comprehensively economic society influence factor.

    Algae and shellfish marine catch amount and breed amount reflect regional marine carbon sinks capability. Organism weight and carbon sinks coefficient can estimate algae and shellfish carbon sinks capability. Marine algae and shellfish catch and breed amount can reflect coastal regional algae and shellfish resource carbon sinks capability sufficiently. According review previous research [39], algae and shellfish carbon sinks capability can estimate according to organisms' weight and carbon sink ratio. The study chose organism weight to analyze and forecast algae and shellfish carbon sinks capability. Algae and shellfish carbon sinks capability calculate method as shown:

    1) Total carbon sinks capability

    Ct=Ca+Cs (1)

    Ct mean algae and shellfish total carbon sinks capability in 9 coastal provinces; Cc means algae carbon sinks capability; Cs means shellfish carbon sinks capability.

    2) Algae carbon sinks capability

    Ca=(Ca, c+Ca, b)ωaζa (2)

    Ca, c means algae caught amount; Ca, b means algae breed amount; ωa means algae dry weight/total weight coefficient, algae weight/total weight coefficient is 20%; ζa means algae carbon sinks coefficient. Algae carbon sinks coefficient is 0.27.

    3) Shellfish carbon sinks capability

    Cs=Cf+CtCf=(Cs, c+Cs, b)ωsθfζs, f
    Ct=(Cs, c+Cs, b)ωsθsζs, s (3)

    Cf means shellfish shell carbon sink capability; Ct means shellfish soft tissue carbon sink capability; Cs, c means shellfish caught amount; Cs, b means shellfish breed amount; ωs means shellfish dry weight/total weights, shellfish dry weight/total weights value is 65%; θf means shellfish shell dry weight/total dry weight coefficient, shellfish shell weight/total weights coefficient is 93%; ζs, f means shellfish carbon sinks coefficient. shellfish shell carbon sinks coefficient is 0.13;θs means shellfish soft tissue dry weight/total dry weight coefficient, shellfish soft tissue weight/total weight coefficient is 7%; ζs, s means algae carbon sinks coefficient. Algae coefficient carbon sinks coefficient is 0.46.

    China 9 coastal provinces algae and shellfish marine catch and breed amount were collected in Chinese fishery Statistical yearbook from 2011–2020. Chinese fishery Statistical yearbook has one year hysteresis reflect algae and shellfish marine catch amount statistics value from 2011 to 2019. According calculation China 9 coastal provinces algae and shellfish carbon sinks capability statistical data in Table 1.

    Table 1.  China 9 coastal provinces algae and shellfish carbon sinks capability.
    carbon sinks capability(t) liaoning hebei shandong jiangsu zhejiang fujian guangdong guangxi hainan total
    2011 208519.3 28962.65 368974.8 73145.42 70474.74 260582.3 193457.7 73908 6537.983 1280036
    2012 235663.4 35913.47 386483.1 74662.77 66694.82 267185.4 195793.9 77663.6 7081.066 1342073
    2013 253459.2 42808.37 402419.4 77429.35 73838.7 281826.7 200471.2 60329.07 7593.667 1400386
    2014 258806.6 46701.99 421257.3 76063.46 76031.81 298459.2 203240.1 84777.53 9100.467 1467352
    2015 262659.4 47983.84 440217 71664.82 79402.67 313269.3 206784.9 89551.56 9546.095 1513548
    2016 277998.6 47771.78 456897.8 72861.71 86442.39 333125.3 209326.5 94336.34 9187.803 1580776
    2017 270177.3 49213.55 461592.6 74175.32 98480.48 340158.8 195013.3 98221.31 8422.673 1589050
    2018 252223.8 45023 463186.6 72827.92 102356 365614.4 197581.1 102705.2 5631.969 1603536
    2019 252963.9 37806.72 440084.8 72575.33 107386.9 389836.5 199768.6 106054.9 4393.08 1608497

     | Show Table
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    Wu et al. [40] present fractional order accumulation to better reflect new information priority theory. Compared with traditional grey model, fractional order accumulation grey model introduced fractional order to prevent traditional small sample grey model forecasts outcome error. FGM (1,1) deals method is:

    Step1: Assume non-negative sequence X(0)={x(0)(1),x(0)(2),,x(0)(n)}, For ζ(0,3], (According to particle swarm optimization test optimum range), ζ order accumulation generating operator (ζ-FGM) is defined as follows:

    x(ζ)(k)=ki=1Ckiki+ζ1x(0)(i),k=1,2,,n

    Where C0ζ1=1;Ck+1k=0;Ckiki+ζ1=(ki+ζ1)(ki+ζ2)(ζ+1)ζ(ki)!.

    When ζ = 1, Ckiki+ζ1=Ckiki=1 ζ-FGM is defined as x(1)(k)=ki=1x(0)(i) equal to traditional 1-AGO grey model in GM (1,1).

    Step 2: FGM (1,1) model establish as:

    dx(ζ)dt+ax(ζ)=b;x(ζ)(1)=x(0)(1)

    In FGM (1,1) model a, b are parameters and u is the control parameter. Solution FGM (1, 1) parameters with least-squares

    [ˆaˆb]=(BTB)1BTY

    where

    B=[x1(ζ)(1)+x1(ζ)(2)21x1(ζ)(2)+x1(ζ)(3)21x1(ζ)(n1)+x1(ζ)(n)21]
    Y=[x1(ζ)(2)x1(ζ)(1)x1(ζ)(3)x1(ζ)(2)x1(ζ)(n)x1(ζ)(n1)].

    Step 3: FGM (1,1) approximate function is:

    ˆx(ζ)(k+1)=(x(0)(1)ˆbˆa)eˆak+ˆbˆa

    Step 4: FGM (1,1) inverse accumulated generating operator in:

    X(ζ)={x(ζ)(1),x(ζ)(2),,x(ζ)(n)}
    α(r)x(0)={α(1)ˆx(1r)(1),α(1)ˆx(1r)(2),α(1)ˆx(1r)(3),,α(1)ˆx(1r)(n)}

    Thus, the fitting values are ˆx(0)(ζ)=ˆx(1)(ζ)ˆx(1)(ζ1).

    Grey model means absolute percentage error (MAPE) can use to reflect actual value and predicted value error. Low degree MAPE values to reflect perfect forecast precision. MAPE calculates formula is:

    MAPE=Σnt=1|(AtFt)At|n×100%

    For better understanding FGM (1,1) solution principle, FGM (1,1) analyze and forecasting process as shown in Figure 3.

    Figure 3.  Algae and shellfish carbon sink capability forecasting process.

    FGM (1,1) model forecasting algae and shellfish carbon sinks capability need to evaluate model availability firstly. Apply FGM (1,1), GM (1,1) and DGM (1,1) model evaluate China 9 coastal provinces total algae and shellfish carbon sinks. The forecast result as shown in Figure 4.

    Figure 4.  Grey model forecast precision result.

    FGM (1,1) new information priority target realized according to fractional accumulation. Particle swarm optimization applied to optimize fractional-order (r = 0.0633). Algae and shellfish carbon sinks capability apply FGM (0.0633) (1,1) forecast MAPE is 0.51%. Forecast result superior than GM (1,1) (MAPE is 2.7%) and DGM (1,1) (MAPE is 1.7%). Hence, FGM (1,1) is more suitable to forecast algae and shellfish carbon sinks capability. Therefore, FGM (1,1) applied to empirical study algae and shellfish carbon sinks capability.

    As show in Figure 5 north coastal provinces algae and shellfish carbon sinks capability on downward trend. According to north coastal province shellfish and algae catch and breed statistics data: Liaoning, Hebei and Shandong three provinces future four years forecast trend will continue decrease. Liaoning shellfish caught and breed amount enormous. In 2016 Liaoning algae and shellfish catch and breed carbon sinks capability has a precipitous decrease. Liaoning proof showed marine carbon sinks capability environment recovered process slowly. Marine carbon sinks modernization is a complexity economic and ecological problem. Industry activity not only effects tradition forest carbon sinks capability, but also effect marine carbon sinks capability. Marine carbon sinks capability recovered must govern environment pollution comprehensively. Through Section 3 Liaoning FGM (1,1) approximate function is:

    ˆx(0.665)(k+1)=(x(0)(1)2149090)e0.0893k+2149090
    Figure 5.  North coastal provinces FGM (1,1) forecast result.

    FGM (1,1) forecast future four years algae and shellfish carbon sinks capability result show Liaoning will still in decrease trend in future four years. The trend means anthropogenic activity frequently caused marine carbon sinks capability decreased important factor. Liaoning balance economic develops and ecological protect are realize social high-quality development vital task. If Liaoning to continue neglect marine carbon sinks capability recovers, Liaoning algae and shellfish carbon sinks capability will continue damage.

    Hebei province algae and shellfish carbon sinks capability also in decrease trend. Algae and shellfish carbon sinks capability decrease in 2018 and 2019 sharply. Marine environment changes lead algae and shellfish catch and breed amount decreased. Bohai bay environmental pollution reduced algae and shellfish carbon sinks capability. Hebei province carbon sinks capability decreased with jin-jing-ji speedily economy developed. Hebei province blends in capital economic circle is an important advance opportunity. But capital economic circle changed Hebei province fishery economy model. Jin-jing-ji integration drives more fishermen to other job. The Bohai bay chiefly fishery function was replaced by commerce and transport function. Tradition fishery faced transformation and upgrading pressure. Algae and shellfish catch and breed carbon sinks capability decreased. Through Section 3 FGM (1,1) approximate function is:

    ˆx(0.38)(k+1)=(x(0)(1)122872.9)e0.2431k+122872.9

    FGM (1,1) forecast future four years algae and shellfish carbon sinks capability result show Hebei province future carbon sinks in decrease trend. It means economic transformation process also changed carbon sinks capability. As tradition coal consumption province, Hebei carbon sinks pressure seriously. The study treads shown if Hebei province ignores algae and shellfish carbon sinks capability function, Hebei carbon sinks capability will continuous declination.

    Shandong province algae and shellfish carbon sinks capability continue increase from 2011–2018 but decrease in 2019. Shandong province algae and shellfish carbon sinks organism community decrease influence Shandong marine carbon sinks capability. Algae and shellfish organism community catch and breed decrease in 2019 means Shandong province fishery industry transform and upgrade decrease algae and shellfish carbon sinks function. Heavy industry CO2 discharged sinks by algae and shellfish hardly. Through Section 3 FGM (1,1) approximate function is:

    ˆx(0.248)(k+1)=(x(0)(1)944486)e0.2028k+944486

    FGM (1,1) forecast future four years algae and shellfish carbon sinks capability result show Shandong province carbon sinks capability in a fluctuate trend, future carbon sinks capability faced decrease risk. Carbon neutralized pressure drove Shandong province paid highly attention to marine carbon sinks function. Shandong province transform economy advance model and applies cleaner energy to reduce carbon sinks pressure. But fish culture structure changed marine algae and shellfish carbon sink capability. Shandong province marine primary industry ratio decreasing means more resources is tilted towards other industries. Hence, future Shandong province algae and shellfish carbon sinks capability will in decrease trend.

    As show in Figure 6, middle coastal provinces algae and shellfish carbon sinks capability forecast result in general increase. According to south coastal province shellfish and algae catch and breed statistics data: Jiangsu, Zhejiang, Fujian three provinces future four years forecast trend also shown in Figure 6. Jiangsu, Zhejiang, Fujian are traditional fishery regional. Zhejiang and Fujian algae and shellfish carbon sinks capability in increase trend, but Jiangsu in decrease trend. Jiangsu algae and shellfish carbon sinks decrease significantly. Jiangsu province fishery carbon sinks depend algae and shellfish in low degree. Algae and shellfish carbon sinks capability fluctuate forceful. Through Section 3 FGM (1,1) approximate function is:

    ˆx(0.054)(k+1)=(x(0)(1)83865)e0.713k+83865
    Figure 6.  Middle coastal provinces FGM (1,1) forecast result.

    FGM (1,1) forecast future four years result show Jiangsu province carbon sinks capability in a decrease trend. As traditional abundant place, Jiangsu province algae and shellfish carbon sinks capability are influenced by environment pollution and marine disaster. Algae and shellfish carbon sinks capability in future four years will continue decrease. Therefore, Jiangsu province should control marine pollution and prevent marine disaster seriously.

    Zhejiang province algae and shellfish carbon sinks capability has significance increase. Zhejiang is traditional marine fishery province. Benefit from marine comprehensive management and protect ability, algae and shellfish carbon sinks capability continue increase. Zhejiang province paid high attention to algae and shellfish absorb greenhouse gas function. Zhejiang province had long history to breed and catch algae and shellfish. Algae and shellfish breed have advanced management technology. Nowadays, Zhejiang province algae and shellfish technology advantage enhance regional carbon sink capability. Zhejiang province algae and shellfish carbon sinks capability continue increase from 2011-2019. Through Section 3 FGM (1,1) approximate function is:

    ˆx(0.995)(k+1)=(x(0)(1)+859557)e0.0689k859557

    FGM (1,1) forecast future four years algae and shellfish carbon sinks capability. Forecast results shown Zhejiang province algae and shellfish carbon sinks capability in increase trend. In future four years Zhejiang province algae and shellfish carbon sinks capability will increase sharply. Zhejiang province algae and shellfish will play vital function in sinks atmosphere CO2. Algae and shellfish advanced management technology drive construct more controllable carbon sinks resource in Zhejiang province. Anthropogenic created algae and shellfish carbon sinks resource have efficient and sustainable characteristic.

    Fujian province algae and shellfish carbon sinks capability has significance increase. Fujian province is also a traditional marine fishery province. Marine breed and catch fishery have long history. Fujian province paid high attention to algae and shellfish catch, breed and protect. Fujian province storm surge disaster frequently. Aim to reduce marine disaster cause algae and shellfish carbon sinks capability reduction, Fujian province formulate especially marine disaster contingency plan to protect algae and shellfish from marine disaster damage. Through Section 3 FGM (1,1) approximate function is:

    ˆx(0.998)(k+1)=(x(0)(1)+4712733)e0.052k4712733.

    FGM (1,1) forecast future four years algae and shellfish carbon sinks capability result shown Fujian province algae and shellfish carbon sinks capability in increase trend. Fujian province algae and shellfish breed high efficiently, carbon sink function remarkable. Anthropogenic protected algae and shellfish resource is the reason of carbon sinks capability increase sharply. Fujian province anthropogenic intervenes marine disaster measures to bring healthy growing environment for marine organism, reduce marine disaster damage algae and shellfish carbon sinks capability.

    As show in Figure 7, south coastal provinces include Guangdong, Guangxi and Hainan three provinces. According to south coastal province algae and shellfish catch and breed statistics data Guangdong, Guangxi and Hainan three provinces future four years forecast trend also shown in Figure 7. Guangdong industry and urbanization have occupied marine fishery space. Aquaculture unregulated used drugs to aggravate marine pollution led red tide disasters. Red tide disaster caused algae and shellfish carbon sinks capability decrease seriously. Marine environment repairs to reduce algae and shellfish catch and breed area. Marine environment regulation reduced low quality and high pollution breed area. Therefore, Guangdong algae and shellfish carbon sinks capability decrease. Through Section 3 FGM (1,1) approximate function is:

    ˆx(0.135)(k+1)=(x(0)(1)289647)e0.3486k+289647.
    Figure 7.  South coastal provinces FGM (1,1) forecast result.

    FGM (1,1) forecast future four years algae and shellfish carbon sinks capability result shown Guangdong province algae and shellfish carbon sinks capability in decrease trend. Guangdong-Hong Kong-Macao greater bay area high marine environment standard and environment regulation will continue to reduce algae and shellfish catch and breed area. Traditional simpleness and low-quality algae and shellfish breed area will vanish. In FGM (1,1) forecast result, Guangdong province algae and shellfish catch and breed carbon sinks capability will continue appear decrease trend.

    Algae and shellfish carbon sinks in Guangxi province has continue increase. Guangxi province paid high attention to apply high-quality algae and shellfish breed technology. Guangxi province algae and shellfish catch and breed carbon sinks capability increase from 2014–2019.The phenomenon reflect Guangxi province fishery economic already received remarkable achievement in marine carbon sinks function. At the other side, algae and shellfish carbon sinks need support by high-quality breed technology. Traditional algae and shellfish breed not only decrease carbon sinks capability, but also cause marine pollution problem. Through Section 3 FGM (1,1) approximate function is:

    ˆx(0.486)(k+1)=(x(0)(1)+1526515)e0.0216k1526515.

    FGM (1,1) forecast future four years algae and shellfish carbon sinks capability result show Guangxi province algae and shellfish carbon sinks capability in increase trend. Guangxi province high-quality technology expand anthropogenic breed algae and shellfish biological community. Anthropogenic removed carbon from marine in time can recover marine carbon sinks capability efficiently, prevent marine become new atmosphere CO2 source. In FGM (1,1) forecast result, Guangxi province algae and shellfish catch and breed carbon sinks capability will continue in increase trend.

    Hainan province algae and shellfish carbon sinks capability continue increase from 2011–2015 but decrease in 2016–2019. Hainan province algae and shellfish carbon sinks organism community changed influence Hainan marine biological carbon sinks capability. Algae and shellfish carbon sinks function in Hannan province appear decrease trend. Algae and shellfish proportion in Hainan fishery product is low. Algae and shellfish are not mainly carbon sinks resource. After 2015, Hainan province reduced traditional high pollution breed pattern and low efficiently breed area cause algae and shellfish carbon sinks capability decrease. Through Section 3 FGM (1,1) approximate function is:

    ˆx(0.845)(k+1)=(x(0)(1)87414)e0.096k+87414.

    FGM (1,1) forecast future four years algae and shellfish carbon sinks capability result shown Hainan province algae and shellfish carbon sinks capability in decrease trend. Hainan province algae and shellfish catch and breed will be replaced by other marine carbon sinks fishery production. In FGM (1,1) forecast result, Hainan province algae and shellfish catch and breed carbon sinks capability will continue appear decrease trend.

    Marine carbon sinks function has become important carbon sinks resource. Marine carbon sinks organism like algae and shellfish living environment change will cause carbon sinks capability variation. Marine ecosystem damaged cause algae and shellfish carbon sinks capability descend. Industrial manufacture CO2 released into atmosphere will not storage and sinks by marine organism. Greenhouse effect and environment pollution will format carbon sinks capability descend vicious cycle. Therefore, in order to better understand shellfish and algae carbon sinks capability in different region is meaningful for excavate shellfish and algae carbon sinks potential and formulate future marine carbon discharge policy. The research used FGM (1,1) model to study algae and shellfish carbon sinks capability in 9 China coastal provinces. According to 9 China coastal provinces shellfish and algae carbon sinks capability research outcome. The research has followed conclusion:

    1) Compared with other grey model, FGM (1,1) has more suitable for predict algae and shellfish carbon sinks capability. FGM (1,1) can realize new information first principle according particle swarm optimization confirmed fractional order. FGM (1,1) is more suitable than GM (1,1) and DGM (1,1) to forecast shellfish and algae carbon sinks capability.

    2) Marine ecosystem recovered is a slowly produce. Shellfish and algae carbon sinks capability have remarkable difference in 9 China coastal provinces. In the study, 9 China coastal provinces algae and shellfish carbon sinks capability different obviously. It is phenomenon reflect algae and shellfish carbon sinks function realize it is not only a technology problem, but also correlate to policy and breed culture.

    3) The study reflects algae and shellfish carbon sinks capability different trend in 9 China coastal provinces regional. Algae and shellfish living state will decide their carbon sinks capability in marine ecosystem. If marine environment continues be polluted, algae and shellfish will absorb CO2 in atmosphere hardly.

    The study already comprehensive research 9 China coastal provinces algae and shellfish carbon sinks capability. The study results have benefit to enhance marine carbon sinks capability in the future. The following suggestions can enhance shellfish and algae carbon sinks capability.

    1) Coastal province green development strategic need considering marine carbon sinks function comprehensively. China as the largest developing country. China faced reduction CO2 emission pressure heavily. Marine carbon sink ecosystem recovered will have positive influence to eliminate industry emission CO2 pollution.

    2) Marine carbon sinks capability development needs nationwide to collaborate. Algae and shellfish carbon sinks function realize need capital and technology collaborate. All coastal provinces regions have responsible to protect marine carbon sinks capability. Marine carbon sinks resource values rational reflection and carbon trade rule enact will have benefit stimulate marine carbon sinks resource developed.

    3) Marine carbon sinks capability regains is a system problem. Coastal province should design algae and shellfish carbon sinks resource recovery plan according to marine ecosystem characteristic. Future studies should aim enhance marine carbon sinks breed economic efficiency. Biological carbon sink economic efficiency will influence sinks carbon capability. Marine carbon sinks economical enhance will transfer more resource in optimize ocean carbon sinks capability.

    However, the study still has some limitations. Firstly, the study only researched and forecast algae and shellfish carbon sinks capability. Future studies can complete research other marine organism carbon sinks capability. It will have benefit to improve marine carbon sink mechanism research. Secondly, this study only provides Chinese marine carbon sinks evidence. Future research can research marine carbon sinks capability in other social environment background ulteriorly.

    This study was supported by the Major Social Science Fund Project of China (14ZDB151).

    The authors declare that they have no conflicts of interest.



    [1] R. Hari, M. V. Kujala, Brain basis of human social interaction: from concepts to brain imaging, Physiol. Rev., 89 (2009), 453–479. https://doi.org/10.1152/physrev.00041.2007 doi: 10.1152/physrev.00041.2007
    [2] L. Kingsbury, W. Hong, A multi-brain framework for social interaction, Trends Neurosci., 43 (2020), 651–666. https://doi.org/10.1016/j.tins.2020.06.008 doi: 10.1016/j.tins.2020.06.008
    [3] L. Tsoi, S. M. Burns, E. B. Falk, D. I. Tamir, The promises and pitfalls of functional magnetic resonance imaging hyperscanning for social interaction research, Soc. Pers. Psychol. Compass, 16 (2022), e12707. https://doi.org/10.1111/spc3.12707 doi: 10.1111/spc3.12707
    [4] I. Gordon, S. Wallot, Y. Berson, Group-level physiological synchrony and individual-level anxiety predict positive affective behaviors during a group decision-making task, Psychophysiology, 58 (2021), e13857. https://doi.org/10.1111/psyp.13857 doi: 10.1111/psyp.13857
    [5] V. Reindl, S. Wass, V. Leong, W. Scharke, S. Wistuba, C. L. Wirth, et al., Multimodal hyperscanning reveals that synchrony of body and mind are distinct in mother-child dyads, Neuroimage, 251 (2022), 118982. https://doi.org/10.1016/j.neuroimage.2022.118982 doi: 10.1016/j.neuroimage.2022.118982
    [6] J. Madsen, L. C. Parra, Cognitive processing of a common stimulus synchronizes brains, hearts, and eyes, PNAS Nexus, 1 (2022), pgac020. https://doi.org/10.1093/pnasnexus/pgac020 doi: 10.1093/pnasnexus/pgac020
    [7] L. D. Lotter, S. H. Kohl, C. Gerloff, L. Bell, A. Niephaus, J. A. Kruppa, et al., Revealing the neurobiology underlying interpersonal neural synchronization with multimodal data fusion, Neurosci. Biobehav. Rev., 146 (2023), 105042. https://doi.org/10.1016/j.neubiorev.2023.105042 doi: 10.1016/j.neubiorev.2023.105042
    [8] Y. Pan, G. Novembre, A. Olsson, The interpersonal neuroscience of social learning, Perspect. Psychol. Sci., 17 (2022), 680–695. https://doi.org/10.1177/17456916211008429 doi: 10.1177/17456916211008429
    [9] E. Redcay, L. Schilbach, Using second-person neuroscience to elucidate the mechanisms of social interaction, Nat. Rev. Neurosci., 20 (2019), 495–505. https://doi.org/10.1038/s41583-019-0179-4 doi: 10.1038/s41583-019-0179-4
    [10] L. Schilbach, B. Timmermans, V. Reddy, A. Costall, G. Bente, T. Schlicht, et al., Toward a second-person neuroscience, Behav. Brain Sci., 36 (2013), 393–414. https://doi.org/10.1017/s0140525x12000660 doi: 10.1017/s0140525x12000660
    [11] A. Czeszumski, S. H. Liang, S. Dikker, P. König, C. P. Lee, S. L. Koole, et al., Cooperative behavior evokes interbrain synchrony in the prefrontal and temporoparietal cortex: a systematic review and meta-analysis of fNIRS hyperscanning studies, eNeuro, 9 (2022), ENEURO.0268-21.2022. https://doi.org/10.1523/eneuro.0268-21.2022 doi: 10.1523/eneuro.0268-21.2022
    [12] S. Dikker, L. Wan, I. Davidesco, L. Kaggen, M. Oostrik, J. McClintock, et al., Brain-to-brain synchrony tracks real-world dynamic group interactions in the classroom, Curr. Biol., 27 (2017), 1375–1380. https://doi.org/10.1016/j.cub.2017.04.002 doi: 10.1016/j.cub.2017.04.002
    [13] D. A. Reinero, S. Dikker, J. J. Van Bavel, Inter-brain synchrony in teams predicts collective performance, Social Cognit. Affective Neurosci., 16 (2021), 43–57. https://doi.org/10.1093/scan/nsaa135 doi: 10.1093/scan/nsaa135
    [14] P. Fries, Rhythms for cognition: communication through coherence, Neuron, 88 (2015), 220–235. https://doi.org/10.1016/j.neuron.2015.09.034 doi: 10.1016/j.neuron.2015.09.034
    [15] M. Zee, H. M. Koomen, I. Van der Veen, Student-teacher relationship quality and academic adjustment in upper elementary school: the role of student personality, J. School Psychol., 51 (2013), 517–533. https://doi.org/10.1016/j.jsp.2013.05.003 doi: 10.1016/j.jsp.2013.05.003
    [16] R. Mogan, R. Fischer, J. A. Bulbulia, To be in synchrony or not? A meta-analysis of synchrony's effects on behavior, perception, cognition and affect, J. Exp. Social Psychol., 72 (2017), 13–20. https://doi.org/https://doi.org/10.1016/j.jesp.2017.03.009
    [17] J. Liu, R. Zhang, B. Geng, T. Zhang, D. Yuan, S. Otani, et al., Interplay between prior knowledge and communication mode on teaching effectiveness: Interpersonal neural synchronization as a neural marker, Neuroimage, 193 (2019), 93–102. https://doi.org/10.1016/j.neuroimage.2019.03.004 doi: 10.1016/j.neuroimage.2019.03.004
    [18] Y. Pan, S. Dikker, P. Goldstein, Y. Zhu, C. Yang, Y. Hu, Instructor-learner brain coupling discriminates between instructional approaches and predicts learning, Neuroimage, 211 (2020), 116657. https://doi.org/10.1016/j.neuroimage.2020.116657 doi: 10.1016/j.neuroimage.2020.116657
    [19] K. Yun, K. Watanabe, S. Shimojo, Interpersonal body and neural synchronization as a marker of implicit social interaction, Sci. Rep., 2 (2012), 959. https://doi.org/10.1038/srep00959 doi: 10.1038/srep00959
    [20] J. Levy, A. Goldstein, R. Feldman, Perception of social synchrony induces mother-child gamma coupling in the social brain, Social Cognit. Affective Neurosci., 12 (2017), 1036–1046. https://doi.org/10.1093/scan/nsx032 doi: 10.1093/scan/nsx032
    [21] A. Stolk, M. L. Noordzij, L. Verhagen, I. Volman, J. M. Schoffelen, R. Oostenveld, et al., Cerebral coherence between communicators marks the emergence of meaning, Proc. Natl. Acad. Sci. U.S.A., 111 (2014), 18183–18188. https://doi.org/10.1073/pnas.1414886111 doi: 10.1073/pnas.1414886111
    [22] S. Kinreich, A. Djalovski, L. Kraus, Y. Louzoun, R. Feldman, Brain-to-brain synchrony during naturalistic social interactions, Sci. Rep., 7 (2017), 17060. https://doi.org/10.1038/s41598-017-17339-5 doi: 10.1038/s41598-017-17339-5
    [23] D. M. Ellingsen, A. Duggento, K. Isenburg, C. Jung, J. Lee, J. Gerber, et al., Patient-clinician brain concordance underlies causal dynamics in nonverbal communication and negative affective expressivity, Transl. Psychiatry, 12 (2022), 44. https://doi.org/10.1038/s41398-022-01810-7 doi: 10.1038/s41398-022-01810-7
    [24] M. Schurz, J. Radua, M. G. Tholen, L. Maliske, D. S. Margulies, R. B. Mars, et al., Toward a hierarchical model of social cognition: A neuroimaging meta-analysis and integrative review of empathy and theory of mind, Psychol. Bull., 147 (2021), 293–327. https://doi.org/10.1037/bul0000303 doi: 10.1037/bul0000303
    [25] L. Ficco, L. Mancuso, J. Manuello, A. Teneggi, D. Liloia, S. Duca, et al., Disentangling predictive processing in the brain: a meta-analytic study in favour of a predictive network, Sci. Rep., 11 (2021), 16258. https://doi.org/10.1038/s41598-021-95603-5 doi: 10.1038/s41598-021-95603-5
    [26] G. Rizzolatti, L. Cattaneo, M. Fabbri-Destro, S. Rozzi, Cortical mechanisms underlying the organization of goal-directed actions and mirror neuron-based action understanding, Physiol. Rev., 94 (2014), 655–706. https://doi.org/10.1152/physrev.00009.2013 doi: 10.1152/physrev.00009.2013
    [27] M. Arioli, N. Canessa, Neural processing of social interaction: Coordinate-based meta-analytic evidence from human neuroimaging studies, Hum. Brain Mapp., 40 (2019), 3712–3737. https://doi.org/10.1002/hbm.24627 doi: 10.1002/hbm.24627
    [28] K. Lehmann, D. Bolis, K. J. Friston, L. Schilbach, M. J. D. Ramstead, P. Kanske, An active-inference approach to second-person neuroscience, Perspect. Psychol. Sci., 2023 (2023), 17456916231188000. https://doi.org/10.1177/17456916231188000 doi: 10.1177/17456916231188000
    [29] J. Barnby, G. Bellucci, N. Alon, L. Schilbach, V. Bell, C. Frith, et al., Beyond theory of mind: A formal framework for social inference and representation, PsyarXiv, 2023. https://doi.org/10.31234/osf.io/cmgu7
    [30] D. Wei, S. Tsheringla, J. C. McPartland, A. Allsop, Combinatorial approaches for treating neuropsychiatric social impairment, Philos. Trans. R. Soc. London, Ser. B, 377 (2022), 20210051. https://doi.org/10.1098/rstb.2021.0051 doi: 10.1098/rstb.2021.0051
    [31] T. Penton, C. Catmur, M. J. Banissy, G. Bird, V. Walsh, Non-invasive stimulation of the social brain: the methodological challenges, Social Cognit. Affective Neurosci., 17 (2022), 15–25. https://doi.org/10.1093/scan/nsaa102 doi: 10.1093/scan/nsaa102
    [32] H. K. Kim, D. M. Blumberger, J. Downar, Z. J. Daskalakis, Systematic review of biological markers of therapeutic repetitive transcranial magnetic stimulation in neurological and psychiatric disorders, Clin. Neurophysiol., 132 (2021), 429–448. https://doi.org/10.1016/j.clinph.2020.11.025 doi: 10.1016/j.clinph.2020.11.025
    [33] A. Czeszumski, S. Eustergerling, A. Lang, D. Menrath, M. Gerstenberger, S. Schuberth, et al., Hyperscanning: A valid method to study neural inter-brain underpinnings of social interaction, Front. Hum. Neurosci., 14 (2020), 39. https://doi.org/10.3389/fnhum.2020.00039 doi: 10.3389/fnhum.2020.00039
    [34] A. L. Valencia, T. Froese, What binds us? Inter-brain neural synchronization and its implications for theories of human consciousness, Neurosci. Conscious., 2020 (2020), niaa010. https://doi.org/10.1093/nc/niaa010 doi: 10.1093/nc/niaa010
    [35] U. Hakim, S. De Felice, P. Pinti, X. Zhang, J. A. Noah, Y. Ono, et al., Quantification of inter-brain coupling: A review of current methods used in haemodynamic and electrophysiological hyperscanning studies, Neuroimage, 280 (2023), 120354. https://doi.org/10.1016/j.neuroimage.2023.120354 doi: 10.1016/j.neuroimage.2023.120354
    [36] A. P. Burgess, On the interpretation of synchronization in EEG hyperscanning studies: a cautionary note, Front. Hum. Neurosci., 7 (2013), 881. https://doi.org/10.3389/fnhum.2013.00881 doi: 10.3389/fnhum.2013.00881
    [37] G. Dumas, J. Nadel, R. Soussignan, J. Martinerie, L. Garnero, Inter-brain synchronization during social interaction, PLoS One, 5 (2010), e12166. https://doi.org/10.1371/journal.pone.0012166 doi: 10.1371/journal.pone.0012166
    [38] K. Gugnowska, G. Novembre, N. Kohler, A. Villringer, P. E. Keller, D. Sammler, Endogenous sources of interbrain synchrony in duetting pianists, Cereb. Cortex, 32 (2022), 4110–4127. https://doi.org/10.1093/cercor/bhab469 doi: 10.1093/cercor/bhab469
    [39] W. Peng, W. Lou, X. Huang, Q. Ye, R. K. Tong, F. Cui, Suffer together, bond together: Brain-to-brain synchronization and mutual affective empathy when sharing painful experiences, Neuroimage, 238 (2021), 118249. https://doi.org/10.1016/j.neuroimage.2021.118249 doi: 10.1016/j.neuroimage.2021.118249
    [40] U. Lindenberger, S. C. Li, W. Gruber, V. Müller, Brains swinging in concert: cortical phase synchronization while playing guitar, BMC Neurosci., 10 (2009), 22. https://doi.org/10.1186/1471-2202-10-22 doi: 10.1186/1471-2202-10-22
    [41] V. Müller, U. Lindenberger, Probing associations between interbrain synchronization and interpersonal action coordination during guitar playing, Ann. N. Y. Acad. Sci., 1507 (2022), 146–161. https://doi.org/10.1111/nyas.14689 doi: 10.1111/nyas.14689
    [42] L. Astolfi, J. Toppi, A. Ciaramidaro, P. Vogel, C. M. Freitag, M. Siniatchkin, Raising the bar: Can dual scanning improve our understanding of joint action, Neuroimage, 216 (2020), 116813. https://doi.org/10.1016/j.neuroimage.2020.116813 doi: 10.1016/j.neuroimage.2020.116813
    [43] F. De Vico Fallani, V. Nicosia, R. Sinatra, L. Astolfi, F. Cincotti, D. Mattia, et al., Defecting or not defecting: how to "read" human behavior during cooperative games by EEG measurements, PLoS One, 5 (2010), e14187. https://doi.org/10.1371/journal.pone.0014187 doi: 10.1371/journal.pone.0014187
    [44] L. Astolfi, J. Toppi, F. De Vico Fallani, G. Vecchiato, S. Salinari, D. Mattia, et al., Neuroelectrical hyperscanning measures simultaneous brain activity in humans, Brain Topogr., 23 (2010), 243–256. https://doi.org/10.1007/s10548-010-0147-9 doi: 10.1007/s10548-010-0147-9
    [45] M. O. Abe, T. Koike, S. Okazaki, S. K. Sugawara, K. Takahashi, K. Watanabe, et al., Neural correlates of online cooperation during joint force production, Neuroimage, 191 (2019), 150–161. https://doi.org/10.1016/j.neuroimage.2019.02.003 doi: 10.1016/j.neuroimage.2019.02.003
    [46] L. Liu, Y. Zhang, Q. Zhou, D. D. Garrett, C. Lu, A. Chen, et al., Auditory-articulatory neural alignment between listener and speaker during verbal communication, Cereb. Cortex, 30 (2020), 942–951. https://doi.org/10.1093/cercor/bhz138 doi: 10.1093/cercor/bhz138
    [47] P. Goldstein, I. Weissman-Fogel, G. Dumas, S. G. Shamay-Tsoory, Brain-to-brain coupling during handholding is associated with pain reduction, Proc. Natl. Acad. Sci. U.S.A., 115 (2018), e2528–e2537. https://doi.org/10.1073/pnas.1703643115 doi: 10.1073/pnas.1703643115
    [48] I. Davidesco, E. Laurent, H. Valk, T. West, S. Dikker, C. Milne, et al., Brain-to-brain synchrony predicts long-term memory retention more accurately than individual brain measures, bioRxiv, (2019), 644047. https://doi.org/10.1101/644047 doi: 10.1101/644047
    [49] Y. Tang, X. Liu, C. Wang, M. Cao, S. Deng, X. Du, et al., Different strategies, distinguished cooperation efficiency, and brain synchronization for couples: An fNIRS-based hyperscanning study, Brain Behav., 10 (2020), e01768. https://doi.org/10.1002/brb3.1768 doi: 10.1002/brb3.1768
    [50] J. Jiang, C. Chen, B. Dai, G. Shi, G. Ding, L. Liu, et al., Leader emergence through interpersonal neural synchronization, Proc. Natl. Acad. Sci. U.S.A., 112 (2015), 4274–4279. https://doi.org/10.1073/pnas.1422930112 doi: 10.1073/pnas.1422930112
    [51] Q. Wang, Z. Han, X. Hu, S. Feng, H. Wang, T. Liu, et al., Autism symptoms modulate interpersonal neural synchronization in children with autism spectrum disorder in cooperative interactions, Brain Topogr., 33 (2020), 112–122. https://doi.org/10.1007/s10548-019-00731-x doi: 10.1007/s10548-019-00731-x
    [52] Y. Hu, Y. Hu, X. Li, Y. Pan, X. Cheng, Brain-to-brain synchronization across two persons predicts mutual prosociality, Social Cognit. Affective Neurosci., 12 (2017), 1835–1844. https://doi.org/10.1093/scan/nsx118 doi: 10.1093/scan/nsx118
    [53] U. Hasson, Y. Nir, I. Levy, G. Fuhrmann, R. Malach, Intersubject synchronization of cortical activity during natural vision, Science, 303 (2004), 1634–1640. https://doi.org/10.1126/science.1089506 doi: 10.1126/science.1089506
    [54] S. A. Nastase, V. Gazzola, U. Hasson, C. Keysers, Measuring shared responses across subjects using intersubject correlation, Social Cognit. Affective Neurosci., 14 (2019), 667–685. https://doi.org/10.1093/scan/nsz037 doi: 10.1093/scan/nsz037
    [55] E. Simony, C. J. Honey, J. Chen, O. Lositsky, Y. Yeshurun, A. Wiesel, et al., Dynamic reconfiguration of the default mode network during narrative comprehension, Nat. Commun., 7 (2016), 12141. https://doi.org/10.1038/ncomms12141 doi: 10.1038/ncomms12141
    [56] J. P. Lachaux, E. Rodriguez, J. Martinerie, F. J. Varela, Measuring phase synchrony in brain signals, Hum. Brain Mapp., 8 (1999), 194–208. https://doi.org/10.1002/(sici)1097-0193(1999)8:4<194::aid-hbm4>3.0.co;2-c doi: 10.1002/(sici)1097-0193(1999)8:4<194::aid-hbm4>3.0.co;2-c
    [57] A. F. C. Hamilton, Hyperscanning: Beyond the hype, Neuron, 109 (2021), 404–407. https://doi.org/10.1016/j.neuron.2020.11.008 doi: 10.1016/j.neuron.2020.11.008
    [58] A. Grinsted, J. C. Moore, S. Jevrejeva, Application of the cross wavelet transform and wavelet coherence to geophysical time series, Nonlin. Processes Geophys., 11 (2004), 561–566. https://doi.org/10.5194/npg-11-561-2004 doi: 10.5194/npg-11-561-2004
    [59] L. S. Wang, J. T. Cheng, I. J. Hsu, S. Liou, C. C. Kung, D. Y. Chen, et al., Distinct cerebral coherence in task-based fMRI hyperscanning: cooperation versus competition, Cereb. Cortex, 33 (2022), 421–433. https://doi.org/10.1093/cercor/bhac075 doi: 10.1093/cercor/bhac075
    [60] A. K. Seth, A. B. Barrett, L. Barnett, Granger causality analysis in neuroscience and neuroimaging, J. Neurosci., 35 (2015), 3293–3297. https://doi.org/10.1523/jneurosci.4399-14.2015 doi: 10.1523/jneurosci.4399-14.2015
    [61] M. B. Schippers, A. Roebroeck, R. Renken, L. Nanetti, C. Keysers, Mapping the information flow from one brain to another during gestural communication, Proc. Natl. Acad. Sci. U.S.A., 107 (2010), 9388–9393. https://doi.org/10.1073/pnas.1001791107 doi: 10.1073/pnas.1001791107
    [62] E. Bilek, P. Zeidman, P. Kirsch, H. Tost, A. Meyer-Lindenberg, K. Friston, Directed coupling in multi-brain networks underlies generalized synchrony during social exchange, Neuroimage, 252 (2022), 119038. https://doi.org/10.1016/j.neuroimage.2022.119038 doi: 10.1016/j.neuroimage.2022.119038
    [63] C. B. Holroyd, Interbrain synchrony: on wavy ground, Trends Neurosci., 45 (2022), 346–357. https://doi.org/10.1016/j.tins.2022.02.002 doi: 10.1016/j.tins.2022.02.002
    [64] Y. Pan, X. Cheng, Two-person approaches to studying social interaction in psychiatry: Uses and clinical relevance, Front. Psychiatry, 11 (2020), 301. https://doi.org/10.3389/fpsyt.2020.00301 doi: 10.3389/fpsyt.2020.00301
    [65] V. Leong, L. Schilbach, The promise of two-person neuroscience for developmental psychiatry: using interaction-based sociometrics to identify disorders of social interaction, Br. J. Psychiatry, 215 (2019), 636–638. https://doi.org/10.1192/bjp.2019.73 doi: 10.1192/bjp.2019.73
    [66] S. V. Wass, M. Whitehorn, I. Marriott Haresign, E. Phillips, V. Leong, Interpersonal neural entrainment during early social interaction, Trends Cognit. Sci., 24 (2020), 329–342. https://doi.org/10.1016/j.tics.2020.01.006 doi: 10.1016/j.tics.2020.01.006
    [67] Y. Pan, G. Novembre, B. Song, X. Li, Y. Hu, Interpersonal synchronization of inferior frontal cortices tracks social interactive learning of a song, Neuroimage, 183 (2018), 280–290. https://doi.org/10.1016/j.neuroimage.2018.08.005 doi: 10.1016/j.neuroimage.2018.08.005
    [68] F. T. Ramseyer, Motion energy analysis (MEA): A primer on the assessment of motion from video, J. Couns. Psychol., 67 (2020), 536–549. https://doi.org/10.1037/cou0000407 doi: 10.1037/cou0000407
    [69] Z. Cao, G. Hidalgo, T. Simon, S. E. Wei, Y. Sheikh, OpenPose: Realtime multi-person 2D pose estimation using part affinity fields, IEEE Trans. Pattern Anal. Mach. Intell., 43 (2021), 172–186. https://doi.org/10.1109/tpami.2019.2929257 doi: 10.1109/tpami.2019.2929257
    [70] S. Guglielmini, G. Bopp, V. L. Marcar, F. Scholkmann, M. Wolf, Systemic physiology augmented functional near-infrared spectroscopy hyperscanning: a first evaluation investigating entrainment of spontaneous activity of brain and body physiology between subjects, Neurophotonics, 9 (2022), 026601. https://doi.org/10.1117/1.NPh.9.2.026601 doi: 10.1117/1.NPh.9.2.026601
    [71] R. Cañigueral, S. Krishnan-Barman, A. F. C. Hamilton, Social signalling as a framework for second-person neuroscience, Psychon. Bull. Rev., 29 (2022), 2083–2095. https://doi.org/10.3758/s13423-022-02103-2 doi: 10.3758/s13423-022-02103-2
    [72] L. Kingsbury, S. Huang, J. Wang, K. Gu, P. Golshani, Y. E. Wu, et al., Correlated neural activity and encoding of behavior across brains of socially interacting animals, Cell, 178 (2019), 429–446.e416. https://doi.org/10.1016/j.cell.2019.05.022 doi: 10.1016/j.cell.2019.05.022
    [73] V. Müller, D. Perdikis, M. A. Mende, U. Lindenberger, Interacting brains coming in sync through their minds: an interbrain neurofeedback study, Ann. N. Y. Acad. Sci., 1500 (2021), 48–68. https://doi.org/10.1111/nyas.14605 doi: 10.1111/nyas.14605
    [74] L. Duan, W. J. Liu, R. N. Dai, R. Li, C. M. Lu, Y. X. Huang, et al., Cross-brain neurofeedback: scientific concept and experimental platform, PLoS One, 8 (2013), e64590. https://doi.org/10.1371/journal.pone.0064590 doi: 10.1371/journal.pone.0064590
    [75] S. Dikker, G. Michalareas, M. Oostrik, A. Serafimaki, H. M. Kahraman, M. E. Struiksma, et al., Crowdsourcing neuroscience: Inter-brain coupling during face-to-face interactions outside the laboratory, Neuroimage, 227 (2021), 117436. https://doi.org/10.1016/j.neuroimage.2020.117436 doi: 10.1016/j.neuroimage.2020.117436
    [76] M. Hallett, Transcranial magnetic stimulation and the human brain, Nature, 406 (2000), 147–150. https://doi.org/10.1038/35018000 doi: 10.1038/35018000
    [77] J. Vosskuhl, D. Struber, C. S. Herrmann, Non-invasive brain stimulation: A paradigm shift in understanding brain oscillations, Front. Hum. Neurosci., 12 (2018), 211. https://doi.org/10.3389/fnhum.2018.00211 doi: 10.3389/fnhum.2018.00211
    [78] A. Liu, M. Vöröslakos, G. Kronberg, S. Henin, M. R. Krause, Y. Huang, et al., Immediate neurophysiological effects of transcranial electrical stimulation, Nat. Commun., 9 (2018), 5092. https://doi.org/10.1038/s41467-018-07233-7 doi: 10.1038/s41467-018-07233-7
    [79] C. S. Herrmann, M. M. Murray, S. Ionta, A. Hutt, J. Lefebvre, Shaping intrinsic neural oscillations with periodic stimulation, J. Neurosci., 36 (2016), 5328–5337. https://doi.org/10.1523/jneurosci.0236-16.2016 doi: 10.1523/jneurosci.0236-16.2016
    [80] S. Alagapan, S. L. Schmidt, J. Lefebvre, E. Hadar, H. W. Shin, F. Frӧhlich, Modulation of cortical oscillations by low-frequency direct cortical stimulation is state-dependent, PloS Biol., 14 (2016), e1002424. https://doi.org/10.1371/journal.pbio.1002424 doi: 10.1371/journal.pbio.1002424
    [81] N. Takeuchi, Perspectives on rehabilitation using non-invasive brain stimulation based on second-person neuroscience of teaching-learning interactions, Front. Psychol., 12 (2022), 789637. https://doi.org/10.3389/fpsyg.2021.789637 doi: 10.3389/fpsyg.2021.789637
    [82] Y. Cabral-Calderin, M. Wilke, Probing the link between perception and oscillations: Lessons from transcranial alternating current stimulation, Neuroscientist, 26 (2020), 57–73. https://doi.org/10.1177/1073858419828646 doi: 10.1177/1073858419828646
    [83] V. Müller, U. Lindenberger, Hyper-brain networks support romantic kissing in humans, PloS One, 9 (2014), e112080. https://doi.org/10.1371/journal.pone.0112080 doi: 10.1371/journal.pone.0112080
    [84] J. Toppi, G. Borghini, M. Petti, E. J. He, V. De Giusti, B. He, et al., Investigating cooperative behavior in ecological settings: An EEG hyperscanning study, PloS One, 11 (2016), e0154236. https://doi.org/10.1371/journal.pone.0154236 doi: 10.1371/journal.pone.0154236
    [85] V. Leong, E. Byrne, K. Clackson, S. Georgieva, S. Lam, S. Wass, Speaker gaze increases information coupling between infant and adult brains, Proc. Natl. Acad. Sci. U.S.A., 114 (2017), 13290–13295. https://doi.org/10.1073/pnas.1702493114 doi: 10.1073/pnas.1702493114
    [86] Y. Mu, C. Guo, S. Han, Oxytocin enhances inter-brain synchrony during social coordination in male adults, Social Cognit. Affective Neurosci., 11 (2016), 1882–1893. https://doi.org/10.1093/scan/nsw106 doi: 10.1093/scan/nsw106
    [87] O. A. Heggli, I. Konvalinka, J. Cabral, E. Brattico, M. L. Kringelbach, P. Vuust, Transient brain networks underlying interpersonal strategies during synchronized action, Social Cognit. Affective Neurosci., 16 (2021), 19–30. https://doi.org/10.1093/scan/nsaa056 doi: 10.1093/scan/nsaa056
    [88] A. Pérez, M. Carreiras, J. A. Duñabeitia, Brain-to-brain entrainment: EEG interbrain synchronization while speaking and listening, Sci. Rep., 7 (2017), 4190. https://doi.org/10.1038/s41598-017-04464-4 doi: 10.1038/s41598-017-04464-4
    [89] J. Sünger, V. Müller, U. Lindenberger, Directionality in hyperbrain networks discriminates between leaders and followers in guitar duets, Front. Hum. Neurosci., 7 (2013), 234. https://doi.org/10.3389/fnhum.2013.00234 doi: 10.3389/fnhum.2013.00234
    [90] Y. Mu, S. Han, M. J. Gelfand, The role of gamma interbrain synchrony in social coordination when humans face territorial threats, Social Cognit. Affective Neurosci., 12 (2017), 1614–1623. https://doi.org/10.1093/scan/nsx093 doi: 10.1093/scan/nsx093
    [91] N. Kopell, G. B. Ermentrout, M. A. Whittington, R. D. Traub, Gamma rhythms and beta rhythms have different synchronization properties, Proc. Natl. Acad. Sci. U.S.A., 97 (2000), 1867–1872. https://doi.org/10.1073/pnas.97.4.1867 doi: 10.1073/pnas.97.4.1867
    [92] P. J. Uhlhaas, W. Singer, Neuronal dynamics and neuropsychiatric disorders: toward a translational paradigm for dysfunctional large-scale networks, Neuron, 75 (2012), 963–980. https://doi.org/10.1016/j.neuron.2012.09.004 doi: 10.1016/j.neuron.2012.09.004
    [93] K. J. Friston, T. Parr, Y. Yufik, N. Sajid, C. J. Price, E. Holmes, Generative models, linguistic communication and active inference, Neurosci. Biobehav. Rev., 118 (2020), 42–64. https://doi.org/10.1016/j.neubiorev.2020.07.005 doi: 10.1016/j.neubiorev.2020.07.005
    [94] E. Tognoli, J. A. Kelso, The coordination dynamics of social neuromarkers, Front. Hum. Neurosci., 9 (2015), 563. https://doi.org/10.3389/fnhum.2015.00563 doi: 10.3389/fnhum.2015.00563
    [95] C. Peylo, Y. Hilla, P. Sauseng, Cause or consequence? Alpha oscillations in visuospatial attention, Trends Neurosci., 44 (2021), 705–713. https://doi.org/10.1016/j.tins.2021.05.004 doi: 10.1016/j.tins.2021.05.004
    [96] W. Klimesch, α-band oscillations, attention, and controlled access to stored information, Trends Cognit. Sci., 16 (2012), 606–617. https://doi.org/10.1016/j.tics.2012.10.007 doi: 10.1016/j.tics.2012.10.007
    [97] S. Hoehl, M. Fairhurst, A. Schirmer, Interactional synchrony: signals, mechanisms and benefits, Social Cognit. Affective Neurosci., 16 (2021), 5–18. https://doi.org/10.1093/scan/nsaa024 doi: 10.1093/scan/nsaa024
    [98] N. J. Davis, S. P. Tomlinson, H. M. Morgan, The role of beta-frequency neural oscillations in motor control, J. Neurosci., 32 (2012), 403–404. https://doi.org/10.1523/jneurosci.5106-11.2012 doi: 10.1523/jneurosci.5106-11.2012
    [99] B. Pollok, D. Latz, V. Krause, M. Butz, A. Schnitzler, Changes of motor-cortical oscillations associated with motor learning, Neuroscience, 275 (2014), 47–53. https://doi.org/10.1016/j.neuroscience.2014.06.008 doi: 10.1016/j.neuroscience.2014.06.008
    [100] V. Müller, J. Sünger, U. Lindenberger, Intra- and inter-brain synchronization during musical improvisation on the guitar, PloS One, 8 (2013), e73852. https://doi.org/10.1371/journal.pone.0073852 doi: 10.1371/journal.pone.0073852
    [101] C. S. Herrmann, D. Strüber, R. F. Helfrich, A. K. Engel, EEG oscillations: From correlation to causality, Int. J. Psychophysiol., 103 (2016), 12–21. https://doi.org/10.1016/j.ijpsycho.2015.02.003 doi: 10.1016/j.ijpsycho.2015.02.003
    [102] S. H. Williams, D. Johnston, Kinetic properties of two anatomically distinct excitatory synapses in hippocampal CA3 pyramidal neurons, J. Neurophysiol., 66 (1991), 1010–1020. https://doi.org/10.1152/jn.1991.66.3.1010 doi: 10.1152/jn.1991.66.3.1010
    [103] G. Novembre, G. Knoblich, L. Dunne, P. E. Keller, Interpersonal synchrony enhanced through 20 Hz phase-coupled dual brain stimulation, Social Cognit. Affective Neurosci., 12 (2017), 662–670. https://doi.org/10.1093/scan/nsw172 doi: 10.1093/scan/nsw172
    [104] C. Szymanski, V. Müller, T. R. Brick, T. von Oertzen, U. Lindenberger, Hyper-transcranial alternating current stimulation: experimental manipulation of inter-brain synchrony, Front. Hum. Neurosci., 11 (2017), 539. https://doi.org/10.3389/fnhum.2017.00539 doi: 10.3389/fnhum.2017.00539
    [105] Y. Pan, G. Novembre, B. Song, Y. Zhu, Y. Hu, Dual brain stimulation enhances interpersonal learning through spontaneous movement synchrony, Social Cognit. Affective Neurosci., 16 (2021), 210–221. https://doi.org/10.1093/scan/nsaa080 doi: 10.1093/scan/nsaa080
    [106] R. T. Canolty, R. T. Knight, The functional role of cross-frequency coupling, Trends Cognit. Sci., 14 (2010), 506–515. https://doi.org/10.1016/j.tics.2010.09.001 doi: 10.1016/j.tics.2010.09.001
    [107] B. Asamoah, A. Khatoun, M. Mc Laughlin, tACS motor system effects can be caused by transcutaneous stimulation of peripheral nerves, Nat. Commun., 10 (2019), 266. https://doi.org/10.1038/s41467-018-08183-w doi: 10.1038/s41467-018-08183-w
    [108] G. Novembre, G. D. Iannetti, Hyperscanning alone cannot prove causality. Multibrain stimulation can, Trends Cognit. Sci., 25 (2021), 96–99. https://doi.org/10.1016/j.tics.2020.11.003 doi: 10.1016/j.tics.2020.11.003
    [109] S. L. Koole, W. Tschacher, Synchrony in psychotherapy: A review and an integrative framework for the therapeutic alliance, Front. Psychol., 7 (2016), 862. https://doi.org/10.3389/fpsyg.2016.00862 doi: 10.3389/fpsyg.2016.00862
    [110] M. Bishop, N. Kayes, K. McPherson, Understanding the therapeutic alliance in stroke rehabilitation, Disability Rehabil., 43 (2021), 1074–1083. https://doi.org/10.1080/09638288.2019.1651909 doi: 10.1080/09638288.2019.1651909
    [111] P. Søndenå, G. Dalusio-King, C. Hebron, Conceptualisation of the therapeutic alliance in physiotherapy: is it adequate, Musculoskeletal Sci. Pract., 46 (2020), 102131. https://doi.org/10.1016/j.msksp.2020.102131 doi: 10.1016/j.msksp.2020.102131
    [112] P. Mistiaen, M. van Osch, L. van Vliet, J. Howick, F. L. Bishop, Z. Di Blasi, et al., The effect of patient-practitioner communication on pain: a systematic review, Eur. J. Pain, 20 (2016), 675–688. https://doi.org/10.1002/ejp.797 doi: 10.1002/ejp.797
    [113] L. Schilbach, Towards a second-person neuropsychiatry, Philos. Trans. R. Soc. London, Ser. B, 371 (2016), 20150081. https://doi.org/10.1098/rstb.2015.0081 doi: 10.1098/rstb.2015.0081
    [114] L. Schilbach, J. M. Lahnakoski, Clinical neuroscience meets second-person neuropsychiatry, in Social and Affective Neuroscience of Everyday Human Interaction: From Theory to Methodology, Cham (CH): Springer, (2023), 177–191.
    [115] L. E. Quiñones-Camacho, F. A. Fishburn, K. Belardi, D. L. Williams, T. J. Huppert, S. B. Perlman, Dysfunction in interpersonal neural synchronization as a mechanism for social impairment in autism spectrum disorder, Autism Res., 14 (2021), 1585–1596. https://doi.org/10.1002/aur.2513 doi: 10.1002/aur.2513
    [116] E. Bilek, G. Stößel, A. Schüfer, L. Clement, M. Ruf, L. Robnik, et al., State-dependent cross-brain information flow in borderline personality disorder, JAMA Psychiatry, 74 (2017), 949–957. https://doi.org/10.1001/jamapsychiatry.2017.1682 doi: 10.1001/jamapsychiatry.2017.1682
    [117] Y. Zhang, T. Meng, Y. Hou, Y. Pan, Y. Hu, Interpersonal brain synchronization associated with working alliance during psychological counseling. Psychiatry Res. Neuroimaging, 282 (2018), 103–109. https://doi.org/10.1016/j.pscychresns.2018.09.007 doi: 10.1016/j.pscychresns.2018.09.007
    [118] N. Takeuchi, T. Mori, Y. Suzukamo, S. I. Izumi, Integration of teaching processes and learning assessment in the prefrontal cortex during a video game teaching-learning task, Front. Psychol., 7 (2017), 2052. https://doi.org/10.3389/fpsyg.2016.02052 doi: 10.3389/fpsyg.2016.02052
    [119] L. Zheng, C. Chen, W. Liu, Y. Long, H. Zhao, X. Bai, et al., Enhancement of teaching outcome through neural prediction of the students' knowledge state, Hum. Brain Mapp., 39 (2018), 3046–3057. https://doi.org/10.1002/hbm.24059 doi: 10.1002/hbm.24059
    [120] L. Zhang, X. Xu, Z. Li, L. Chen, L. Feng, Interpersonal neural synchronization predicting learning outcomes from teaching-learning interaction: A Meta-analysis, Front. Psychol., 13 (2022), 835147. https://doi.org/10.3389/fpsyg.2022.835147 doi: 10.3389/fpsyg.2022.835147
    [121] S. M. Fleming, R. J. Dolan, The neural basis of metacognitive ability, Philos. Trans. R. Soc. London, Ser. B, 367 (2012), 1338–1349. https://doi.org/10.1098/rstb.2011.0417 doi: 10.1098/rstb.2011.0417
    [122] A. G. Vaccaro, S. M. Fleming, Thinking about thinking: A coordinate-based meta-analysis of neuroimaging studies of metacognitive judgements, Brain Neurosci. Adv., 2 (2018), 2398212818810591. https://doi.org/10.1177/2398212818810591 doi: 10.1177/2398212818810591
    [123] J. F. Martín-Rodríguez, J. León-Carrión, Theory of mind deficits in patients with acquired brain injury: a quantitative review, Neuropsychologia, 48 (2010), 1181–1191. https://doi.org/10.1016/j.neuropsychologia.2010.02.009 doi: 10.1016/j.neuropsychologia.2010.02.009
    [124] M. Al Banna, N. A. Redha, F. Abdulla, B. Nair, C. Donnellan, Metacognitive function poststroke: a review of definition and assessment, J. Neurol. Neurosurg. Psychiatry, 87 (2016), 161–166. https://doi.org/10.1136/jnnp-2015-310305 doi: 10.1136/jnnp-2015-310305
    [125] B. Nijsse, J. M. Spikman, J. M. A. Visser-Meily, P. L. M. de Kort, C. M. van Heugten, Social cognition impairments are associated with behavioural changes in the long term after stroke, PloS One, 14 (2019), e0213725. https://doi.org/10.1371/journal.pone.0213725 doi: 10.1371/journal.pone.0213725
    [126] Y. X. Yeo, C. F. Pestell, R. S. Bucks, F. Allanson, M. Weinborn, Metacognitive knowledge and functional outcomes in adults with acquired brain injury: A meta-analysis, Neuropsychol. Rehabil., 31 (2021), 453–478. https://doi.org/10.1080/09602011.2019.1704421 doi: 10.1080/09602011.2019.1704421
    [127] P. Lakatos, J. Gross, G. Thut, A new unifying account of the roles of neuronal entrainment, Curr. Biol., 29 (2019), R890–R905. https://doi.org/10.1016/j.cub.2019.07.075 doi: 10.1016/j.cub.2019.07.075
    [128] K. B. Jensen, P. Petrovic, C. E. Kerr, I. Kirsch, J. Raicek, A. Cheetham, et al., Sharing pain and relief: neural correlates of physicians during treatment of patients, Mol. Psychiatry, 19 (2014), 392–398. https://doi.org/10.1038/mp.2012.195 doi: 10.1038/mp.2012.195
    [129] S. G. Shamay-Tsoory, N. I. Eisenberger, Getting in touch: A neural model of comforting touch, Neurosci. Biobehav. Rev., 130 (2021), 263–273. https://doi.org/10.1016/j.neubiorev.2021.08.030 doi: 10.1016/j.neubiorev.2021.08.030
    [130] B. M. Fitzgibbon, M. J. Giummarra, N. Georgiou-Karistianis, P. G. Enticott, J. L. Bradshaw, Shared pain: from empathy to synaesthesia, Neurosci. Biobehav. Rev., 34 (2010), 500–512. https://doi.org/10.1016/j.neubiorev.2009.10.007 doi: 10.1016/j.neubiorev.2009.10.007
    [131] D. M. Ellingsen, K. Isenburg, C. Jung, J. Lee, J. Gerber, I. Mawla, et al., Dynamic brain-to-brain concordance and behavioral mirroring as a mechanism of the patient-clinician interaction, Sci. Adv., 6 (2020), eabc1304. https://doi.org/10.1126/sciadv.abc1304 doi: 10.1126/sciadv.abc1304
    [132] T. J. Kaptchuk, F. G. Miller, Placebo effects in medicine, N. Engl. J. Med., 373 (2015), 8–9. https://doi.org/10.1056/NEJMp1504023 doi: 10.1056/NEJMp1504023
    [133] M. Ienca, R. W. Kressig, F. Jotterand, B. Elger, Proactive ethical design for neuroengineering, assistive and rehabilitation technologies: the cybathlon lesson, J. Neuroeng. Rehabil., 14 (2017), 115. https://doi.org/10.1186/s12984-017-0325-z doi: 10.1186/s12984-017-0325-z
    [134] R. Cohen Kadosh, N. Levy, J. O'Shea, N. Shea, J. Savulescu, The neuroethics of non-invasive brain stimulation, Curr. Biol., 22 (2012), R108–111. https://doi.org/10.1016/j.cub.2012.01.013 doi: 10.1016/j.cub.2012.01.013
    [135] S. G. Shamay-Tsoory, Brains that fire together wire together: Interbrain plasticity underlies learning in social interactions, Neuroscientist, 28 (2022), 543–551. https://doi.org/10.1177/1073858421996682 doi: 10.1177/1073858421996682
    [136] A. Gramfort, M. Luessi, E. Larson, D. A. Engemann, D. Strohmeier, C. Brodbeck, et al., MNE software for processing MEG and EEG data, Neuroimage, 86 (2014), 446–460. https://doi.org/10.1016/j.neuroimage.2013.10.027 doi: 10.1016/j.neuroimage.2013.10.027
    [137] R. D. Pascual-Marqui, C. M. Michel, D. Lehmann, Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain, Int. J. Psychophysiol., 18 (1994), 49–65. https://doi.org/10.1016/0167-8760(84)90014-x doi: 10.1016/0167-8760(84)90014-x
    [138] J. Onton, M. Westerfield, J. Townsend, S. Makeig, Imaging human EEG dynamics using independent component analysis, Neurosci. Biobehav. Rev., 30 (2006), 808–822. https://doi.org/10.1016/j.neubiorev.2006.06.007 doi: 10.1016/j.neubiorev.2006.06.007
    [139] C. S. Nam, Z. Traylor, M. Chen, X. Jiang, W. Feng, P. Y. Chhatbar, Direct communication between brains: A systematic PRISMA review of brain-to-brain interface, Front. Neurorobot., 15 (2021), 656943. https://doi.org/10.3389/fnbot.2021.656943 doi: 10.3389/fnbot.2021.656943
    [140] G. Thut, T. O. Bergmann, F. Fröhlich, S. R. Soekadar, J. S. Brittain, A. Valero-Cabré, et al., Guiding transcranial brain stimulation by EEG/MEG to interact with ongoing brain activity and associated functions: A position paper, Clin. Neurophysiol., 128 (2017), 843–857. https://doi.org/10.1016/j.clinph.2017.01.003 doi: 10.1016/j.clinph.2017.01.003
    [141] S. Kohli, A. J. Casson, Removal of gross artifacts of transcranial alternating current stimulation in simultaneous EEG monitoring, Sensors (Basel), 19 (2019), 190. https://doi.org/10.3390/s19010190 doi: 10.3390/s19010190
    [142] D. Bolis, J. Balsters, N. Wenderoth, C. Becchio, L. Schilbach, Beyond autism: introducing the dialectical misattunement hypothesis and a Bayesian account of intersubjectivity, Psychopathology, 50 (2017), 355–372. https://doi.org/10.1159/000484353 doi: 10.1159/000484353
    [143] G. Zarubin, C. Gundlach, V. Nikulin, A. Villringer, M. Bogdan, Transient amplitude modulation of alpha-band oscillations by short-time intermittent closed-loop tACS, Front. Hum. Neurosci., 14 (2020), 366. https://doi.org/10.3389/fnhum.2020.00366 doi: 10.3389/fnhum.2020.00366
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