# Modulating complex brain states using MVPA-based neurofeedback: A systematic review

Valeria Finelli, Pierre Yger, Cecile Bordier, Renaud Jardri, Paul Allen

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Valeria Finelli, Pierre Yger, Cecile Bordier, Renaud Jardri, Paul Allen. Modulating complex brain states using MVPA-based neurofeedback: A systematic review. NeuroImage, 2026, 329, pp.121814. 10.1016/j.neuroimage.2026.121814. hal-05512634

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Review

# Modulating complex brain states using MVPA-based neurofeedback: A systematic review

Valeria Finelli<sup>a, *, †</sup>, Pierre Yger<sup>a</sup>, Cécile Bordier<sup>b</sup>, Renaud Jardri<sup>a, 1</sup>, Paul Allen<sup>a, c, 1</sup>

ARTICLE INFO

Keywords:

Real-time fMRI

Neurofeedback

Multivoxel pattern analysis (MVPA)

Emotional regulation

Mental health

Cognitive training

A B S T R A C T

Multi-voxel pattern analysis (MVPA)-based fMRI neurofeedback is a promising tool for modulating brain states and influencing cognition and behaviour, yet its effectiveness remains unclear. This systematic review investigates its efficacy across emotion regulation, fear conditioning, associative and perceptual learning, attention, craving, semantic neurofeedback and motor rehabilitation, incorporating a meta-analytic synthesis of neural effects. A systematic search of PubMed, Web of Science, IEEE, and DPBL databases identified 29 studies. Published studies exhibit considerable variability in protocol designs and methodological aspects related to the implementation of MVPA-based neurofeedback. Overall, the global meta-analysis revealed a moderate, statistically significant effect of MVPA-based neurofeedback on neural outcomes, with subgroup analysis indicating a similar moderate effect in emotion regulation. Most studies support neural modulation following neurofeedback training across multiple domains, though behavioural outcomes are less consistent. Fear reduction, attention and perceptual learning studies demonstrated both neural and behavioural changes, while associative learning and craving reduction studies showed evidence of neural regulation but unclear behavioural outcomes. Studies on motor rehabilitation and semantic neurofeedback demonstrated neural modulation but lacked behavioural assessments. Emotion regulation studies consistently supported neural modulation, however, only a few studies reported behavioural improvements. While these findings highlight the potential of neurofeedback, they also emphasise the need for standardized methodologies and clearer theoretical frameworks. Clarifying terminology used to define different approaches and addressing MVPA-specific methodological considerations – such as preprocessing, motion correction, and classifier selection – will be critical for refining neurofeedback protocols and unlocking the full potential of MVPA-based neurofeedback for both clinical and research applications.

1. Introduction

Real-time fMRI (rt-fMRI) neurofeedback is a non-invasive technique that enables participants to gain voluntary control over neuronal activity in specific brain regions (Pindi et al., 2022). Since its development in 2003 (Weiskopf et al., 2003), rt-fMRI neurofeedback has attracted significant attention for its potential to explore brain-behavior relationships, to modulate emotion, cognition, and perception, and to study or treat neuropsychiatric disorders (Hampson, 2021). In a typical setup, participants use cognitive strategies to modulate target brain areas or networks, receiving feedback to optimize these strategies and achieve the desired neural control. This modulation can lead to

behavioral changes in addition to symptom improvements (Donantueno et al., 2023; Orlov et al., 2018). Previous studies have demonstrated the potential of rt-fMRI neurofeedback in inducing improvements in cognitive domains in healthy individuals (Cortese et al., 2016; deBencourt et al., 2015) as well as reduction of symptoms in clinical populations (Pereira et al., 2022; Weaver et al., 2020).

While most studies have employed univariate activation-based or functional connectivity feedback, multivoxel pattern analysis (MVPA)-feedback has emerged as a promising alternative approach (Watanabe et al., 2017). Unlike univariate or connectivity methods, MVPA analyses identify spatially-distributed brain activity patterns, allowing for better discrimination of complex brain states (Oblak et al., 2019). MVPA-based

<footnote><sup>a</sup> University of Lille, Inserm, CHU Lille, U1172 - LilNCog - Lille Neuroscience & Cognition, Lille, France</footnote>

<footnote><sup>b</sup> Univ. Lille, CNRS, Inserm, CHU Lille, Institut Pasteur de Lille, US 41 - UAR 2014 - PLBS, Lille F-59000, France</footnote>

<footnote><sup>c</sup> Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: valeria.finelli@univ-lille.fr (V. Finelli).</footnote>

<footnote n="1">¹ These authors contributed equally as last authors to this work</footnote>
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Overview of MVPA-based neurofeedback methodology. (A) Schematic representation of the MVPA-based neurofeedback procedure, where brain activity is measured via fMRI, analyzed using multivariate pattern analysis (MVPA), and feedback is provided to the participant via a visual display. (B) A common classification method used in MVPA-based neurofeedback: Support Vector Machine (SVM), where the distance from the discriminant hyperplane dissociating starting and target states is used to compute the feedback signal. (C) Effect of motion on fMRI analyses, comparing its greater impact on MVPA weight maps (left) versus an activation cluster obtained with a more conventional univariate approach (right).</figcaption></figure>

neurofeedback involves developing classifiers —either regional or whole-brain—to decode specific brain states (Fig. 1A). Although constructing these classifiers can be computationally demanding, their application in real-time requires limited computational load and is achievable with modern processing capacity (Taschereau-Dumouchel et al., 2021). This approach offers higher sensitivity than univariate analyses (Cortese et al., 2021) and is particularly useful for identifying complex neurofeedback targets (LaConte, 2011; LaConte et al., 2007), potentially leading to more effective training and stronger inferences about brain-behaviour relationships (Sitaram et al., 2017).

Studies utilizing MVPA-based neurofeedback have shown promising results in several domains, such as emotion regulation (Li et al., 2016; Shibata et al., 2016), motor rehabilitation (Bagarinao et al., 2018; Lee et al., 2019), and attentional control (deBettencourt et al., 2015; Oka et al., 2024). However, considerable variability exists in terms of methodologies, study designs, and outcome measures. Studies typically include small sample sizes and lack standardisation, making it difficult to draw firm conclusions about efficacy. Nevertheless, conducting a systematic review at this early stage that included 29 studies, allows us to summarise the current state of the evidence, highlight the extensive methodological weaknesses and make recommendations for strengthening future methodology, design and research.

This review aims to evaluate the effectiveness of MVPA-based fMRI neurofeedback in modulating brain states and influencing cognition and behaviour across domains, such as associative learning, emotion regulation, fear conditioning, motor rehabilitation, attention, and craving. To complement the qualitative synthesis, we conducted a meta-analysis of neural outcomes across all eligible studies, with planned subgroup analyses to explore the most represented domain-specific effects.

## 2. Methods

### 2.1. Protocol pre-registration

A protocol for the systematic review was developed in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA; Page et al., 2021)) and registered with the International Prospective Register of Systematic Reviews (PROSPERO; CRD42024529573).

### 2.2. Search strategy

A systematic search of the PubMed, Web of Science, IEEE and DPBL databases was conducted up until April 3, 2025, using the following keywords: ("functional magnetic resonance imag*" OR "fMRI" OR "functional MRI" OR "functional neuroimaging") AND ("neurofeedback" OR "neuro-feedback" OR "biofeedback" OR "neural feedback" OR "NF" OR "NFB" OR "NFT" OR "feedback-training" OR "real-time" OR "real time" OR "real-time fMRI") AND ("MVPA" OR "multi-voxel pattern analysis" OR "multivariate analysis" OR "support vector machine" OR "SVM" OR "pattern classification" OR "decoded neurofeedback" OR "decnef"). Additional searches were conducted using ClinicalTrials.gov and preprint servers: https://www.biorxiv.org, https://www.medrxiv.org/, https://psyarxiv.com/ and https://www.arxiv.org.

### 2.3. Study selection

A total of 339 articles were identified after duplicate removal. Of these, 293 articles were excluded based on title and abstract, leaving 45 articles for full-text assessment.

Studies were included if they:

1. Investigated human participants of any age or demographic group, with no restrictions on clinical or non-clinical populations.

2. Applied MVPA-based fMRI neurofeedback with the aim of modulating behaviour, cognition, or brain function.

3. Were published in English.

Non-controlled, pilot, and feasibility studies were also included due to the novelty of the field, which limits the number of published studies.

Studies were excluded if:

1. Neurofeedback was used solely for purposes such as task classification, device control, or behavioural responses unrelated to brain modulation.

2. They reported secondary analyses or reused participant data previously published.

3. They were review articles, meta-analyses, or scoping reviews.
<figure><img src="image_3.png" /><figcaption>Fig. 2. PRISMA flowchart of literature search and exclusion steps that resulted in 29 studies included in the review.</figcaption></figure>

4 The implementation of a closed-loop neurofeedback system was unclear and could not be verified based on the methodological details provided.

Based on these criteria, 29 articles were included (see Fig. 2).

A global meta-analysis of neural outcomes was conducted across all identified MVPA-based neurofeedback studies (n = 29). Two studies with only one or two participants were excluded for methodological reasons as the formula used to compute Hedges' g<sub>av</sub> requires a minimum sample size of three. One study was excluded due to a study design that did not include first- and last-run neural accuracy measures. Following data extraction, six further studies were excluded due to missing data and lack of response from authors. For studies with multiple neurofeedback conditions or design variations, only one dataset per study was included to maintain independence of observations. Specifically, when studies included up- and down-regulation groups, the up-regulation group was selected to align with the majority of studies. If classifiers were computed at both the individual and group level, the individual-level classifier was chosen. In studies with multiple target measures (e. g., two finger pairs), the primary study target was selected. For studies

reporting both continuous and intermittent feedback, one dataset was chosen arbitrarily. To assess the robustness of this selection, a supplementary analysis was conducted using the alternative feedback dataset. Consequently, 20 studies were included in the final meta-analysis. Subgroup analyses were planned for each application domain; however, due to the limited number of studies in most domains, quantitative subgroup analysis was conducted only for emotion regulation, where a minimum of five studies was available.

## 2.4. *Data extraction*

For the systematic review, the following data were extracted: information about study design, details of the neurofeedback protocol and control conditions; methods used for online signal-processing, and outcomes of the neurofeedback training, including both behavioural and neural effects. Data was summarized and reported in three different sections: (i) details of protocol design, (ii) methodological considerations specific to MVPA-based studies, (iii) behavioural and neural effects.

For the meta-analysis, classifier accuracy during the first and last
neurofeedback run/session was extracted as the primary outcome. For each included study, mean and standard deviation (SD) values were collected. If alternative measures of variability (e.g., standard error or confidence intervals) were reported, they were converted to SD using the *Meta-Analysis Accelerator* tool (Abbas et al., 2024).

When relevant data was not directly reported in the text, values were extracted from published graphs using *WebPlotDigitizer*, version 5.2 (Rohatgi, 2024). In cases where data could not be obtained from the text or graphs, the corresponding authors were contacted.

## 2.5. Statistical analysis

Effect sizes were calculated using Hedges' g<sub>av</sub> (Eq. 1), a corrected measure of Cohen's d<sub>av</sub> (Eq. 2), following the approach described by Hirst et al. (2018) (Table S1).

$$Hedges g_{av} = d_{av} * \left( 1 - \left( \frac{3}{4 * n - 1} - 1 \right) \right) \qquad (1)$$

$$Cohen's d_{av} = \frac{M_{diff}}{\sqrt{\left(\frac{SD_{1}^{2} + SD_{2}^{2}}{d2x}\right)}} \qquad (2)$$

This method adjusts for small sample bias and uses the average SD of observations as the standardizer (Cumming, 2012; Cumming and Calin-Jageman, 2016; Lakens, 2013). While Hedges' g<sub>av</sub> may not be the optimal measure of effect size for within-subject designs, it provides a practical and conservative solution for when the correlation coefficient (r) is not reported and cannot be extracted. Similarly, variance was calculated as described by Hirst et al. (2018) to adjust for the absence of a correlation coefficient (Eq. 3).

$$Vg_{av} = \left( \frac{n_1 + n_2}{n_1 n_2} \right) + \left( \frac{g_{av}^2}{2(n_1 + n_2)} \right) \qquad (3)$$

The meta-analysis was conducted using the metafor package in R (Viechtbauer, 2010). A random-effects model was applied, and the Paule-Mandel estimator was chosen to calculate the variance of the random effects (Kacker, 2004). Heterogeneity was assessed using the I2 statistic and Cochran's Q-test. Publication bias was visually evaluated with funnel plots and statistically tested using Egger's test.

## 2.6. Quality assessment

Quality assessment was conducted via the National Heart, Lung, and Blood Institute (NHLBI) quality assessment tools based on the study design: Quality Assessment of Controlled Intervention Studies; Quality Assessment of Case-Control Studies; Quality Assessment Tool for Before-After (Pre- Post) Studies with No Control Group (NHLBI, 2021). The Quality Assessment of Controlled Intervention Studies tool was adapted to also evaluate cross-over designs, and the Single-Case Experimental Design (SCED) scale was used to assess single-subject studies (Tate et al., 2008). To evaluate the quality of each paper, each “yes” answer was assigned a score of 1, while answers with “no/not applicable/not reported” were assigned a score of 0. For SCED assessment, a partial score of 0.5 was assigned in cases of partial reporting. The articles were then categorised based on the score obtained into three categories: ‘good quality’ for scores over 70 %, ‘fair quality’ between 50 % and 70 % and ‘poor quality’ below 50 %.

To specifically assess the design and reporting quality of the included neurofeedback studies, the CRED-nf checklist was used (Ros et al., 2020).

# 3. Results

## 3.1. Protocol design

When conducting a neurofeedback study, several methodological factors need to be considered, such as the number of training sessions, timing of neurofeedback runs, feedback presentation, instructions and choice of control groups (Humpston et al., 2020). Currently, published studies exhibit significant variability in protocol designs (Table 1).

The number of sessions varied greatly across studies, ranging from 1 to 10 sessions. Most studies conducted between 2 and 6 sessions, while many used only a single session. Only one study included up to 10 sessions (Shibata et al., 2011). Variation was also high in the number of runs within a session, which ranged from 2 to 12, with most studies employing between 3 and 9 runs. A common approach was to adjust the number of runs per participant based on time constraints and participants' availability.

In terms of runs timing, each run is defined by regulate (where participants attempt to modulate brain activity) and rest blocks. In current studies, the duration of regulate blocks ranged from 6 to 50 s. Most studies used either a short 6 s duration or durations between 10 and 30 s, with only four studies extending to 30–50 s. Less variability was observed for the duration of rest blocks, with most studies using short periods under 15 s, while others ranged from 20 to 30 s. Studies with shorter regulate periods often had shorter rest periods. A notable case was the study by Oka et al. (2024), where the timing of stimuli was based on decoder performance, resulting in no fixed timing between rest and regulate blocks.

Regarding feedback presentation, all studies used visual feedback, with the most common display being a disk with an expanding diameter based on regulation success (Fig. 1A). Other studies employed other simple visual presentations, such as thermometer, slider, ring, shape, arrow or colour of a circle. Five studies used more complex feedback displays, specifically one study provided a live video feed of a humanoid robot's arm movement (Bagarinao et al., 2018), while four studies modified stimuli directly (e.g. changing the composition of an image, adjusting opacity of the visual stimuli or altering the background colour of the virtual reality environment) (Calvert et al., 2022; deBettencourt et al., 2015; Lorenzetti et al., 2018; Weaver et al., 2020). Additionally, two studies used a multi-dimensional scaling plot as feedback, where the participant's mental state was represented by a red start on the plot, with the distance of the star from the target mental state indicating modulation success (Ciarlo et al., 2022; Russo et al., 2021). Furthermore, about half of the studies offered monetary rewards based on participants' success.

Feedback presentation can be continuous, i.e. updated with each acquired volume, or intermittent, i.e. averaged across the whole regulation block and shown at the end (Hellrung et al., 2018). Of the included papers, thirteen provided continuous feedback, fourteen intermittent feedback, one presented participants with either continuous or intermittent feedback (Fede et al., 2023) and one had no fixed block duration (Oka et al., 2024). In case of intermittent feedback, a 4- to 7 s delay was introduced after regulate blocks to account for the hemodynamic delay. Only two studies used a longer 20 s delay, while two others had no or 1 s delay. Feedback presentation typically lasted 1 to 2 s, with only four studies extending to 5 s. Notably, Oka et al. (2024) did not employ any feedback display as decoding performance was used to trigger stimuli.

Regarding the instructions provided, studies can be categorised into two groups: implicit or explicit. About half of the studies provided explicit instructions to participants, giving them specific tasks or strategies to use, such as a motor imagery or attention task, or autobiographical memory recall (Bagarinao et al., 2018; deBettencourt et al., 2015; Moll et al., 2014). Moreover, participants were made aware of the feedback's aim and the target brain modulation. The remaining studies provided implicit instructions, where participants were simply asked to maximize the feedback received without any additional instructions or strategies.
Box 1

Operational definitions of MVPA-based neurofeedback approaches.

A. MVPA Classification Methods

*   **Support Vector Machine (SVM):** A supervised machine learning algorithm that constructs an optimal hyperplane to separate brain activity patterns from different mental states or conditions (LaConte et al., 2007; Li et al., 2016).

*   **Logistic Regression (LR):** A supervised classification algorithm that estimates the probability that a given brain pattern belongs to a specific mental state (Yamashita et al., 2008).

*   **Representational Similarity Analysis (RSA):** A multivariate technique that assesses the similarity structure of brain activity patterns by comparing how closely current brain states resemble target patterns, without directly classifying them (Ciarlo et al., 2022).

B. MVPA Neurofeedback Experimental Designs

*   **MVPA-based neurofeedback:** An umbrella term referring to any neurofeedback paradigm that uses multivoxel pattern analysis to extract neural patterns for real-time feedback (Hampson, 2021).

*   **Decoded Neurofeedback (DecNef):** A subset of MVPA-neurofeedback characterized by implicit learning, where participants are unaware of the target neural state and often receive monetary rewards (Taschereau-Dumouchel et al., 2022).

*   **Associative Neurofeedback:** A DecNef approach where non-conscious associations are formed between brain activity patterns and specific external stimuli (e.g., images, words) (Amano et al., 2016).

*   **Adaptive Neurofeedback:** A subset of MVPA-neurofeedback in which task parameters (e.g. difficulty, timing) are modified dynamically based on the participant's decoded brain activity in real-time (Weaver et al., 2020).

*   **Semantic Neurofeedback:** A subset of MVPA-neurofeedback which uses representational similarity analysis (RSA) to compare the participant's current neural pattern to a target representation in semantic space (Goebel et al., 2024).

Lastly, a key consideration in neurofeedback studies is the selection of the control group. About a third of the studies implemented no control group or condition. The most used control condition was a no-neurofeedback group, where participants completed the task, either inside or outside the MRI, without receiving any feedback. One study applied this strategy in a cross-over design (Bagarinao et al., 2018). Other strategies involved a control group that only participated in the pre- and post-behavioural tasks or the use of a within-subject control condition. Two of the studies that investigated neurofeedback effects in clinical populations included a healthy control group (Calvert et al., 2022; Weaver et al., 2020). Notably, the study by deBettencourt et al. (2015) employed several control groups: one group receiving veridical feedback, another receiving sham feedback from a different participant, a third not receiving any neurofeedback training, a fourth receiving feedback based on their reaction time while completing the task outside the MRI, and a fifth receiving sham feedback from another participant in the reaction time group. Three studies employed two active conditions, where participants received feedback in opposite directions, aiming to either upregulate or downregulate brain activity (Berman et al., 2023; Cortese et al., 2016; Shibata et al., 2016).

3.2. Methods for MVPA-based neurofeedback

Beyond the previously discussed methodological aspects, MVPA-
based neurofeedback involves specific features to consider, such as the
target pattern and its definition, the classification method employed,
and its real-time application (Table 2).

Regarding the target pattern, about a third of the included studies constructed a whole-brain classifier. Other studies focused on a specific brain region of interest, such as V1/V2, primary motor cortex, sensorimotor cortex, bilateral fusiform gyrus, cingulate cortex, ventral temporal cortex, bilateral insula, inferior temporal cortex or para-hippocampal and fusiform gyrus, with two studies concentrating on individualised regions of interest (Iordan et al., 2020; Lorenzetti et al., 2018). Lastly, four studies focused on multiple brain regions.

To define the target pattern, most studies conducted a functional localizer, either on the same day as the neurofeedback training or during an earlier visit. Only four studies employed a different approach. Berman et al. (2023) used a classifier developed in a previous study, while Cushing et al. (2023), Pereira et al. (2022) and Taschereau-Dumouchel et al. (2018) developed the classifier using data from surrogate participants, i.e. individuals who are exposed to the stimuli to build the classifier but do not take part in the final neurofeedback procedure. These four studies also differ in that they do not use a classifier in the native space. Specifically, Berman et al. (2023) and Pereira et al. (2022) used a

classifier in the MNI space, while Cushing et al. (2023) and Tascher-
eau-Dumouchel et al. (2018) applied the “hyperalignment” method.
This approach, introduced by Taschereau-Dumouchel et al. (2018), al-
lows for the collection of functional data from surrogate participants,
which is then subsequently aligned with the native space of the desig-
nated participants who will undergo neurofeedback training.

For classification, most studies relied on common approaches,
namely *sparse logistic regression* (SLR) and *support vector machine* (SVM,
Fig. 1B). While SLR aims to predict the probability that an instance
belongs to a given class or not (Yamashita et al., 2008), SVM aims to find
the best possible hyperplane that separates the data points of different
classes (Weaverdyck et al., 2020). Fourteen studies used SLR, with some
employing variations such as multinomial SLR, iterative-SLR, SLR with
the addition of a net regularizer, or a custom SLR classifier. Eleven
studies, on the other hand, chose SVM, with variations including
3-dimensional SVM, two-class SVM, multi-class SVM, or the addition of
Effect Mapping. Only one study used a log likelihood-ratio-based pattern
classifier (Iordan et al., 2020). Berman et al. (2023) used the *Stimulus
Intensity Independent Pain Signature* (SIIPS) decoder developed by Woo
et al. (2017), which employs a multivariate statistical approach. Lastly,
Ciarlo et al. (2022) and Russo et al. (2021) used *Representational Simi-
larity Analysis* (RSA) to implement semantic neurofeedback (see Box 1 for
summary of decoding methods).

Applying the decoder in real-time requires several pre-processing
steps. While some of these steps are commonly used, others are quite
rare. In the included studies, motion correction was applied by most,
while the remaining studies applied realignment or co-registration. Only
two studies used both. Linear detrending, z-scoring and smoothing were
also widely used. In contrast, slice time correction, temporal high-pass
filtering, segmentation, masking to exclude non-brain voxels or
include only grey matter, or a GLM to remove nuisance signals, were
used by only one or two studies. Berman et al. (2023) also implemented
normalization to the MNI as their classifier was built in the MNI space.
Ciarlo et al. (2022) and Russo et al. (2021) employed a GLM for
pre-processing, incorporating task time-courses as predictors and mo-
tion parameters as confounds. These studies used RSA, requiring
different pre-processing steps. Importantly, three studies did not report
any pre-processing steps.

Finally, calculation of the feedback signal depends on the classifi-
cation method used. Among the articles using SLR, most studies calcu-
lated the feedback signal as directly proportional to the estimated
likelihood of the target brain pattern. Of those, three studies applied
specific formulas to adjust the minimum and maximum values. Collin
et al. (2022) and Iordan et al. (2020) instead increased or decreased the
feedback image by a fixed amount depending on whether decoder
performance or estimated likelihood exceeded a specific threshold. A different approach was implemented by Oka et al. (2024), in which the decoder performance was used to trigger stimuli when it exceeded 95 %. On the other hand, for SVM classifiers, two approaches can be identified. Most papers based the feedback signal on the hyperplane distance predicted by the classifier (Fig. 1B), with two studies applying specific formulas to adjust these values. The remaining four papers used only correct class prediction. Specifically, Bagarinao et al. (2018) and Lee et al. (2019) relied on correct class classification to indicate movement in the desired direction, as they used a multi-class classifier, while Pereira et al. (2022) and Sitaram et al. (2011) updated the feedback display by adding or removing a bar from the thermometer display based on correct or incorrect classification. In Ciarlo et al. (2022) and Russo et al. (2021), the feedback signal reflected the dissimilarity between the current brain activation pattern and the baseline pattern projected into a representational space.

### 3.3. Neural and behavioural effects of neurofeedback

In this section, we discuss the effectiveness of MVPA-based neurofeedback in modulating brain states and behaviour across several domains. To enhance cross-study comparability and practical usability, we synthesized key design features across studies into a concise integrative table (Table 3), linking decoder targets, feedback signal definitions, comparison conditions, and observed neural and behavioural effects.

#### 3.3.1. Associative learning

Associative decoded neurofeedback aims to create an association between a specific brain pattern and a target stimulus (Taschereau-Dumouchel et al., 2021). This approach was used by Amano et al. (2016) and Knotts et al. (2019) to establish an association between the colour red and orientation or movement, respectively. While Knotts et al. (2019) did not observe any changes in decoder likelihood across training, Amano et al. (2016) found an increase during training compared to baseline. Despite the different neural effects observed, both studies found an increase in red perception frequency after training, with Amano et al. (2016) finding that this effect persisted at 3-5 months follow-up. Similarly, Margolles et al. (2024) intended to pair the mental representation of dogs with a specific hiragana character. They found a linear improvement of classifier performance across training, which did not translate in any behavioural effect in a visual search task. Collin et al. (2022) sought to create an association between objects and context. They found that above chance performance was only observed during the last block of training and not in previous blocks. However, while they managed to create an association between objects and context, this did not have any effect on participants' performance in an associative learning task. Lastly, Oblak et al. (2021) tried to associate presses of one finger with the mental representation of the adjacent finger. In particular, they wanted to associate ring-little fingers and middle-index fingers. To establish successful regulation, they calculated a bias score based on the similarity of the mental state to the target finger and the dissimilarity to the non-target finger. They found that participants were not able to increase the middle finger bias score but were able to increase the ring finger bias score, suggesting a difference in modulation ability between different fingers. However, no behavioural effect was observed in a task aimed to assess hand representation.

#### 3.3.2. Emotion regulation

Li et al. (2016) and Moll et al. (2014) examined the ability to modulate positive emotions, such as happiness and tenderness/affection. Both papers found an increase in the percentage of positive trials when comparing last vs first run, while no improvement was observed in the control group. Moreover, Li et al. (2016) found a trend towards significance in the positive subscale of the *Positive And Negative Affect Scale* (PANAS) in the experimental group, which was not

observed in the negative subscale nor in the control group. Lorenzetti et al. (2018) also found an improvement in emotion regulation across runs for both tenderness and anguish. Shibata et al. (2016) explored the ability to induce changes in facial preference and measured regulation success as the change in the induced activation shift, i.e. how far an induced activation pattern during the induction stage was shifted away from the activation pattern for the average behavioural preference rating for each subject. They found increased induced activation shift in the high-preference group, decreased activation shift in the low-preference group and a linear improvement of classifier performance across training. Moreover, Shibata et al. (2016) found increased preference rating in the higher-preference group, decreased ratings in the lower-preference group and no difference for the control group after neurofeedback training. These changes in preference rating were correlated with the induced activation shifts, such as an increase in induced activation shift resulted in an increase in preference ratings. Sitaram et al. (2011) also explored the ability to modulate emotional states and found an improvement in classification performance in the second session, which was maintained in the third.

Emotion regulation was also explored in clinical populations. Pereira et al. (2022) looked at induction of happiness in patients with depression and found an improvement in classifier accuracy comparing last vs first run and last vs first day. Neurofeedback training also resulted in clinical improvement on both HDRS and BDI-II, which persisted at 10-day follow-up. Weaver et al. (2020) and Calvert et al. (2022) recruited patients with PTSD and compared the ability to modulate emotional states between patients and control groups rather than across training. Calvert et al. (2022) exposed participants to a trauma-related script and instructed them to either engage or disengage with the memory. They found that both groups showed greater engagement with the stimuli in the engage condition than the disengage condition, but the PTSD group showed a higher engagement. Moreover, feedback helped participants engage more in the task in the 'engage' condition but did not influence participant's engagement in the 'disengage' condition. They also observed a decrease in skin conductance response across runs, but no main effect of group, condition or feedback. Weaver et al. (2020) found that patients with PTSD were less able to modulate their brain activation compared to controls, and this impairment was more prominent for trauma-related stimuli.

#### 3.3.3. Fear reduction

Koizumi et al. (2016) aimed to achieve counterconditioning by pairing the feared stimuli to reward. They found that participants were able to successfully learn to induce the target pattern, reaching above level performance on day two and three. Similarly, Tascheau-Dumouchel et al. (2018) found increased likelihood for the target decoder across training, which was not observed for the control decoder. In both studies, training resulted in a decreased fear response to the target stimuli, which, however, was not correlated with the induction likelihood during training. Lastly, Cushing et al. (2023) also found that participants were able to successfully activate the target pattern. They included an affective Stroop task to investigate behavioural effects and found a borderline significant interaction between phobia type (target/control) and time (pre/post), so that reaction time to the target phobia were faster after neurofeedback training than they were to the control phobia.

#### 3.3.4. Motor rehabilitation

Bagarinao et al. (2018) found an increase in decoder accuracy in the first and second run, but not in the last run. Similarly, Lee et al. (2019) found increased decoder accuracy during a mental motor practice task after neurofeedback training. This effect was not observed in the control group, which underwent training without feedback.

#### 3.3.5. Visual perceptual learning

Shibata et al. (2011) aimed to induce an activity pattern
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** (A) Forest plot showing the efficacy of MVPA-neurofeedback using the Paule-Mandel model estimator. The plot depicts effect sizes with standard errors. Symbol size reflects sample size. The overall effect is moderate, but significant (0.39). (B) Funnel plot displaying effect against standard error for MVPA-neurofeedback studies.</figcaption></figure>

corresponding to a specific orientation and found a linear increase in pattern induction across training, which resulted in improved sensitivity in an orientation discrimination task after neurofeedback training. Moreover, the sensitivity change was correlated with the change in likelihood induction. Similarly, Wang et al. (2021) induced an activity pattern corresponding to local motion and found a significant difference in decoder accuracy between the first and the last run, with participants showing linear improvement across training. This resulted in improved sensitivity to the target local motion after neurofeedback training. Lastly, Jordan et al. (2020) aimed to use neurofeedback training to sculpt categories for complex visual objects that did not previously exist in the brain. They found an increase in decoder likelihood from first to last session for the target category, while no change was observed for the control category. They also observed an improvement in discrimination performance for the target category, but did not include any pre-post comparison. Moreover, they observed a strong correlation between the neural and the perceptual change.

### 3.3.6. Craving reduction

Fede et al. (2023) aimed to reduce alcohol craving and trained participants to decrease brain activation patterns corresponding to craving when shown alcohol images. They found a significant increase in the rate of successful regulation trials between first and last run, but no change in craving rating after training. Interestingly, Kim et al. (2024) used both individual and group-level classifiers with the aim to compare their performance on smoking craving reduction. They found that

classifier performance increased across training runs for both classifiers, but they observed greater accuracy for the individual-level classifier, suggesting its increased utility compared to the group-level classifier.

### 3.3.7. Attention

deBettencourt et al. (2015) aimed to improve sustained attention. They found that the feedback group showed a greater increase in classifier accuracy than the control group, suggesting that they were better able to modulate their attentional state. This resulted in improved sensitivity in a sustained attention task after neurofeedback training in the experimental group, while this effect was absent in all control groups. Moreover, improved sensitivity was correlated with increased decoder likelihood. The design applied by Oka et al. (2024) is quite different from other studies, in that they used decoder accuracy to trigger stimuli. Specifically, when decoder accuracy was above 95 %, a stimulus was triggered to which the participant had to react by either approaching or avoiding. Oka et al. (2024) investigated changes in brain activation during approach-avoid task and found reduced activation in the precuneus, cuneus and cerebellum vermis when pushing away gaming images after training. Behaviourally, they found a decrease in the number of positive trials, i.e. a reduction in attention to gaming stimuli, which however did not reach significance. Moreover, there was no change in the *Problematic Online Gaming Questionnaire* (POGQ) after training.
<figure><img src="image_5.png" /><figcaption>**Fig. 4.** (A) Forest plot showing the efficacy of MVPA-neurofeedback using the Paule-Mandel model estimator. The plot depicts effect sizes with standard errors. Symbol size reflects sample size. The overall effect is moderate, but significant (0.45). (B) Funnel plot displaying effect against standard error for MVPA-neurofeedback studies in emotion regulation.</figcaption></figure>

### 3.3.8. Semantic feedback

Semantic neurofeedback is based on real-time RSA and aims to train participants to move between different mental representations. Russo et al. (2021) investigated whether a single participant could modulate its brain activation by imaging different concrete objects. They found that the participant was able to modulate the mental representation of the instructed stimulus so that to reduce the distance, i.e. dissimilarity, with the target representation. The same method was later applied by Ciarlo et al. (2022) in a multi-subject study. They found that participants were able to reduce the distance between the target and the current mental state in the last run compared to the first run in each training session, but there was no improvement between sessions.

### 3.3.9. Others

Cortese et al. (2016) used decoded neurofeedback to manipulate confidence bi-directionally and found that induction success was slightly higher in day 2 compared to day 1, independent of condition. Moreover, they observed an increase in confidence rating after high-confidence training session and a decrease after low-confidence session, with no change in behavioural performance. Induction success was also correlated with confidence change, so that higher induction success resulted in greater confidence change. However, this correlation was mainly

driven by the high-confidence session as no significant correlation was found between induction success and confidence changes in the low-confidence session.

Berman et al. (2023) trained participants to either upregulate or downregulate SIIPS using neurofeedback and found that participants in the down-regulation group showed a decreased SIIPS expression over the course of training, but no change in SIIPS expression was observed in the upregulation group. This was further supported by pain ratings, as a decreased decoder induction was correlated with a decrease in pain ratings in the downregulation group, while no correlation was observed in the upregulation group.

## 3.4. Neural effects of MVPA-based neurofeedback

Twenty studies were included in the global meta-analysis. Domains and decoded target states for these studies are summarized in Table 3 (* indicates studies included in the meta-analysis). The analysis revealed a moderate, statistically significant effect size (0.39, SE = 0.12, *p* = 0.0016), suggesting improvements in classifier accuracy following neurofeedback training (Fig. 3A). Heterogeneity between studies was high (τ² = 0.27, SE = 0.10; I² = 93.32%; Q(19) = 244.54, *p* < 0.001; H² = 14.96). The funnel plot suggested asymmetry (Fig. 3B), and Egger's
regression test was statistically significant ($\tau = 2.0, p = 0.04$), indicating potential publication bias.

To assess the robustness of these results, we conducted a supplementary analysis using the alternative feedback dataset. This analysis also yielded a statistically significant effect (0.28, SE = 0.14, $p < 0.05$), with high heterogeneity ($\tau^2 = 0.34, SE = 0.12; I^2 = 94.42\%$; Q(19) = 235.86, $p < 0.001$; $H^2 = 17.91$) and no indication of publication bias (Egger's regression: tau = 0.25, $p = 0.8$). Forest and funnel plots for this supplementary analysis are shown in Supplementary Figure S1A and S1B, respectively.

These results provide preliminary evidence that MVPA-based neurofeedback may induce neural modulation as reflected in classifier performance. However, the substantial heterogeneity across studies limits the confidence with which this conclusion can be drawn.

### 3.4.1. Emotion regulation

Five studies were included in the subgroup domain-specific meta-analysis focused on emotion regulation. The analysis revealed a moderate, statistically significant effect size ($0.45, SE = 0.13, p < 0.001$), indicating improvements in classifier-derived neural measures following neurofeedback training within this domain (Fig. 4C).

Heterogeneity was moderate ($\tau^2 = 0.03, SE = 0.05$), with an $I^2$ value of 47.15%. Cochran's Q test approached statistical significance ($Q(4) = 7.91, p = 0.09$), and the $H^2$ statistic was 1.89.

Publication bias for this subgroup was evaluated with a funnel plot and Egger's regression. The funnel plot indicated some dispersion of effect sizes (Fig. 4D), and Egger's test showed no significant asymmetry ($\tau = -0.62, p = 0.53$).

## 3.5. Quality assessment

In this section, we assess the quality of published studies (NHLBI, 2021) and reporting quality according to the CRED-nf checklist (Ros et al., 2020).

### 3.5.1. NHLBI risk of bias

The quality assessment, conducted using appropriate tools based on study design, revealed that most studies were rated as "Fair," with five studies rated as "Good" and two as "Poor" (Table S2). For controlled intervention studies, almost all studies did not report randomisation methods, whether treatment allocation was concealed and blinding. Moreover, no study justified the sample size with power calculations. However, none of these studies were randomized-clinical trials (RCT) and the majority were defined as pilot or proof-of-concept studies. The only RCT included was rated as "Good". Similarly to controlled intervention studies, case-control and pre-post studies lacked reporting of sample size justification and blinding.

### 3.5.2. CRED-nf checklist

The CRED-nf checklist for all included studies is reported in Table S3.

**Pre-experiment:** Only two studies (Calvert et al., 2022; Cushing et al., 2023) were pre-registered, with one study being a pilot study for a pre-registered clinical trial (Fede et al., 2023). For sample size justification, only one study conducted a power analysis, while five studies provided justification based on previous or pilot studies. Seven studies were defined as pilot or proof-of-concept study. The remaining studies did not provide any justification.

**Control groups:** Eighteen of the included studies employed control groups or conditions, ranging from healthy control groups, no neurofeedback training groups, and pre-post behavioural task groups. Because of the study design, most studies could not implement a double-blind design. Of those who could, only three applied it. In those three studies, one examined participants and experimenter blinding, one examined just participants blinding, and one did not report any blinding assessment. Interestingly, some studies employing implicit designs assessed participants' blinding. In terms of clinical efficacy, only three

studies included clinical populations and none of them included a standard-of-care intervention group. However, these studies were all classified as pilots.

**Control measures:** Only seven studies collected and reported data on psychosocial factors. All studies specified whether participants were provided with a strategy or not and the majority reported the strategies participants used during training. Of the ten studies that did not report the strategies, seven of them instructed participants with specific strategies and explicit instructions. In terms of online preprocessing, this was reported by almost all studies, apart from three studies. No study employing a control condition/group reported condition/group effects for artifacts.

**Feedback specification:** Information on feedback specification was reported by most studies. All studies reported feedback modality and content, brain activity contrasts used for feedback and hardware and software used. Moreover, online-feature extraction was not reported by just one study. In terms of the reinforcement schedule, only four studies did not report it. However, these studies did include some information, such as number of sessions or length of runs.

**Outcome measures – Brain:** All studies reported regulation success based on feedback signal, except Oka et al. (2024). However, Oka et al. (2024) used classifier accuracy to trigger the stimuli and therefore this analysis could not have been conducted. Only nine studies plotted both within- and between-session contrasts, while seven studies reported only between-sessions and thirteen did not plot any contrasts at all. In the studies including a control group, only two studies did not conduct any comparison between the experimental and control groups.

**Outcome measures – Behaviour:** Twenty-three studies included measures of clinical or behavioural significance and thirteen of them investigated the correlation between regulation success and behavioural changes.

**Data storage:** Only four studies uploaded all materials and scripts to an open access repository.

## 4. Discussion

This systematic review investigated the effectiveness of MVPA-based fMRI neurofeedback in modulating brain states and influencing cognition and behaviour. Most studies demonstrated that participants could successfully modulate their brain activation patterns through neurofeedback training. A cross-study summary of all included studies, including decoder targets, feedback signal definitions, comparison conditions, and reported neural and behavioral effects, is provided in Table 3. Our global meta-analysis provides further preliminary evidence that MVPA-based neurofeedback training may induce neural modulation; however, the small number of studies and substantial heterogeneity limit the strength of this conclusion. Studies on fear reduction and visual perceptual learning showed that neurofeedback training led to neural modulation as well as decreased fear response and increased sensitivity to the target stimuli, respectively. In the attention domain, deBettencourt et al. (2015) found that modulation of the target brain pattern correlated with improved task performance, while Oka et al. (2024) reported changes in brain activation pre- and post-training. Interestingly, research on bidirectional regulation of confidence (Cortese et al., 2016) and pain (Berman et al., 2023) indicated that participants were only able to modulate brain activity in one direction, which was mirrored in behavioural outcomes. For emotion regulation, studies consistently supported the modulation of target brain patterns, though only two studies (Li et al., 2016; Shibata et al., 2016) explored and found behavioural improvements. Subgroup domain-specific analysis focusing specifically on emotion regulation supports these findings, providing the first evidence that neurofeedback training significantly enhances emotion regulation, as indicated by classifier performance. Emotion regulation has also been explored in clinical populations. While Pereira et al. (2022) demonstrated that patients with depression could induce a happiness-related brain state, leading to reductions in
Box 2

Best practice recommendations for MVPA-based neurofeedback studies.

A. Terminology

Use precise and consistent terminology to differentiate between MVPA-based neurofeedback approaches (e.g., DecNef, associative, semantic, adaptive) and classifier types (e.g., SVM, LR, RSA)

Clearly state whether the design is implicit or explicit, and specify the level of blinding (e.g., participant, experimenter)

B. Study Design and Methodological Rigor

Include appropriate control conditions (e.g., yoked feedback, sham feedback, active behavioural tasks)

Incorporate behavioural or clinical outcome measures

Include a priori power calculations to ensure adequate sample size

Implement double-blind procedures wherever possible

C. Motion and Signal Quality

Continuously monitor head motion during decoding and induction sessions

Consider using prospective motion correction or trial-wise exclusion

Optimise and report real-time preprocessing pipelines (e.g., normalization, detrending) to ensure reliable decoding

D. Future Directions

Develop consensus guidelines and share open-source pipelines to enhance reproducibility

depression scores, Calvert et al. (2022) and Weaver et al. (2020) found that patients with PTSD exhibited lower modulation ability compared to controls, but no clinical measures were assessed. Similarly, associative learning and craving reduction studies both found neural modulation that, however, did not translate in any behavioural effects. Only Amano et al. (2016) and Knotts et al. (2019) found a behavioural effect in associative learning, though the latter did not observe neural modulation. Lastly, studies on motor rehabilitation and semantic neurofeedback demonstrated neural modulation but lacked behavioural assessments.

Overall, this systematic review highlights the potential of neuro-
feedback in fostering neural and behavioural change, but also points out
that the field is still in its early stages and evidence for consistent
behaviour or neural effects remains limited. Several methodological
limitations further restrict the strength of conclusions that can be drawn.
The methodological quality across studies was generally low, with
typical scores between 50 % and 55 %, reflecting the exploratory nature
of most studies. Common limitations included: lack of control group,
absence of blinding and randomisation and no power analyses. Even
when using the CRED-nf checklist to evaluate neurofeedback-specific
criteria, these limitations on experimental control emerged. Collec-
tively, these issues underscore that the current evidence base is pre-
liminary and that more rigorous, well-powered and standardised studies
are needed to establish reliable neural and behavioural effects of MVPA-
based neurofeedback.

Moreover, although most studies demonstrated neural modulation,
behavioural findings show inconsistencies. First of all, it is important to
notice that there are less reported data in terms of behavioural
improvement as not all studies included a behavioural outcome mea-
sure. In addition, methodological issues (e.g. absence of enough power)
could have contributed to these mixed results, highlighting the need for
standardised methodology. Power calculations for MVPA neurofeedback
studies are particularly challenging to perform a priori given the novelty
of the field. Future studies should integrate well-powered designs and
behavioural assessments aligned to the targeted brain function. More-
over, collaborative initiatives, such as the ENIGMA neurofeedback
group, could help overcome these limitations by providing larger,
shared datasets, enabling more robust meta-analyses and more accurate
predictions of intervention success.

Our global meta-analysis provides preliminary support that MVPA-
based neurofeedback can induce measurable neural modulation across
domains, with a moderate overall effect. Nevertheless, heterogeneity
across studies was substantial, reflecting the lack of standardisation
across studies. Differences in experimental design, neurofeedback tar-
gets, outcome measures, sample characteristics, and feedback protocols
all contribute to variability in observed effects, limiting the general-
isability of the findings and indicating that effects may not be uniform
across protocols. Moreover, while using classification accuracy allows
comparison between studies using different decoding methods, it also
limits interpretability. Classification accuracy reflects categorical

discriminability rather than the magnitude of the neural representation
of the target state, and differences in baseline symmetry across tasks
limit comparability of observed changes. Indeed, similar changes in
classification accuracy could reflect distinct underlying neural phe-
nomena, such as improved separability between classes or successful
induction of a low-base-rate state. Finally, although we did not formally
analyse moderators, future work could explore how factors such as
classifier accuracy, number of training sessions, or feedback modality
relate to neurofeedback success, and investigate pre-screening measures
to distinguish learners from non-learners. Such analyses would provide
valuable guidance for designing more effective and replicable MVPA-
based neurofeedback protocols.

Subgroup domain-specific analysis focusing specifically on emotion
regulation indicated a similar moderate effect with lower heterogeneity,
suggesting that neural changes in this domain may be relatively
consistent. This effect is in the same range than the one reported by Pindi
et al. (2022) for univariate neurofeedback (g = 0.46, p = 0.01). This
suggests that both approaches may yield similar effects. Nevertheless,
effect size comparisons alone do not address potential differences in how
univariate and multivariate approaches target emotional representa-
tions. It could be argued that MVPA-based approaches may offer con-
ceptual advantages for rt-fMRI neurofeedback in emotion regulation.
Different emotions often activate distributed and partially overlapping
brain regions (Kragel and LaBar, 2016; Todd et al., 2020). For example,
the amygdala has been shown to respond to multiple emotional states,
both positive and negative (Todd et al., 2020). Moreover, Chang et al.
(2015) demonstrated that although similar brain regions are implicated
in negative emotional responses and somatic pain, the underlying
multivariate activation patterns are largely uncorrelated. This suggests
that MVPA can dissociate overlapping neural representations that
cannot be distinguished using univariate methods. Therefore, while
univariate neurofeedback may be sufficient for simpler paradigms,
MVPA-based approaches may be better suited for more complex designs
involving multiple or overlapping emotional states.

As a relatively novel field, MVPA-based neurofeedback requires
continued investigation to address these challenges. One of the critical
areas for clarification lies in the terminology used across studies,
particularly the distinction between MVPA-neurofeedback and decoded
neurofeedback (DecNef). MVPA-based neurofeedback refers to all neu-
rofeedback studies using MVPA to provide feedback to participants
(Hampson, 2021), while DecNef is a specific subset of
MVPA-neurofeedback in which an implicit approach is taken, and the
feedback is often paired with monetary reward (Taschereau-Dumouchel
et al., 2021). Therefore, not all MVPA-neurofeedback studies employ an
implicit approach, and it is important to not interchange the two terms.
In addition, we can also identify associative neurofeedback, when
DecNef is used to create a non-conscious association between a brain
activation pattern and a stimuli (Amano et al., 2016). Two other cate-
gories of MVPA-neurofeedback can be also identified: semantic and
<table><caption>**Table 1**
Summary of neurofeedback training protocols across various studies.</caption><thead><tr><th>Authors</th><th>Experimental Design</th><th>Training</th><th>Neurofeedback run periods and timing</th><th>Feedback</th><th>Instructed strategies</th></tr></thead><tbody><tr><td>Amano (2016)</td><td>associative neurofeedback</td><td>3 sessions / 11.9 ± 0.1 runs / 15 trials</td><td>6 s regulate / 7 s rest / 1 s feedback / 6 s ITI</td><td>intermittent visual feedback, size of a grey disk + monetary reward</td><td>IMPLICIT<br>Make the size of the solid grey disk as large as possible</td></tr><tr><td>Bagarinao (2018)</td><td>MVPA-based neurofeedback</td><td>2 session / 4 runs / 8 trials</td><td>30 s rest / 30 s regulate</td><td>continuous visual feedback, live video feed of a humanoid robot's arm movement</td><td>IMPLICIT<br>Motor imagery task</td></tr><tr><td>Berman (2023)</td><td>decoded neurofeedback</td><td>3 sessions / 8 runs / 16 trials + 1 session / 3 runs / 16 trials</td><td>1.7 s rest / 5.2 s regulate / 5.2 s rest / 5.2 s feedback / 5.2 s ITI</td><td>intermittent visual feedback, size of grey disk + monetary reward</td><td>IMPLICIT<br>Maximize monetary reward</td></tr><tr><td>Calvert (2022)</td><td>MVPA-based neurofeedback</td><td>1 session / 2 runs / ? trials</td><td>instructions (engage or disengage) / 20 s regulate / ? rest</td><td>continuous visual feedback, opacity of visual stimuli (trauma script)</td><td>EXPLICIT<br>Proposed strategies from a list</td></tr><tr><td>Ciarlo (2022)</td><td>semantic neurofeedback</td><td>2 session / 3 runs / 10 trials</td><td>20 s rest / 20 s regulate / 20 s rest / 4 s feedback</td><td>intermittent visual feedback, red star in representational space with movement trajectory representing the change from the previous feedback</td><td>EXPLICIT<br>Imagine the target object</td></tr><tr><td>Collin (2022)</td><td>associative neurofeedback</td><td>1 session / 2 block (one for each target category) / 3 runs / ? trials</td><td>2 s stimuli (object) / 22 s regulate</td><td>continuous visual feedback, color of a circle and visibility of a hand with the thumb pointing up + monetary reward</td><td>EXPLICIT<br>Vividly imagine instructed object (faces or houses)</td></tr><tr><td>Cortese (2016)</td><td>decoded neurofeedback</td><td>4 sessions / 10 ± 0.1 runs / 16 trial</td><td>6 s regulate / 6 s rest / 2 s feedback / 6 s ITI</td><td>intermittent visual feedback, size of a grey disk + monetary reward</td><td>IMPLICIT<br>Make the size of a solid white disc as large as possible</td></tr><tr><td>Cushing (2023)</td><td>decoded neurofeedback</td><td>1,3 or 5 session / ? Runs / 16 trials</td><td>6 s rest / 6 s regulate / 4 s rest / 2 s feedback</td><td>intermittent visual feedback, size of a circle + monetary reward</td><td>IMPLICIT<br>Get the best feedback</td></tr><tr><td>deBettencourt (2015)</td><td>adaptive neurofeedback</td><td>1 session / 6-9 runs / 8 trials</td><td>1 s text cue (instruction) / 1 s rest / 50 s regulate / 4-6 s rest</td><td>continuous visual feedback, composition of visual stimuli (mixture of face and scene)</td><td>EXPLICIT<br>Sustained attention task</td></tr><tr><td>Fede (2023)</td><td>MVPA-based neurofeedback</td><td>Continuons: 2 sessions / 4 runs / 16 trials<br>Intermittent: 2 sessions / 4 runs / 18 trials</td><td>Continuous: 17 s regulate / 5 s average feedback display<br>Intermittent: 12 s regulate / 5 s feedback</td><td>continuos and intermittent visual feedback, thermometer + monetary reward</td><td>EXPLICIT<br>Given example strategies</td></tr><tr><td>Iordan (2020)</td><td>MVPA-based neurofeedback</td><td>5-6 sessions / 1-7 runs / 20 trials</td><td>16 s regulate / 12 s rest</td><td>continuous visual feedback, shape wobbling + monetary reward</td><td>IMPLICIT<br>Make each shape oscillate less or even stop</td></tr><tr><td>Kim (2024)</td><td>MVPA-based neurofeedback</td><td>1 session / 2 runs / 14 trials</td><td>28-46 s regulate / 12-16 s rest</td><td>continuous visual feedback, slider</td><td>EXPLICIT<br>Example strategies</td></tr><tr><td>Knotts (2019)</td><td>associative neurofeedback</td><td>3 sessions / 9.6 ± 0.4 runs / 16 trials</td><td>1 s cue / 6 s regulate / 6 s rest / 2 s feedback / 5 s ITI</td><td>intermittent visual feedback, size of black disk + monetary reward</td><td>IMPLICIT<br>Maximize the size of black feedback disc</td></tr><tr><td>Koizumi (2016)</td><td>decoded neurofeedback</td><td>3 sessions / 12 runs / 15 trials</td><td>6 s regulate / 7 s fixate / 1 s feedback / 6 s ITI</td><td>intermittent visual feedback, size of white disk + monetary reward</td><td>IMPLICIT<br>Maximize the size of white disc</td></tr><tr><td>Lee (2019)</td><td>MVPA-based neurofeedback</td><td>1 session / 40 steps in a virtual maze</td><td>10 s regulate / 10 s rest</td><td>continuous visual feedback, arrows/circle</td><td>EXPLICIT<br>Motor mental imagery</td></tr><tr><td>Li (2016)</td><td>MVPA-based neurofeedback</td><td>1 session / 3 runs / 6 trials</td><td>30 s rest / 30 s regulate</td><td>continuous visual feedback, thermometer</td><td>EXPLICIT<br>Happy or sad memories</td></tr><tr><td>Lorenzetti (2018)</td><td>MVPA-based neurofeedback</td><td>2 session / 3 runs / 8 trials</td><td>30 s rest / 46 s regulate</td><td>continuous visual feedback, background color of visual stimuli (virtual environment)</td><td>EXPLICIT<br>Enhance the target emotion</td></tr><tr><td>Margolles (2024)</td><td>associative neurofeedback</td><td>3 sessions / 8 runs / 18 trials + 1 session / 2 runs / 18 trials</td><td>6 s regulate / 6 s rest / 1 s feedback / 6 s ITI</td><td>intermittent visual feedback, size of red circle</td><td>IMPLICIT<br>Enlarge a feedback disk</td></tr><tr><td>Moll (2014)</td><td>MVPA-based neurofeedback</td><td>1 session / 3 runs / 8 trials</td><td>44 s regulate / 30 s rest</td><td>continuous visual feedback, rings distortion</td><td>EXPLICIT<br>Re-experience the emotion associated with their autobiographical episodes as intensely as possible</td></tr><tr><td>Oblak (2021)</td><td>associative neurofeedback</td><td>3 session / 8 runs / 20 trials</td><td>2 s cue / 10 s regulate / 1 s rest / 2 s feedback / 1 s ITI</td><td>intermittent visual feedback, thermometer + monetary reward</td><td>IMPLICIT<br>Maximize the height of the feedback thermometer</td></tr><tr><td>Oka (2024)</td><td>decoded neurofeedback</td><td>3 sessions / 3.4 ± 0.4 blocks (first day), 8.4 ± 0.4 (second day), 3.4 ± 0.3 (third day)</td><td>Participants were instructed to passively look at a fixation circle and a stimulus appeared when the decoder probability was above 95 %</td><td>No feedback presented</td><td>EXPLICIT<br>Approach-avoidance task</td></tr><tr><td>Pereira (2022)</td><td>MVPA-based neurofeedback</td><td>4 sessions / 4 runs / 8 trials</td><td>? s regulate / 20 s rest</td><td>continuous visual feedback, thermometer + monetary reward</td><td>EXPLICIT<br>Autobiographical happy memories or motor imagery</td></tr><tr><td>Russo (2021)</td><td>semantic neurofeedback</td><td>1 session / ? Runs / ? trials</td><td>20 s rest / 20 s regulate / 20 s rest / 5 s feedback</td><td>intermittent visual feedback, red star in representational space display</td><td>EXPLICIT<br>Imagine the target object</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>Authors</th><th>Experimental Design</th><th>Training</th><th>Neurofeedback run periods and timing</th><th>Feedback</th><th>Instructed strategies</th></tr></thead><tbody><tr><td>Shibata (2011)</td><td>decoded neurofeedback</td><td>5–10 sessions / 10.8 ± 0.3 runs / 15 trials</td><td>6 s regualte / 6 s rest / 2 s feedback / 6 s ITI</td><td>intermittent visual feedback, size of a green disk + monetary reward</td><td>IMPLICIT<br>Make the solid green disc as large as possible</td></tr><tr><td>Shibata (2016)</td><td>decoded neurofeedback</td><td>3 sessions / 10.8 ± 0.2 runs / 15 trials</td><td>0.5 s cue / 6 s regulate / 6 s rest / 2 s feedback / 5.5 s ITI</td><td>intermittent visual feedback, size of a green disk + monetary reward</td><td>IMPLICIT<br>Make the solid green disc large as possible</td></tr><tr><td>Sitaram (2011)</td><td>MVPA-based neurofeedback</td><td>1 session / 3 runs / ? Trials</td><td>4.5 s rest / 24 s regulate</td><td>continuous visual feedback, thermometer</td><td>EXPLICIT<br>Recall emotional episodes from their personal lives</td></tr><tr><td>Taschereau-Dumouchel (2018)</td><td>decoded neurofeedback</td><td>5 sessions / ? runs / 10.13 trials (day 3–4), 5.21 trials (day 1–5))</td><td>6 s regulate / 6 s rest / 2 s feedback / 6 s ITI</td><td>intermittent, visual feedback, size of a grey disk + monetary reward</td><td>IMPLICIT<br>Maximise monetary reward</td></tr><tr><td>Wang (2021)</td><td>decoded neurofeedback</td><td>3 sessions / 10.63 ± 1.5 runs / 15 trials</td><td>6 s regulate / 6 s rest / 2 s feedback / 6 s ITI</td><td>intermittent visual feedback, size of a green disk</td><td>IMPLICIT<br>Make the green disc as large as possible</td></tr><tr><td>Weaver (2020)</td><td>adaptive neurofeedback</td><td>1 session / 2 runs / 12 trials</td><td>30 s regulate / 8 s rest</td><td>continuous visual feedback, opacity of visual stimuli (face or word)</td><td>EXPLICIT<br>Attention task</td></tr></tbody></table>

ITI. Inter-trial interval.

adaptive neurofeedback. Semantic neurofeedback was introduced by Russo et al. (2021) and it uses RSA to provide feedback based on the similarity of the participant's brain state to the target brain state. Adaptive neurofeedback instead refers to a specific neurofeedback design in which brain activity is used to adjust the parameters (e.g., difficulty, stimuli) of an ongoing task (Weaver et al., 2020). Hence, while MVPA-neurofeedback identifies all different approaches, careful consideration needs to be taken when using the terms DecNef, associative, semantic or adaptive neurofeedback. For clarity and consistency, we provide a summary of these different methods and designs in Box 1.

Another point of confusion lies in distinguishing implicit and blind designs. Blinding helps control for biases and ensures the quality and validity of experimental studies (Monaghan et al., 2021). Blinding is quite challenging in neurofeedback studies, and it has been argued that implicit approaches allow double-blind designs (Kvamme et al., 2022). However, this is not necessarily true. Blinding refers to “the act of withholding information about the assigned interventions from people involved in the trial from the time of group assignment until the experiment is complete” (Monaghan et al., 2021), while using an implicit approach refers to participants' awareness of experimental purposes. Precisely, participants are not provided with any instructions or strategies, and they are unaware of experimental purposes, although they are aware of taking part in a neurofeedback study and that the feedback reflects their performance. Therefore, it can be argued that participants are only “partially blinded”. Three studies report implementing a double-blind design (Berman et al., 2023; Cushing et al., 2023; Taschereau-Dumouchel et al., 2018), but this can be debated for the latter two. Both studies used an implicit design, training one classifier for neurofeedback, while using another as control. Though participants and experimenters were unaware of the targeted classifier, participants knew they were receiving real feedback. Taschereau-Dumouchel et al. (2018) informed participants their feedback reflected brain activation, while Cushing et al. (2023) told participants they were receiving a treatment for their phobias and were actively controlling the feedback received. Therefore, while the design is blinded to the classifier, it is not blinded in the traditional sense. Implicit designs help reduce placebo effects and experimental biases, but they do not remove the need for a control group without veridical feedback.

MVPA-neurofeedback also involves specific methodological considerations. Compared to univariate methods (i.e., activation-based or connectivity-based neurofeedback), multi-voxel classifiers methods are particularly sensitive to motion artefacts (Magland and Childress, 2014) (Fig. 1C). Correct alignment between the decoder session and the induction session must be precise as it can be affected by minimal head movement, leading to imperfect decoding and feedback score

calculations (Cortese et al., 2021). However, only two studies (Knotts et al., 2019; Wang et al., 2021) calculated a correlation coefficient between the current data and the mean data acquired during the decoder session to exclude trials with significant head movement. While this method effectively removes problematic trials, it does not offer a way to directly account for motion artefacts within the signal itself. In real-time applications, preprocessing pipelines are simplified, and priority is given to fast algorithms that allow minimum processing delay, but there needs to be a balance between acceptable data quality and small processing time (Heunis et al., 2020). At the moment, there is great variation in the preprocessing steps implemented in these studies and it is unclear which steps have a significant valuable impact. Oblak et al. (2019) ran a simulation analysis and found that normalization and detrending were the only two steps that had a large effect on real-time decoding performance. However, more methods and simulation papers are needed to identify the best approach for MVPA-neurofeedback. A promising solution is prospective motion correction using optical tracking systems. These methods continuously monitor head position using MR-compatible external markers and update the image acquisition field in real time (Berglund et al., 2021). Unlike post hoc correction, optical tracking prevents motion artefacts at the source and does not interfere with the MR sequence or timing (Madore et al., 2023; Silic et al., 2024). Prospective motion correction has been shown to reduce motion artefacts and improve image quality (Nael et al., 2021). Moreover, because the correction occurs during acquisition, no additional reconstruction time is required, making it particularly attractive for real-time and neurofeedback applications. On the downside, this approach can be expensive, and requires additional setup and calibration time, which may limit its application. Finally, movements during training might also have a big impact on decoding accuracy. Magland and Childress (2014) found that although in most cases high classification accuracies were mostly explained by neural activity, in some cases they were influenced by task-correlated facial and head-movements. This further highlights the need for robust, real-time solutions to minimize motion-related noise throughout the entire neurofeedback process.

In addition, there is still an open debate over whether individual or group-level classifiers are preferable. Most studies used individual-level classifiers, which have the advantage of requiring minimal transformation in real-time (Taschereau-Dumouchel et al., 2021). However, this method relies on limited data from a single session and requires exposure to an additional session, which might be challenging for some patients, e.g. exposure to feared objects in phobic patients. An alternative is the use of group-level classifiers, which can be constructed using two different approaches. The first is to construct a classifier in the MNI space using a larger amount of data as done by Berman et al. (2023) and
<table><caption>**Table 2**
Summary of the methods used across various studies employing multivariate pattern analysis (MVPA)-based neurofeedback.</caption><thead><tr><th>Authors</th><th>Devices</th><th>Acquisition parameters</th><th>Classifier method</th><th>Online preprocessing</th><th>Calculation of Feedback signal</th></tr></thead><tbody><tr><td>Amano (2016)</td><td>3T MRI scanner (Verio, Siemens) with a head coil</td><td>gradient EPI sequence (33 contiguous slices; TR = 2 s, voxel size = 3 × 3 × 3.5 mm³, 0 mm slice gap; oriented parallel to the AC-PC plane)</td><td>SLR</td><td>-Motion correction<br>-BOLD signal time-course extraction from each voxel<br>-Linear trend removal and z-score normalization<br>-Average of BOLD signal during regulation period (shifted for hemodynamic delay)</td><td>decoder likelihood</td></tr><tr><td>Bagarinao (2018)</td><td>Siemens's Magnetom Trio (Siemens, Erlangen, Germany) 3T scanner with a 12-channel head coil</td><td>GE EPI sequence (TR = 2.0 s, TE = 30 ms, FA = 80°, 37 axial slices with a distance factor of 30 %, FOV = 192 mm, slice thickness = 3.0 mm, 64 × 64 matrix dimension, voxel size = 3.0 mm × 3.0 mm × 3.0 mm)</td><td>SVM</td><td>-Realignment to mean functional image<br>-Smoothing<br>-Mask to exclude outside brain voxels</td><td>correct category classification</td></tr><tr><td>Berman (2023)</td><td>3T MRI scanner (Prisma Siemens) with a 64-channel head coil</td><td>gradient EPI sequence (33 contiguous slices; TR = 0.867 s, TE = 20 ms, Acceleration factor PE = 2, voxel size = 3 × 3 × 3 mm 3, FOV = 192 × 192 mm, matrix size = 64 × 64, slice thickness = 3 mm, 0 mm slice gap, FA = 58°; oriented parallel to the AC-PC plane)</td><td>multivariate model (SIIPS)</td><td>-Realignment to a mean fMRI BOLD image<br>-Normalization to MNI<br>-Linear trends removal and z-score normalization<br>-Average of BOLD signal during regulation period (shifted for hemodynamic delay)</td><td>estimated output rating</td></tr><tr><td>Calvert (2022)</td><td>Philips 3T Achieva X-series MRI scanner (Philips Healthcare, Eindhoven, The Netherlands) with a 32-channel head coil</td><td>EPI sequence (TR/TE/FA = 2000 ms/30 ms/90°, FOV = 240 × 240 mm, matrix = 80 × 80, 37 oblique slices, ascending sequential slice acquisition, slice thickness = 2.5 mm with 0.5 mm gap, final resolution 3.0 × 3.0 × 3.0 mm³)</td><td>SVM</td><td>-Motion correction<br>-Incremental detrending<br>-Spatial smoothing<br>-Segmentation into gray matter, white matter and cerebrospinal fluid</td><td>hyperplane distance</td></tr><tr><td>Ciarlo (2022)</td><td>3T scanner (Siemens Magnetom Skyra, Siemens Healthcare, Erlangen, Germany)</td><td>multi-band accelerated EPI sequence (TR = 1000 ms, TE = 30 ms, number of axial slices = 48, matrix size = 96 × 96, FOV = 288 mm, slice thickness = 3 mm, interslice gap = 0 mm, multi-band accelerator factor = 4)</td><td>RSA</td><td>-Motion correction<br>-Spatial smoothing<br>-GLM computation with one predictor time-course for the imagery task, six motion parameters and a linear confound predictors</td><td>dissimilarity between current and base pattern</td></tr><tr><td>Collin (2022)</td><td>3T Siemens Prisma scanner (Siemens, Erlangen, Germany) with a 32-channel head coil</td><td>2D EPI sequence (voxel size 3.3 × 3.3 × 3 mm, TR = 2000 ms, TE = 30 ms, FA = 80°, Multi-Band acceleration factor = 2, FOV = 212 × 212 × 105 mm)</td><td>SLR + elastic net regularizer</td><td>-Motion correction<br>-Slice time correction<br>-GLM to remove nuisance signal</td><td>decoder perfomance compared to chance level</td></tr><tr><td>Cortese (2016)</td><td>3T MR scanner (Siemens, Trio) with a head coil</td><td>gradient EPI sequence (33 contiguous slices; TR = 2 s, TE = 26 ms, FA = 80°, voxel size = 3 × 3 × 3.5 mm³, 0 mm slice gap; oriented parallel to the AC-PC plane)</td><td>SLR</td><td>-Motion correction<br>-BOLD signal time-course extraction from each voxel<br>-Linear trend removal and z-score normalization<br>-Average of BOLD signal during regulation period (shifted for hemodynamic delay)</td><td>decoder likelihood</td></tr><tr><td>Cushing (2023)</td><td>3T Siemens Prisma scanner with a 32-channel head coil</td><td>multi-band sequence (acceleration factor = 6, phase encoding in the P-A direction, voxel size: 2.0 × 2.0 × 2.0 mm³, FOV: 208 × 208mm², num. slices: 72, TR: 1000 ms, TE: 37.00 ms, FA: 60°)</td><td>Multinomial SLR</td><td>-Realignment to a template image<br>-Detrending</td><td>decoder likelihood</td></tr><tr><td>deBettencourt (2015)</td><td>3T MRI scanner (Siemens Skyra) with a 16-channel head coil</td><td>GE EPI sequence (TR = 2 s, TE = 28 ms, 3 mm isotropic voxel size, 64 × 64 matrix, FOV = 192 mm, 36 slices)</td><td>penalized SLR</td><td>-Motion correction<br>-Masked to eliminate non-brain voxels<br>-Spatial smoothing<br>-Z-score normalisation</td><td>likelihood estimates of the relevant minus irrelevant category (with sigmoidal transformation for a range of 0.17–0.98)</td></tr><tr><td>Fede (2023)</td><td>3T Siemens Prisma MRI</td><td>EPI pulse sequence (TR = 2 s, FA: 90°, FOV: 24 × 24 cm, 38 mm slice thickness, 36 slices, multislice mode: interleaved)</td><td>SVM</td><td>Not reported</td><td>hyperplane distance</td></tr><tr><td>Iordan (2020)</td><td>3T Siemens Skyra scanner with a 64-channel head coil</td><td>EPI sequence (TR = 2000 ms, TE = 28 ms; 36 transversal slices; multislice mode: interleaved)</td><td>linearly discriminative</td><td>-Alignment to the template volume</td><td>log-likelihood ratio estimate</td></tr></tbody></table>

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<table><caption>**Table 2 (continued)**</caption><thead><tr><th>Authors</th><th>Devices</th><th>Acquisition parameters</th><th>Classifier method</th><th>Online preprocessing</th><th>Calculation of Feedback signal</th></tr></thead><tbody><tr><td>Kim (2024)</td><td>3T Siemens Tim-Trio scanner (Erlangen, Germany) with a 12-channel head coil</td><td>voxel size, 3 × 3 × 3 mm; FA = 64°; IPAT factor 2)<br>standard GE EPI sequence (TR = 2000 ms, TE = 30 ms, FOV = 220 × 220 mm², matrix size = 64 × 64, voxel size = 3.4 × 3.4 × 4.0 mm³, FA = 90°, and 33 interleaved slices)</td><td>loglikelihood-ratio-based pattern<br>three-dimensional SVM</td><td>-Temporal high-pass filtering<br>-Z-score normalisation<br>-Slice timing correction<br>-Motion correction</td><td>hyperplane distance</td></tr><tr><td>Knotts (2019)</td><td>3T MRI scanners (Siemens, Verio [N=15] or Siemens, Trio [N=2])</td><td>gradient EPI sequences (33 contiguous slices; TR = 2 s, TE = 26 ms, FA = 80°, voxel size = 3 × 3 × 3.5 mm³, 0 mm slice gap; oriented parallel to the AC-PC plane)</td><td>Iterative SLR</td><td>-Motion correction<br>-BOLD signal extracted, detrended and z-score normalised<br>-Average across regulation period (shifted for hemodynamic delay)</td><td>decoder likelihood</td></tr><tr><td>Koizumi (2016)</td><td>3T MRI scanner (Trio, Siemens) with a head coil</td><td>gradient EPI sequence (33 contiguous slices; TR = 2000 ms, TE = 30 ms, voxel size = 3 × 3 × 3.5 mm³, FOV = 192 × 192 mm, matrix size = 64 × 64, slice thickness = 3.5 mm, 0 mm slice gap, FA = 80°; oriented parallel to the ACPC plane)</td><td>SLR</td><td>-Alignment<br>-Motion correction</td><td>decoder likelihood</td></tr><tr><td>Lee (2019)</td><td>Siemens 3T Tim Trio scanner (Erlangen, Germany)</td><td>T2*-weighted single-shot EPI sequence (64 × 64 mm acquisition matrix, 192 × 192 mm FOV, 31 (interleaved) slices, voxel size of 3.0 × 3.0 × 3.3 mm, TE = 30 ms, TR = 2 s, FA = 90°, gap = 1 mm)</td><td>multi-class SVM</td><td>-Motion correction<br>-Spatial smoothing</td><td>correct category classification</td></tr><tr><td>Li (2016)</td><td>3T GE Discovery MR750 scanner (General Electric, Fairfield, Connecticut, USA) with a standard 8-channel birdcage head coil</td><td>standard GRE-T2* EPI sequence (TR = 2000 ms, TE = 30 ms, FOV = 220 mm, matrix size = 64 × 64, slices = 33, slice thickness = 3.5 mm, and FA = 80°)</td><td>SVM</td><td>-Realignment<br>-Spatial smoothing<br>-Masked to eliminate non-brain voxels<br>-Z -scoring of time-course</td><td>hyperplane distance</td></tr><tr><td>Lorenzetti (2018)</td><td>Site 1: 3T Philips Achieva<br>Site 2: 3T Siemens Magnetom Skyra</td><td>T2*-weighted EPI sequence (TR/TE = 2000/22 (ms), matrix = 64 × 64, FOV = 240 mm², FA = 90°, isotropic voxel = 3.75 mm³, 24 slices)</td><td>SVM</td><td>Not reported</td><td>hyperplane distance</td></tr><tr><td>Margolles (2024)</td><td>3T Siemens Magnetom Prisma-fit whole-body MRI scanner with a 64-channel head-coil</td><td>GE EPI sequence (TR = 2000 ms, TE = 28 ms, FA = 74°, FOV = 192 mm, 34 axial slices with no inter-slice gap and a 3 × 3 × 3.5 mm³ voxel resolution)</td><td>custom SLR using Keras</td><td>-Co -registration<br>-Linear detrending was applied and BOLD signal z-scored normalised</td><td>decoder likelihood</td></tr><tr><td>Moll (2014)</td><td>3T Achieva scanner (Philips Medical Systems)</td><td>T2*-weighted EPI sequence (TR = 2000 ms, TE = 30 ms, matrix = 64,664, FOV = 240 mm, FA = 90°, voxel size 363 mm; slice thickness = 5 mm, 22 slices)</td><td>two-class SVM</td><td>-Motion correction<br>-Spatial smoothing<br>-Normalization</td><td>hyperplane distance</td></tr><tr><td>Oblak (2021)</td><td>Siemens Skyra 3T scanner with a 32-channel head coil</td><td>EPI sequence (TR = 2 s; 36 slices; in-plane resolution 2.3 × 2.3 mm; 100 × 100 matrix size; 2.15-mm slice thickness; 0.15-mm slice gap; 2x multiband factor)</td><td>Multinomial SLR</td><td>-Linear trend removal and z-score normalisation</td><td>decoder likelihood</td></tr><tr><td>Oka (2024)</td><td>3T scanner (Prisma; Siemens, Erlangen, Germany) with a 64-channel head coil</td><td>T2*-weighted GE multiband EPI sequence (TR = 1750 ms, TE = 30.0 ms, FA = 70°, FOV = 200 × 200 mm², resolution = 2 × 2 mm², MB factor = 4, voxel size = 2 × 2 × 2 mm³, 76 interleaved axial slices (thickness = 2.0 mm, no gaps); parallel to the AC-PC line)</td><td>Iterative SLR</td><td>NA</td><td>NA</td></tr><tr><td>Pereira (2022)</td><td>3T Siemens Magnetom Skyra whole-body MRI scanner (Siemens Medical Solutions, Erlangen, Germany) with a 32-channel head coil</td><td>2D EPI sequence (TR/ TE/ FA = 2000 ms/ 30 ms/ 90°; FOV = 220 mm; 34 slices, 3 mm³ isotropic voxels, 3 mm slice thickness, 210 volumes and 34 slices, aligned with the AC-PC plane)</td><td>SVM + Effect Mapping</td><td>-Co -registration<br>-Smoothing</td><td>correct category classification</td></tr><tr><td>Russo (2021)</td><td>7T whole-body scanner (Magnetom; Siemens AG, Erlangen, Germany)</td><td>multi-band repeated GE EPI sequence (TR = 1000 ms, TE = 21 ms, number of axial slices = 2)</td><td>RSA</td><td>-GLM computation with one predictor time -course for the imagery task and estimated</td><td>dissimilarity between current and base pattern</td></tr></tbody></table>

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<table><caption>**Table 2 (continued)**</caption><thead><tr><th>Authors</th><th>Devices</th><th>Acquisition parameters</th><th>Classifier method</th><th>Online preprocessing</th><th>Calculation of Feedback signal</th></tr></thead><tbody><tr><td>Shibata (2011)</td><td>3T MR scanner with a head coil</td><td>60, matrix = 112 × 112, FOV = 224 mm, thickness = 2 mm, interslice gap = 0 mm, multi-band factor = 4)<br>gradient EPI sequence (33 contiguous slices; TR = 2 s, voxel size = 3 × 3 × 3.5 mm³, 0 mm slice gap; oriented parallel to the AC-PC plane)</td><td>multinomial SLR</td><td>-Motion correction<br>-BOLD signal time-course extraction from each voxel<br>-Linear trend removal and z-score normalization<br>-Average of BOLD signal during regulation period (shifted for hemodynamic delay)</td><td>decoder likelihood</td></tr><tr><td>Shibata (2016)</td><td>3T MR scanner (Siemens MAGNETOM Verio) with a 12-channel head matrix coil</td><td>radiant EPI sequence (33 contiguous slices; TR = 2 s, voxel size = 3 × 3 × 3.5 mm³, 0 mm slice gap, FOV = 192 mm × 192 mm, TE = 26 ms, matrix size = 64 × 64, bandwidth = 2367 Hz/pixel; phase encoding direction: from anterior to posterior, slice order: interleaved; oriented parallel to the AC-PC plane)</td><td>SLR</td><td>-Motion correction<br>-BOLD signal time-course extraction from each voxel<br>-Linear trend removal and z-score normalization<br>-Average of BOLD signal during regulation period (shifted for hemodynamic delay)</td><td>estimated output rating</td></tr><tr><td>Sitaram (2011)</td><td>3T TIM whole body scanner (Siemens Magnetom Trio Tim, Siemens, Erlangen, Germany) with a standard 12-channels head coil</td><td>TR = 1.5 s, matrix size = 64 × 64, echo time TE = 30 ms, FA=70°, bandwidth = 1.954 kHz/pixel</td><td>SVM</td><td>-Realignment to the first brain scan of the first run<br>-Spatial smoothing</td><td>correct category classification</td></tr><tr><td>Taschereau-Dumouchel (2018)</td><td>3T MRI scanners (Prisma Siemens and Verio Siemens) with a head coil</td><td>33 contiguous slices; TR = 2000 ms, TE = 30 ms, voxel size = 3 × 3.5 mm 3, FOV = 192 × 192 mm, matrix size = 64 × 64, slice thickness = 3.5 mm, 0 mm slice gap, FA = 80°; oriented parallel to the AC-PC plane</td><td>SLR</td><td>-Realigned to match the coordinates of the Decoder Construction session by realigning to the first EPI image acquired during this session.<br>-Motion correction<br>-Polynomial trends removal and signal standardisation</td><td>decoder likelihood</td></tr><tr><td>Wang (2021)</td><td>3T Siemens PRISMA MRI scanner with a 64-channel head coil</td><td>T2*-weighted EPI sequence (33 continuous slices; TR = 2 s, TE = 30 ms, FA = 90°, voxel size = 3 mm × 3 mm × 3 mm; oriented parallel to the AC-PC plane)</td><td>SLR</td><td>-Motion correction</td><td>decoder likelihood</td></tr><tr><td>Weaver (2020)</td><td>GE MR750 3T scanner with an 8-channel headcoil</td><td>EPI sequences (TR/TE/FA = 2000 ms/25 ms/60°, FOV = 24 cm, matrix = 64 × 64, 40 sagittal slices, slice thickness = 4 mm, original resolution was 4 × 3.75 × 3.75 mm, and images were resampled to a final isotropic 3 × 3 × 3 mm)</td><td>SVM</td><td>-Registration to the training data registration image<br>-Smoothing was applied<br>-Masked for gray matter<br>-Z-score normalisation and detrending</td><td>hyperplane distance (with sigmoidal transformation for a range of 0.17–0.83)</td></tr></tbody></table>

TR. Repetition time; TE. Echo time; FOV. Field of view; FA. Field angle; GE. Gradient-echo; EPI. Echo-planar imaging; AC-PC. Anterior commissure - posterior commissure. SLR. Sparse Logistic Regression; SVM. Support Vector Machine; SIIPS. Stimulus Intensity Independent Pain Signature; RSA. Representation Similarity Analysis.

Pereira et al. (2022). However, this will require extra steps for its application in real-time. The second method, called the hyperalignment method, was introduced by Taschereau-Dumouchel et al. (2018). This method allows to align functional data from surrogate participants to the native space of the “real” participants, thereby bypassing the need of exposure to an additional scanning session and eliminating the need of realignment in real-time. Comparison between the use of individual and group-level classifiers was only explored by Kim et al. (2024), who found an increase in classification accuracy with the individual-level classifier, but not with group-level classifier, concluding that individual classification outperforms group classification. However, more studies are necessary to explore this further and the approach chosen will likely reflect the aim of the study.

While MVPA-based neurofeedback continues to advance, current evidence remains insufficient to support clinical applications. Most studies to date are preliminary, underpowered, and primarily conducted

in healthy participants. Although promising proof-of-concept findings have emerged, more rigorous, well-controlled trials are necessary before MVPA-neurofeedback can be considered a viable clinical intervention. Future research should focus on refining experimental protocols, clarifying mechanisms of action, and establishing standardised procedures for data acquisition, analysis, and reporting. Collaborative initiatives, such as the ENIGMA working groups, could help address current limitations and further advance the field by providing larger shared datasets and facilitating more robust meta-analyses. To support these efforts, we provide a set of best practice recommendations in Box 2.

## 5. Conclusion

This review highlights that MVPA-based neurofeedback has the potential to induce neural changes across a range of domains. However, behavioural outcomes remain inconsistent, with only a subset of studies
<table><caption>**Table 3**

Cross-study summary of MVPA-based neurofeedback studies.</caption><thead><tr><th>Authors</th><th>Decoder targets</th><th>Feedback Signal</th><th>Control Condition</th><th>Neural Effects</th><th>Behavioural Effects</th></tr></thead><tbody><tr><td colspan="6"><strong>Associative learning</strong></td></tr><tr><td>Amano (2016)*</td><td>Red vs Green</td><td>Decoder likelihood</td><td>No-NFB control (MRI)</td><td>↑ decoder likelihood</td><td>↑ red perception</td></tr><tr><td>Collin (2022)*</td><td>Faces vs Houses</td><td>Classifier accuracy vs chance</td><td>Single-arm study</td><td>↑ classifier performance (last block); ↔ earlier blocks</td><td>↔ associative learning performance</td></tr><tr><td>Knotts (2019)</td><td>Red vs Green / High vs Low confidence</td><td>Decoder likelihood</td><td>Single-arm study</td><td>↔ decoder likelihood</td><td>↑ red perception (2AFC task)</td></tr><tr><td>Margolles (2024)*</td><td>Dog vs Scissors</td><td>Decoder likelihood</td><td>No-NFB control (MRI)</td><td>↑ classifier performance</td><td>↔ visual search performance</td></tr><tr><td>Oblak (2021)*</td><td>Little vs Ring finger</td><td>Decoder likelihood</td><td>Single-arm study</td><td>↑ ring finger bias; ↔ middle finger bias</td><td>↔ temporal order judgement</td></tr><tr><td colspan="6"><strong>Attention</strong></td></tr><tr><td>deBettencourt (2015)</td><td>Faces vs Scenes</td><td>Likelihood difference (relevant – irrelevant)</td><td>Sham feedback; No-NFB control (behaviour); reaction time feedback; sham reaction time feedback</td><td>↑ classifier accuracy</td><td>↑ sustained attention sensitivity</td></tr><tr><td>Oka (2024)</td><td>Positive vs Negative attentional bias</td><td>NA</td><td>Single-arm study</td><td>↓ activation (precuneus, cuneus, cerebellum vermis) during push-away task</td><td>↔ Problematic Online Gaming Questionnaire (POGQ)</td></tr><tr><td colspan="6"><strong>Confidence</strong></td></tr><tr><td>Cortese (2016)*</td><td>High vs Low confidence</td><td>Decoder likelihood</td><td>Within-subject bidirectional NF</td><td>↑ decoder likelihood (both arms)</td><td>↑ confidence (high-confidence); ↓ confidence (low-confidence)</td></tr><tr><td colspan="6"><strong>Craving</strong></td></tr><tr><td>Fede (2023)*</td><td>Look vs Crave</td><td>Hyperplane distance</td><td>Single-arm study</td><td>↑ regulation success</td><td>↔ craving rating</td></tr><tr><td>Kim (2024)*</td><td>Crave vs Don't crave</td><td>Hyperplane distance</td><td>Single-arm study</td><td>↑ classifier performance (individual); ↔ group</td><td>NA</td></tr><tr><td colspan="6"><strong>Emotion regulation</strong></td></tr><tr><td>Calvert (2022)</td><td>Engage vs Disengage</td><td>Hyperplane distance</td><td>Healthy control group</td><td>↑ hyperplane distance (engage); ↔ disengage</td><td>↓ fear response</td></tr><tr><td>Li (2016)*</td><td>Happy vs Sad</td><td>Hyperplane distance</td><td>No-NFB control (MRI)</td><td>↑ regulation success</td><td>↑ positive affect (PANAS)</td></tr><tr><td>Lorenzetti (2018)*</td><td>Tenderness vs Anguish</td><td>Hyperplane distance</td><td>Single-arm study</td><td>↑ decoder likelihood</td><td>NA</td></tr><tr><td>Moll (2014)*</td><td>Tenderness/Affection vs Pride</td><td>Hyperplane distance</td><td>No-NFB control (MRI)</td><td>↑ positive trials</td><td>NA</td></tr><tr><td>Pereira (2022)*</td><td>Happiness vs Motor imagery</td><td>Correct category classification</td><td>Single-arm study</td><td>↑ classifier accuracy</td><td>↑ clinical outcomes (HDRS, BDI-II)</td></tr><tr><td>Shibata (2016)*</td><td>High vs Low preference</td><td>Estimated output rating</td><td>Bidirectional NF; No-NFB control (MRI)</td><td>↑ activation shift (high-preference); ↓ activation shift (low-preference); ↑ classifier performance</td><td>↑ preference (high-preference); ↓ preference (low-preference); ↔ control group</td></tr><tr><td>Sitaram (2011)</td><td>Happy vs Disgust</td><td>Correct category classification</td><td>Single-arm study</td><td>↑ classifier performance (sessions 2–3)</td><td>NA</td></tr><tr><td>Weaver (2020)</td><td>Attend vs Ignore threat</td><td>Hyperplane distance</td><td>Healthy control group</td><td>↓ modulation (PTSD vs controls)</td><td>NA</td></tr><tr><td colspan="6"><strong>Fear</strong></td></tr><tr><td>Cushing (2023)*</td><td>Feared vs Other animal</td><td>Decoder likelihood</td><td>Single-arm study</td><td>↑ target decoder activation</td><td>↓ reaction time (affective Stroop, target phobia)</td></tr><tr><td>Koizumi (2016)*</td><td>Target vs Non-target stimuli</td><td>Decoder likelihood</td><td>Single-arm study</td><td>↑ classifier performance (days 2–3); ↔ day 1</td><td>↓ fear response</td></tr><tr><td>Taschereau-Dumouchel (2018)*</td><td>Feared vs Other animal</td><td>Decoder likelihood</td><td>Single-arm study</td><td>↑ decoder likelihood</td><td>↓ fear response</td></tr><tr><td colspan="6"><strong>Motion</strong></td></tr><tr><td>Bagarinao (2018)*</td><td>Left vs Right hand</td><td>Correct category classification</td><td>Within-subject cross-over (MRI no-feedback)</td><td>↑ decoder accuracy (runs 1–2); ↔ run 3</td><td>NA</td></tr><tr><td>Lee (2019)*</td><td>Walking forward / Turning left / Turning right / Touching a tree</td><td>Correct category classification</td><td>No-NFB control (MRI)</td><td>↑ decoder accuracy</td><td>NA</td></tr><tr><td colspan="6"><strong>Pain</strong></td></tr><tr><td>Berman (2023)*</td><td>SIIPS pattern</td><td>Estimated output rating</td><td>Bidirectional NF</td><td>↓ SIIPS expression (down-reg); ↔ up-reg</td><td>NA</td></tr><tr><td colspan="6"><strong>Semantic Feedback</strong></td></tr><tr><td>Ciarlo (2022)*</td><td>Cat / Dog / Hammer / Chair</td><td>Pattern dissimilarity</td><td>Single-arm study</td><td>↓ target-current distance (within sessions); ↔ between sessions</td><td>NA</td></tr><tr><td>Russo (2021)</td><td>Cat / Dog / Hammer / Chair</td><td>Pattern dissimilarity</td><td>NA</td><td>↓ target-current distance</td><td>NA</td></tr><tr><td colspan="6"><strong>Visual Perceptual Learning</strong></td></tr><tr><td>Iordan (2020)</td><td>Category 1 vs. Category 2 (abstract shape)</td><td>Log-likelihood ratio</td><td>Single-arm study</td><td>↑ decoder likelihood</td><td>↑ discrimination performance (2AFC task)</td></tr><tr><td>Shibata (2011)*</td><td>Target vs Non-target orientation</td><td>Decoder likelihood</td><td>No-NFB control (behaviour)</td><td>↑ decoder likelihood</td><td>↑ orientation discrimination sensitivity</td></tr><tr><td>Wang (2021)</td><td>Target vs Non-target sekuler display</td><td>Decoder likelihood</td><td>No-NFB control (MRI)</td><td>↑ decoder accuracy</td><td>↑ local motion sensitivity</td></tr></tbody></table>

<footnote>* indicates the studies included in the quantitative meta-analysis.</footnote>
demonstrating reliable effects. These mixed results likely stem from substantial variability in study designs, feedback protocols and outcome measures. Methodological limitations, such as small sample sizes and lack of appropriate control conditions, further constrain interpretation. Moreover, technical challenges specific to MVPA, including sensitivity to motion, remain unresolved. While promising proof-of-concept findings are reported, more rigorous, well-powered and systematic investigations are needed to establish reliable neural and behavioural effects. Future research should focus on refining protocols, methodological quality, clarifying underlying mechanisms and standardising protocol implementation and reporting, in order to advance MVPA-based neurofeedback toward robust and generalisable applications.

## Funding

This research did not receive supports from commercial or not-for-profit sectors. VF is funded by the WILL chair program of excellence of the University of Lille (obtained by PA and RJ), France. This work also received funding (attributed to RJ) from the French government managed by the National Research Agency (ANR) under the France 2030 program and the Precision Psychiatry Research Plan (PEPR PROPSY), reference ANR-22-EXPR-0006.

## Data availability

The data is provided in the submission.

## CRediT authorship contribution statement

**Valeria Finelli:** Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.
**Pierre Yger:** Writing – review & editing. **Cécile Bordier:** Writing – review & editing.
**Renaud Jardri:** Writing – review & editing, Visualization, Supervision, Methodology, Conceptualization.
**Paul Allen:** Writing – review & editing, Visualization, Supervision, Methodology, Conceptualization.

## Declaration of competing interest

The authors do not have any other competing interests to declare.

## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.neuroimage.2026.121814.

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