Participants

A total of 23 native Japanese speakers who attended a speed-reading training course at Sokudoku School (https://www.pc-sokudoku.co.jp/) were recruited as participants for this study. Four participants failed to accomplish the entire experiment. Consequently, a total of 19 participants were included in the analysis. Each participant spent 40 min in one training program; each participant took 9–200 training programs (Table 1). These 19 participants (7 females, 12 males), ranging between 20 and 46 years of age (mean age = 32.4 years, SEM = 2.13 years), were right-handed (assessed using a handedness inventory) [20], and had either normal or corrected-to-normal vision. The speed-reading training program at Sokudoku School primarily comprises six courses: (1) Expanding the field of view, involving a series of practices that show how to expand and focus the view during reading; (2) Shifting gaze points, focusing on improving reading speed and efficiency by controlling the movement of the eyes; (3) Word recognition automation, through extensive practice for the quick recognition of characters during reading; (4) Characters imaging, aiming to associate characters with images or create mental associations for memorization and understanding; (5) Association method, involving the use of association techniques to enhance character memory and connect information to existing knowledge points or images; (6) Parallel imaging, including techniques for parallel association, comparing two or more concepts side by side to facilitate deeper understanding. After extensive training in the speed-reading of Japanese texts, these participants were able to read such texts at a speed of 7,030–29,250 characters per minute (c/min; mean speed = 11,280.5 c/min, SEM = 2,214.2 c/min). In general, native Japanese speakers who had never undergone speed-reading training could read Japanese text at a speed of 506–3,610 c/min (mean speed = 1,069.6 c/min, SEM = 173.7 c/min). This difference showed that the selected participants were able to read Japanese text at a significantly faster speed than ordinary Japanese speakers.

Table 1 Characteristics of the training group and comparison group

In addition, 19 untrained participants (8 females, 11 males; mean age = 29.8 years, SEM = 1.26 years) were recruited from the Kyoto University postgraduate students. Prior to the current experiment, we ensured that the participants had no experience regarding formal training in speed reading. As the comparison group, they were assigned identical experimental tasks and questions to the training group. The detailed data and P-values for all characteristics are presented in Table 1 for both the training and comparison groups.

All participants provided written and verbal informed consent before the start of the experiment. Prior to entering the MRI scanner, they completed questionnaires at the MRI center and reported their current health conditions and medical histories, including physical injuries and mental disorders. All participants were fully debriefed and received enough payment. The experimental procedures for this study were approved by the Institutional Ethics Committee of Kyoto University and performed in accordance with the Declaration of Helsinki.

Experimental Stimuli

Experimental texts from diverse authors were selected from Japanese expository essays, encompassing all types of Japanese characters (approximately 53% hiragana, 13% katakana, and 32% kanji). Rigorous selection ensured participants’ unfamiliarity with the texts. We chose three texts with approximately the same total number of characters (around 27,000) in a whole experiment, edited for consistency, each of which was separated into nine 30-s videos. We maintained uniformity in the number of lines (three lines, around 60 characters) per page across the experimental texts. Categorizing videos based on varying presentation speeds (slow speed = 50 pages/min, medium = 100 pages/min, fast speed = 150 pages/min) and each speed condition contained three videos. Therefore, every slow-speed video had 25 pages around 1,500 characters, every medium-speed video had 50 pages around 3,000 characters, and every fast-speed video had 75 pages around 4,500 characters. In the MRI scanner, each video was presented on a screen (visual angle, 11.18° × 10.20°) mounted on the head of the scanner that bore a uniform black background. The participants viewed each video through a mirror on a head coil positioned over their eyes.

Experimental Paradigm

A block design fMRI experiment utilized 19 blocks per fMRI session, alternating between 9 task blocks and 10 rest blocks (Fig. 1). Each block consisted of a 30-s video. Participants engaged in 3 task blocks for each speed condition (slow, medium, and fast) per session, presented in a pseudo-random order and the same order as reading the text. During rest blocks, participants focused on a fixation cross displayed on the screen. Three sessions, each featuring a whole text including 9 task blocks (9 videos), were recorded for each participant. Throughout the session, participants attended to sentences presented as video during task blocks and fixed their gaze at the fixation cross during rest blocks. After the fMRI scanning, all participants were asked to answer five questions related to the content of the text. They were well-proportionately hidden within the slow, medium, and fast speed blocks, and participants were required to select one option of three by pressing a corresponding button. Owing to the adjustment of the position where the content related to the question appeared, the sequence of five questions was pseudo-randomly arranged for slow, medium, and fast reading speeds. Stimulus presentation and the collection of behavioral responses were controlled using E-Prime software (version 3.0; Psychological Software Tools Inc., Pittsburgh, USA).

Fig. 1

The setup for the fMRI experiments. Three fMRI sessions, each lasting 570 s, were conducted. Each speed condition comprised 9 blocks lasting 30 s, mirroring the duration of the rest blocks. Each session consisted of a total of 9 task blocks and 10 rest blocks. Following each scanning, five pseudo-random questions were presented to assess speed-reading performance.

fMRI Data Acquisition

All images were acquired using an MRI machine with a 3-Tesla magnet equipped with a 32-channel phased-array head coil (Verio, Siemens, Munich, Germany) located at the Kokoro Research Center, Kyoto University, Japan. Functional images were obtained using a T2*-weighted gradient echoplanar imaging sequence with the following parameters: echo time (TE)/repetition time (TR), 30 ms/2,500 ms; flip angle, 90°; field of view (FOV), 192 mm × 192 mm; matrix, 64 × 64; 38 interleaved axial slices of 3-mm thickness without gaps; resolution, 3 mm × 3 mm × 3 mm voxels. Structural scans were also acquired using T1-weighted 3-dimensional magnetization-prepared rapid gradient echo sequences (TE, 3.51 ms; TR, 2,000 ms; inversion time, 990 ms; FOV, 256 mm × 256 mm; matrix, 256 × 256; resolution, 1.0 mm × 1.0 mm × 1.0 mm; altogether, 208 total axial sections without gaps).

Preprocessing of fMRI Data

The acquired imaging data were preprocessed and analyzed using Statistical Parametric Mapping (SPM12) (Wellcome Department of Imaging Neuroscience). Each participant’s Echo Planar Imaging (EPI) images were corrected for geometric distortions caused by susceptibility-induced field inhomogeneity. This was performed using a combined correction for static distortions and changes in distortions caused by head motion. Static distortions were calculated using the FieldMap toolbox to process each participant’s B0 field map [21]. All functional brain volumes were realigned to the first volume and spatially normalized to a standard stereotactic space using the template in the Montreal Neurological Institute (MNI) space. These images were resampled into 2 mm × 2 mm × 2 mm voxels during the normalization. All EPI images were smoothed using an 8-mm Gaussian kernel. The data were high pass-filtered with a time constant of 128 s [22].

Whole-brain Activation Analyses

For the training and comparison groups, every session of fMRI data was modeled on a voxel-by-voxel basis, and three variables in the task condition (slow, medium, and fast speeds) were used as regressors, based on the general linear model (GLM) [23]. Each stimulus onset was modeled as a block design in a condition-specific “speed-reading task” lasting 30 s per block. The resulting stimulus functions were convolved with a canonical hemodynamic response function, which provided the regressors for GLM. Finally, group-level analysis was conducted using a mixed-effects approach, and the independent sample t-test was performed across participants to investigate the brain regions involved in the variability of responses in the slow, medium, and fast reading conditions. Additionally, to assess the specificity of results for those with speed-reading training, we investigated the training group > comparison group contrast across three-speed conditions. For illustrative purposes only (i.e., not for statistical inference), all fMRI activations in our work were presented using a voxel-level threshold of P <0.05 with false discovery rate (FDR) correction and an extent threshold of >20 voxels [24,25,26]. The results were inclusively masked with the search mask as described in this article.

Parametric Modulation Analysis

To assess the activation related to each condition in comparison to the baseline for individual participants, we performed a parametric modulation analysis [27] with 1st-order polynomial expansion to investigate the modulation effect of reading speed in each participant. In our work, the events can be modulated by three parameters (slow, medium, and fast speed) using SPM12. In the parametric modulation analysis, for each participant, we first combined the onsets of three-speed conditions into one and set the parametric modulators (condition onsets: [slow medium fast]T, parameter matrix values: [−1 0 1]T). After model estimating, we could obtain the designed matrix that had two rows for each session. The first row of the designed matrix is the common reading condition and the second row is the modulated matrix by parameters, i.e. the contrast [0 1] of designed matrix represents the speed positive (speed increasing from slow to medium and to fast, and modulated matrix [28] is [0 1]·[−1 0 1;−1 0 1]T = [−1 0 1]T), while the contrast [0 −1] represents the speed negative (speed decreasing from fast to medium and to slow, and condition matrix is [0 −1]·[−1 0 1;−1 0 1]T = [1 0 −1]T). The time series from each voxel was high-pass filtered (1/128-Hz cut-off) to remove low-frequency noise and signal drift. The motion parameters were included as predictors of no interest in the regression model.

Among these contrasts, we focused on the speed positive and speed negative contrasts. The contrast images from the individual first-level analyses were entered into a second-level random-effects analysis. Different contrast maps were created, and one-sample t-tests were used to compare each speed-reading condition with the baseline to assess the overall activity within the whole sample. Statistical analyses of the functional data were performed using a mixed-effects model. Furthermore, we investigated the group difference between speed-negative and speed-positive contrasts between the training group and the comparison group.

Psychophysiological Interaction Analysis

Psychophysiological interaction (PPI) is a hypothetical method [29] that calculates the degree of effective functional connectivity between one or more predefined brain regions of interest and other brain regions. To examine the functional connectivity among different cortical areas during speed-reading and its modulation depending on the difference in reading speed, we performed PPI analysis using the obtained fMRI data. PPI was recorded across the entire brain, in which regions of the voxel boosted the signal changes associated with seed regions of interest (ROIs) during task performance as well as the degree of change in speed regulation for speed-reading tasks. We used PPI analysis to identify the brain regions that were significantly associated with changes in reading speed in the ROI masks.

Considering the established role in reading, we mainly concentrated on the functional connectivity on the left occipitotemporal network. The time series volume of interest (VOI) was selected based on an effect across groups (the training group > comparison group) for the speed negative contrast. For the PPI analysis, we selected the vOT ([−38, −40, −20], radium 6 mm) as the seed region and extracted the VOI. Next, for each participant, we investigated the speed-contrast effects on PPI. We employed the t-test to analyze the functional connectivity within the training group and the comparison group separately. Finally, we examined the differences between the two groups.

Dynamic Causal Modeling Analysis

Building upon the group differences identified in the PPI analysis, we aimed to ascertain the effective connection using dynamic causal modeling (DCM) analysis. Given that the PPI analysis only unveiled changes in connectivity between the vOT and other related regions in the occipitotemporal pathway, we focused on the experimental stimuli’ effects on the interactions among these regions. The DCM [30] provides a framework that describes the information flow between neurons in various predefined ROIs and accounts for changes in bold responses during task performance. In a DCM, there exist three distinct types of parameters: input sensory parameters, that is, the extent to which brain regions react to experimental stimuli; fixed parameters, which characterize effective connectivity among regions; and modulatory parameters, which delineate alterations in effective connectivity induced by experimental conditions.

From the results of the PPI analysis described above, we selected four ROIs in the left hemisphere with the radius of 6 mm, that is, the inferior occipital gyrus (iO; [−36, −74, −12]), the ventral occipitotemporal cortex (vOT; [−38, −40, −20]), the posterior superior temporal sulcus (pSTS; [−64, −18, 2]), and the anterior superior temporal sulcus (aSTS; [−56, 0, −14]). The iO is a brain region located in the occipital lobe that is primarily responsible for processing visual information and is a part of the visual sensory input. Thus, we set the iO as the sensory input region. The vOT is involved in word recognition. Both the pSTS and aSTS are important for motion perception and information recognition. We speculated that the information flow involving these brain regions might be modulated by the speed of reading.

The first principal component of the time series for each VOI was used for the analyses. We tested possible models to describe the connectivity of regions using fixed-effect Bayesian model selection to select the optimal model. We estimated all DCMs using SPM12, which allowed us to obtain the posterior distribution of the model parameters and the probability of each model. With the initial assumption that each speed condition would independently affect one of the ten connections or simultaneously impact two of them, resulting in a total of 10 + \(}_^\) = 55 models for each speed condition. In total, we constructed 55 DCMs × 3 conditions × 2 groups = 330 DCMs overall. Then, using the slow condition as a baseline, we compared the expected posterior probability (Ep) values of fast and slow speeds, as well as medium and slow speeds. In addition, a group comparison was done for each of the three task speeds.