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. Author manuscript; available in PMC: 2022 Dec 1.
Published in final edited form as: J Physiol. 2021 Nov 30;599(24):5451–5463. doi: 10.1113/JP282387

High-definition transcranial direct current stimulation modulates performance and alpha/beta parieto-frontal connectivity serving fluid intelligence

Yasra Arif 1,2, Rachel K Spooner 1,2, Elizabeth Heinrichs-Graham 1, Tony W Wilson 1
PMCID: PMC9250752  NIHMSID: NIHMS1817569  PMID: 34783045

Abstract

Fluid intelligence (Gƒ) includes logical reasoning abilities and is an essential component of normative cognition. Despite the broad consensus that parieto-prefrontal connectivity is critical for Gf (e.g., the parieto-frontal integration theory of intelligence, P-FIT), the dynamics of such functional connectivity during logical reasoning remains poorly understood. Further, given the known importance of these brain regions for Gf, numerous studies have targeted one or both of these areas with noninvasive stimulation with the goal of improving Gf, but to date there remains little consensus on the overall stimulation-related effects. To examine this, we applied high-definition direct current anodal stimulation to the left and right dorsolateral prefrontal cortex (DLPFC) of twenty-four healthy adults for 20 min in three separate sessions (sham, left and right active). Following stimulation, participants completed a logical reasoning task during magnetoencephalography (MEG). Significant neural responses at the sensor-level were imaged using a beamformer, and peak task-induced activity was subjected to dynamic functional connectivity analyses to evaluate the impact of distinct stimulation montages on network activity. We found that participants responded faster following right DLPFC stimulation versus sham. Moreover, our neural findings followed a similar trajectory of effects such that left parieto-frontal connectivity decreased following right and left DLPFC stimulation compared to sham, with connectivity following right stimulation being significantly correlated with the faster reaction times. Importantly, our findings are consistent with P-FIT, as well as the neural efficiency hypothesis (NEH) of intelligence. In sum, this study provides evidence for beneficial effects of right DLPFC stimulation on logical reasoning.

Keywords: magnetoencephalography, logical reasoning, functional connectivity, phase coherence

Graphical Abstract:

Offline prefrontal HD-tDCS modulated behavioral and network-level neuronal activity underlying logical reasoning in healthy adults. The reaction time improved following right DLPFC stimulation, which was further associated with decreased left parieto-frontal connectivity in the alpha/beta range compared to sham.

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Introduction:

Fluid intelligence (Gƒ) includes rational thinking abilities and enables the utilization of adaptive reasoning to form an association between different stimuli and/or abstract patterns (Blair, 2006; Carroll, 1993; Deary et al., 2007; Horn and Cattell, 1966; Marshalek et al., 1983). Previous literature suggests that Gƒ parallels academic and professional success and equates to mental caliber (Deary et al., 2007; Furnham and Monsen, 2009; Kuncel and Hezlett, 2007; Laidra et al., 2007; Ones et al., 2005; Ree and Earles, 1991; Salgado et al., 2003; Schmidt and Hunter, 1998). Further evidence comes from a large volume of studies implicating participants with high Gƒ generally respond faster and more accurately to cognitive tasks (e.g., executive function and working memory) compared to those with average or low Gƒ ability (Ackerman et al., 2005; Grabner et al., 2004; Horn and Cattell, 1966; Kane and Engle, 2002; Vernon, 1983). Assessment tools for Gƒ commonly include matrix reasoning and propositional analogy tests (Ferrer et al., 2009; Wright et al., 2008), which are also the types of tests most often employed in neuroimaging studies of Gƒ (e.g., Bagga et al., 2014; Miasnikova et al., 2019; Taylor et al., 2020).

Gƒ is known to be strongly reliant on an intact prefrontal cortex (Conway et al., 2002; Crone and Ridderinkhof, 2011; Duncan, 2013; Engle et al., 1999; Ferrer et al., 2009; Kane and Engle, 2002) and to undergo marked deterioration following frontal lobe lesions (Duncan et al., 1995; Roca et al., 2010). However, Jung et al. argued against this unitary concept, and instead proposed a network-level model as a cerebral correlate of fluid intelligence, precisely recognized as the parieto-frontal integration theory of intelligence (P-FIT). According to P-FIT, the dynamic interactions among several frontal and parietal brain regions, including but not limited to bilateral dorsolateral prefrontal cortices (DLPFC) and superior and inferior parietal areas, form the neural basis of intelligence (Jung and Haier, 2007). Unfortunately, P-FIT does not expand on the nature of such dynamic interactions between cortical regions. Nonetheless, in support of this theory, many fMRI and PET studies have shown Gƒ-related activations in these regions (Duncan et al., 2000; Ischebeck et al., 2007; Ischebeck et al., 2006) and seminal work from Neubauer and colleagues has shown an inverse correlation between intelligence and cerebral activity on logical reasoning tasks (Neubauer and Fink, 2009; Neubauer et al., 2004). The latter findings support the idea of neural efficiency and thereby another leading theory in this literature, the neural efficiency hypothesis (NEH) of intelligence, which was first put forward by Haier and colleagues (Haier et al., 1988). According to the NEH, people with higher intelligence recruit lesser cortical volume compared to those with average/lower intelligence when performing the same cognitive task, and this translates into better behavioral performance (Dunst et al., 2014; Neubauer and Fink, 2009).

Recently, following multiple reports of potentially beneficial effects of noninvasive transcranial electrical stimulation (tES) on Gƒ, there has been a surge of interest in further exploration of distinct stimulation montages targeting relevant brain regions and their resulting effects on Gƒ. More specifically, Santarnecchi et al. (2013) showed enhanced performance on a logical reasoning task during online transcranial alternating current stimulation (tACS) of the prefrontal cortex (Santarnecchi et al., 2013). Subsequent EEG and fMRI studies corroborated this finding by demonstrating better behavioral outcomes on measures of Gƒ following tACS of left parietal and frontal regions (Neubauer et al., 2017; Pahor and Jaušovec, 2014). Moreover, Brem et al. demonstrated that stimulation improved fluid intelligence performance when combined with cognitive training (Brem et al., 2018). While these studies have provided intriguing evidence delineating the crucial brain regions serving Gƒ and the impact of neuromodulation, much remains to be discovered in regard to how the stimulation affects neural population dynamics, connectivity, and other critical neural parameters.

Besides tACS, another commonly used type of tES is transcranial direct current stimulation (tDCS). Unfortunately, the mechanisms underlying tDCS are not fully understood, however it is thought to modulate the resting membrane potential by altering the local ionic environment (Fertonani and Miniussi, 2017; Filmer et al., 2014; Hunter et al., 2015; Jang et al., 2009; Liebetanz et al., 2002; Nitsche et al., 2003). Conventional tDCS using sponge electrodes has been utilized extensively to alter cognitive performance (Coffman et al., 2014; Fregni et al., 2005; Kuo and Nitsche, 2012), however, owing to a comparatively larger electrode size, there is generally broad electric current dispersion that modulates brain areas outside of the target region (Masina et al., 2021). By comparison, so-called high-definition tDCS (HD-tDCS) uses metal electrodes and when applied using the 4X1 ring montage is thought to provide more focal and longer-lasting effects (Datta et al., 2009; Datta et al., 2008; Edwards et al., 2013; Kuo et al., 2013). Our laboratory has leveraged this attribute of HD-tDCS in several recent studies and demonstrated specific effects on multiple cognitive domains, including visuospatial attention (Arif et al., 2020), verbal working memory (Koshy et al., 2020), and selective attention (Spooner et al., 2020). Herein, we investigate the effects of such stimulation on the dynamic functional connectivity that serves Gƒ. To this end, we applied HD-tDCS to the left and right DLPFC in separate runs, followed by offline magnetoencephalography (MEG) sessions during which participants performed a novel logical reasoning task. We chose the DLPFC as the site of stimulation based on the P-FIT of intelligence, the DLPFC’s broad connectivity with other regions (e.g., parietal cortices), and the large number of studies that have repeatedly emphasized the critical role of the prefrontal cortex in logical reasoning (Cole et al., 2015; Kane and Engle, 2002). In line with the literature, we hypothesized better behavioral performance following active compared to sham stimulation. Further, we hypothesized differential modulation of functional connectivity between stimulation sites and the cortical regions showing task-induced activity by stimulation montages (i.e., left- or right-anodal DLPFC, and sham). More specifically, owing to the general agreement on the involvement of right-lateralized neural activity in attention-demanding higher order cognitive processing (Corbetta et al., 2000; Heilman and Van Den Abell, 1980; Posner, 1980; Spooner et al., 2020), we hypothesized that right DLPFC stimulation would have a greater impact on the neural networks underlying logical reasoning.

Methods

Ethical approval

This experimental work conformed to the standards set by the Declaration of Helsinki, except for registration in a database. The study protocol was approved by the University of Nebraska Medical Center’s Institutional Review Board. A full description of the study was given to all participants, followed by written informed consent, which was obtained following the guidelines of the University of Nebraska Medical Center’s Institutional Review Board, which approved the study protocol.

Participants

Twenty-four healthy adults (9 females, 1 left-handed) with a mean age of 23.1 years (range: 19–31 years) were enrolled. Exclusionary criteria included any medical illness affecting CNS function (e.g., HIV/AIDS), any neurological or psychiatric disorder, history of head trauma, current substance abuse, and the MEG laboratory’s standard exclusion criteria (e.g., ferromagnetic implants).

High-definition Transcranial Electrical Stimulation

An HD-tDCS 4×1 electrode configuration with a central anode surrounded by four cathodes (Soterix Medical, New York) was applied on the left and right DLPFC, using the international 10/20 system (Jasper, 1958; Klem et al., 1999). Based on the Okamoto et al. transformations of the scalp-based international 10/20 system into MNI space, the central anode was placed on F3 and F4, corresponding to the left and right DLPFC, respectively (Okamoto et al., 2004; Okamoto and Dan, 2005). To identify the focality and intensity of our HD-tDCS configurations, current density modeling was conducted with the Soterix HD Explore software and tissue conductivities from the literature (gray matter=0.276, white matter=0.126, CSF=1.65, skull=0.01, skin=0.465, air=1×10−7, gel=0.3, electrodes= 5.8×107; Datta et al., 2009; Huang et al., 2013; Huang et al., 2018). Each participant completed three separate visits, each at the same time of day but separated by at least one week (M=10.8 days, SD=7.2 days; Fig. 1). Stimulation conditions were pseudorandomized to include two anodal (one left DLPFC active and one right DLPFC active) and one sham (right or left DLPFC, counterbalanced across participants) HD-tDCS sessions. Participants, as well as the researchers who analyzed the data, were kept blind as to which visits encompassed active stimulations and sham. During the active visits, participants underwent 20 min of 2.0-mA direct-current stimulation, plus a 30-s ramp-up period. During the period of stimulation, a battery of cognitive tasks from the NIH toolbox was administered, during all three visits, to each participant to keep them mentally engaged (Hodes et al., 2013; McDonald, 2014). For the sham visit, the same protocol was followed, but without a period of actual stimulation outside of the ramping period. This widely used approach was adopted so that the participant would not know if he/she was being stimulated during any given session (i.e., the beginning of the stimulation can sometimes be detected, but the person rapidly habituates). Following active/sham stimulation, participants were prepared for MEG recording. By design, there was an interval of about 55 min from the end of the stimulation to the initiation of this MEG experiment (Fig. 1), which fits well within the time window for offline effects, as shown by Kuo et al. who found that cortical excitability peaks about 30 min after cessation of HD-tDCS proceeded by a slow dissipation over the next 70–90 min (Kuo et al., 2013). Further evidence for such extended tDCS effects comes from another study that reported an increase in cortical excitability lasting for 90 min following 13 min of 1 mA anodal stimulation (Nitsche and Paulus, 2001).

Figure 1. Visit timeline, current density model and experimental paradigm.

Figure 1.

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Participants received 20 min of anodal and sham HD-tDCS over the left and right DLPFC. Stimulation montages were pseudorandomized across three visits, each separated by at least a week. Current distribution modeling using our HD-tDCS montage revealed focal stimulation of the left and right DLPFC (left). Following HD-tDCS participants completed a logical reasoning paradigm during MEG recording (right). The total visit time from the beginning of stimulation to the end of the MEG task was approximately 89 min.

MEG Experimental Paradigm

Participants completed a non-progressive logical reasoning task, adapted from the classic Raven’s Progressive Matrices (Figure 1; Raven, 2003). During the task, participants were seated in a magnetically shielded room with their head positioned within the MEG helmet-shaped sensor array. They were instructed to fixate on a cross-hair presented centrally within a 2 × 2 grid for a jittered period of 2750 ms ± 250 ms. Within this grid, either the bottom left or bottom right box was highlighted. An array of four complex figures was then presented for 4000 ms. Participants were instructed to determine whether the complex figure in the highlighted box accurately completed the 2 × 2 grid given the pattern of shapes/colors in the other three boxes. Participants responded, as fast as they could, by pressing a button with their right index finger if the highlighted figure correctly completed the matrix, or by pressing a button with their right middle finger if the highlighted figure did not correctly complete the matrix. There was a total of 120 trials, equally split and pseudorandomized between correct and incorrect matrix completions. The task lasted approximately 14 mins.

MEG Data Acquisition

All recordings were conducted in a one-layer magnetically shielded room with active shielding engaged for environmental noise compensation. With an acquisition bandwidth of 0.1–330 Hz, neuromagnetic responses were sampled continuously at 1 kHz using an Elekta MEG system (Helsinki, Finland) with 306 sensors, including 204 planar gradiometers and 102 magnetometers. During data acquisition, participants were monitored via real-time audio–visual feeds from inside the shielded room. Each MEG dataset was individually corrected for head motion and subjected to noise reduction using the signal space separation method with a temporal extension (Taulu and Simola, 2006).

Structural MRI Processing and MEG Coregistration

Prior to MEG measurement, four coils were attached to the subject’s head and localized, together with the three fiducial points and scalp surface, with a 3D digitizer (FASTRAK 3SF0002, Polhemus Navigator Sciences, Colchester, VT, USA). Once the subjects were positioned for MEG recording, an electric current with a unique frequency label (e.g., 322 Hz) was fed to each of the coils. This induced a measurable magnetic field and allowed each coil to be localized in reference to the sensors throughout the recording session. As coil locations were also known with respect to head coordinates, all MEG measurements could be transformed into a common coordinate system. With this coordinate system, each participant’s MEG data were coregistered with high-resolution T1-weighted structural brain data prior to source space analysis using BESA MRI (Version 2.0). Structural MRI data were transformed into Talairach space and aligned parallel to the anterior and posterior commissures. Following source analysis, each participant’s MEG functional images were also transformed into standardized space and spatially resampled.

MEG Preprocessing, Time-frequency Transformation, and Sensor-Level Statistics

Eye blinks and cardio-artifacts were removed from the data using signal space projection (SSP), which was accounted for during source reconstruction (Uusitalo and Ilmoniemi, 1997). The continuous magnetic time series was divided into epochs (duration: 6500 ms), beginning −2500 ms prior to the onset of the matrix stimuli and extending 4000 ms afterward. The baseline period was defined as −1800 to −800 ms prior to the onset of the matrix stimuli to minimize any anticipation effects. Epochs containing artifacts were removed based on a fixed threshold method, supplemented with visual inspection. Briefly, the amplitude and gradient distributions across all trials were determined per participant, and those trials containing the highest amplitude and/or gradient values relative to this distribution were rejected based on participant-specific thresholds. This approach was employed to minimize the impact of individual differences in sensor proximity and head size, which strongly affect MEG signal amplitude. Across all conditions and participants, the average amplitude threshold was 1007.99 fT (SD=199.29), and the average gradient threshold was 291.17 fT/epoch (SD=116.48). On average, 84.44 (SD=15.32) trials per participant per stimulation condition were used for further analysis, and the number of trials per participant did not significantly differ by stimulation condition, F(2,46) = 0.843, p = 0.437. The percentage of included trials per condition was as follows: left: 86.30 (71.91%), right: 88.70 (73.92%), and sham: 87.70 (73.08%). Epochs remaining after artifact rejection were transformed into the time-frequency domain using complex demodulation (Kovach and Gander, 2016), and the resulting spectral power estimations per sensor were averaged over trials to generate time-frequency plots of mean spectral density. The time-frequency analysis was performed with a frequency-step of 2 Hz and a time-step of 25 ms between 4 and 100 Hz to examine higher frequency activity (e.g., alpha, gamma), while a complementary time-frequency analysis was performed at 1 Hz/50 ms resolution from 2 to 10 Hz for better identification of lower frequency activity in the theta range. These sensor-level data were normalized using the respective bin’s baseline power, which was calculated as the mean power during the −1800 to −800 ms-time period. The specific time frequency windows used for source reconstruction were determined by statistical analysis of the sensor level spectrograms across all participants and stimulation conditions using the entire array of 204 gradiometers. Briefly, paired-sample t-tests against baseline were initially conducted on each data point, and the output spectrogram of t-values was thresholded at p < 0.05 to define time-frequency bins containing potentially significant oscillatory deviations across all participants and stimulation conditions. Time-frequency bins that survived the threshold were then clustered with temporally and/or spectrally neighboring bins that were also above the threshold (p < 0.05), and a cluster value was derived by summing the t-values of all data points in the cluster. Nonparametric permutation testing (10,000 permutations) was then used to derive a distribution of cluster values, and the significance level of the observed clusters was tested directly using this distribution (Ernst, 2004; Maris and Oostenveld, 2007).

MEG beamformer imaging and statistics

Cortical neural responses were imaged using the dynamic imaging of coherent sources (DICS) beamformer (Groß et al., 2001; Van Veen et al., 1997), which employs spatial filters in the time-frequency domain to calculate source power for the entire brain volume. Such images are typically referred to as pseudo-t maps, with units (pseudo-t) that reflect noise-normalized power differences (i.e., active versus passive) per voxel. MEG pre-processing and imaging used the Brain Electrical Source Analysis (BESA; Version 6.1) software. Normalized source power was computed for the selected time-frequency windows (see Results) over the entire brain volume per participant at 4.0 mm × 4.0 mm × 4.0 mm resolution. The resulting beamformer images were averaged across all participants and HD-tDCS configurations (i.e., left- and right-anodal DLPFC, and sham) to assess the neuroanatomical basis of the significant oscillatory responses identified through the sensor-level analysis of MEG responses observed during the logical reasoning task.

Functional Connectivity Analyses

To probe dynamic functional connectivity between the site of DLPFC stimulation and the cortical regions showing task-related neural activity identified in our main analyses, phase coherence was computed within the same theta, alpha/beta, and gamma time-frequency windows derived from our sensor-level statistical analyses. Specifically, we estimated the phase-locking value, PLV (Lachaux et al., 1999) between the prefrontal sites of electrical stimulation (i.e., left and right DLPFC) and the peak voxels per time-frequency response within the active brain regions. The PLV reflects the intertrial variability of the phase relationship between pairs of brain regions as a function of time. Values close to 1 indicate strong synchronicity (i.e., phase locking) between the two brain regions within the specific time window across trials, whereas values close to 0 indicate substantial phase variation between the two signals and, thus, weak synchronicity (connectivity) between the two regions. To investigate the differential impact of stimulation montage on the connectivity between prefrontal and more posterior regions showing the most robust responses, we extracted the mean PLV per participant and stimulation condition within the time-frequency windows used for beamforming. The computed values were then compared via 1×3 repeated measures ANOVA with tDCS condition (i.e., left and right anodal DLPFC and sham) as a within-subject factors.

Results

All the participants successfully completed the study, but one was excluded due to poor accuracy (i.e., 61.67%). The remaining 23 participants were 19–31 years old, with a mean age of 23.3 years.

Behavioral Effects

In regard to the behavioral effects, a 1×3 repeated measures (i.e., within-subjects) ANOVA with tDCS condition was conducted, the linear contrast for reaction time was significant, F(1,22) = 5.22, p = 0.032, suggesting a significant difference between at least two conditions. Post-hoc analyses showed that participants were significantly faster following HD-tDCS of the right DLPFC compared to the sham condition, t(22) = −2.28, p = 0.032, while reaction time following left DLPFC did not differ from sham or right DLPFC stimulation (Fig. 2). Accuracy on the logical reasoning task was generally high across all three conditions (i.e., left: 89%, right: 89.68%, and sham: 88.76%) and did not show any significant effects of tDCS montage.

Figure 2. Behavioral performance on the logical reasoning task.

Figure 2.

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Stimulation montage (i.e., left and right active stimulation conditions and sham) is denoted at the bottom with the mean reaction time displayed on the y-axis. Each plot includes the individual data points, median (horizontal line), first and third quartile (box), and local minima and maxima (whiskers). Following right DLPFC stimulation, participants responded faster on the task compared to the sham condition. Error bars show the SEM. *p < 0.05.

Sensor-level analysis:

Statistical evaluation of the time-frequency spectrograms showed a large synchronization in the theta range (3–7 Hz) across a group of MEG sensors near parietal and occipital regions 50–400 ms following stimulus presentation. Additionally, there was an alpha/beta response (i.e., a decrease from baseline, or desynchronization) between 250−1000 ms that spanned from 10 to 22 Hz, which partially overlapped in space with the theta response across a number of parietal and occipital sensors. Finally, a strong synchronization was observed in the gamma range (64–90 Hz; 150–750 ms), which was most prominent in MEG sensors near the occipital cortices (all ps < 0.001, corrected; Fig. 3). The neural populations generating each of these three time-frequency responses were then imaged separately in each participant.

Figure 3. Neural responses to the logical reasoning task.

Figure 3.

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(Left): Grand-averaged time–frequency spectrograms of MEG sensors exhibiting one or more significant responses, with gamma activity at the top, alpha/beta middle, and theta at the bottom. In each spectrogram, frequency (Hz) is shown on the y-axis and time (ms) on the x-axis and. All signal power data are expressed as percent difference from baseline, with color legends shown to the right of the spectrograms. Dashed lines indicate the time–frequency windows that were subjected to beamforming. (Right): Grand-averaged beamformer images (pseudo-t) across all participants and HD-tDCS montages for each time–frequency component, with theta at the bottom, alpha/beta in the middle, and gamma at the top. Separate color scale bars are shown for each.

Source-level analysis:

To identify the spatial origin of the sensor-level oscillatory responses, the aforementioned time-frequency windows of interest were imaged using a beamformer, and grand average maps were computed. These indicated that the alpha/beta response originated in the left parietal and bilateral occipital regions, while the increases in theta and gamma power were confined to inferior and posterior occipital cortices, bilaterally (see Fig. 3).

Dynamic Functional Connectivity:

Alterations in functional connectivity as a function of stimulation montage were evaluated using phase coherence between peak prefrontal sites of HD-tDCS (Fig. 1) and peak task-related activity in the cortical regions shown in Fig. 3. Briefly, we averaged the PLV between the DLPFC node (left or right) and each of the more posterior peaks (see Fig. 3) per stimulation condition and participant and then conducted a repeated measures (i.e., within-subjects) ANOVA. Since we did not have any hemispheric hypotheses, we averaged the PLVs across the bilateral peaks for occipital theta, alpha/beta, and gamma prior to conducting the ANOVAs. For theta and gamma, there were no significant effects of stimulation condition. In contrast, for alpha/beta, significant effects of stimulation condition were observed for parieto-frontal connectivity, F(2, 40) = 5.97, p = 0.005, and post hoc analyses revealed that participants exhibited weaker phase coherence between the left DLPFC and parietal cortex following right stimulation relative to sham, t(20) = −3.389, p = 0.003, which correlated significantly but weakly in the expected direction with reaction time following right DLPFC stimulation, r = 0.385, p = 0.035. Likewise, following left stimulation, alpha/beta phase coherence between the left DLPFC and parietal cortex was also weaker relative to sham, t(21) = −2.102, p = 0.048 (Fig. 4). No significant effects of tDCS montage were observed for alpha/beta PLV between DLPFC and occipital cortices.

Figure 4. Differential modulation of parieto-frontal connectivity by stimulation montages.

Figure 4.

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(Left) Phase locking value (PLV) is represented on the y-axes. Each plot includes the individual data points, median (horizontal line), first and third quartile (box), and local minima and maxima (whiskers). Weaker alpha/beta phase coherence was observed between left DLPFC and left parietal cortex following right and left stimulation relative to sham. Glass brains to the right show the pathway corresponding to the data on the left. Error bars reflect the SEM. *p < 0.05, **p < 0.01.

Notably, to ensure our findings were not confounded by any conditional relative power differences at the seeds and targets used in our connectivity analyses (Brookes et al., 2011), a similar repeated measures ANOVA was conducted using the power data, which revealed no significant effects of stimulation condition, either in left parietal, F(2,40) 1.397, p = 0.259 or left DLPFC regions, F(2,40) 1.348, p = 0.271.

Discussion:

The present study aimed to characterize the impact of offline prefrontal HD-tDCS effects on the network-level neuronal activity serving logical reasoning in healthy adults. Better behavioral outcomes (i.e., faster reaction times) on the logical reasoning task were observed following right DLPFC stimulation compared to sham. Further, regarding the neural mechanisms, both active (i.e., left and right) DLPFC stimulation montages were associated with decreased left parieto-frontal connectivity in the alpha/beta range compared to sham. Interestingly, behavioral and neural indices were significantly but weakly correlated following right DLPFC stimulation. We discuss the implications of these novel findings below.

Our behavioral findings supported our hypothesis and fit well with the previous work on Gƒ ability. Specifically, we found that following HD-tDCS of right DLPFC, participants took less time to respond while maintaining equal accuracy compared to sham. Though recruitment of right prefrontal cortex during logical reasoning processing has been reported previously in two neuroimaging studies (Królicki and Wróbel, 2011; Parsons and Osherson, 2001), our study, to our knowledge, is the first to suggest that transcranial direct-current stimulation of this region leads to improved behavioral performance and that this improvement may be subserved by altered functional connectivity in left fronto-parietal networks. Notably, owing to the inherent differences in the task design and the imaging modality utilized in the aforementioned studies (Królicki and Wróbel, 2011; Parsons and Osherson, 2001), the parallels between our findings and those reported in these studies should be interpreted with caution. Further, it is prudent to also point out that behavior following left prefrontal tDCS did not differ from right prefrontal tDCS or sham. Given our neural findings, it could be that left prefrontal tDCS is also associated with a beneficial effect (although smaller) on logical reasoning ability, but we were not able to fully detect it here. While possible, we are hesitant to speculate further given the lack of correlation between behavioral and neural effects following left prefrontal stimulation and encourage future studies to further probe this possibility.

As mentioned earlier, the P-FIT of intelligence essentially encompasses integration and abstraction of incoming sensory information by the parietal cortex, which further interacts with DLPFC to evaluate this information, find a solution, and test the hypothesis (Colom et al., 2009; Nikolaidis et al., 2017). This theory was first described in seminal work by Jung et al. (Jung and Haier, 2007) and has since been supported by a large body of subsequent studies and gained wide acceptance (Basten et al., 2015; Langer et al., 2012; Li and Tian, 2014; Preusse et al., 2011; Song et al., 2008; Vakhtin et al., 2014; Wang et al., 2011). Our neural findings further agree with and expand on this conceptualization of parieto-frontal integration serving Gf and, further, provide evidence for left parieto-frontal connectivity modulation by the anodal stimulation of the DLPFC. Notably, our data showed left lateralized parieto-frontal connectivity serving Gf, which resonates with an MRS imaging study by Nikolaidis et al. where they demonstrated the involvement of left parieto-frontal cortex in governing Gf by quantifying N-acetyl aspartate (NAA) distribution (Nikolaidis et al., 2017). Though we did not find any lateralized effect of stimulation, the modulatory effects of both active stimulation configurations on the left parieto-frontal pathway differed from sham such that the connectivity was reduced following prefrontal stimulation, with the right hemisphere showing a larger effect size relative to the left. This is also in line with a recent study from our lab, which showed that the right active stimulation condition preferentially impacted neural activity in the other hemisphere’s frontal-visual network, serving visual selective attention (Spooner et al., 2020). Of note, the effects of the right prefrontal tDCS could have resulted in increased attention to the stimuli, although we believe this is unlikely given occipital responses did not differ between the stimulation conditions and typically such attention effects are reflected by significantly stronger occipital oscillations (McCusker et al., 2020; McDermott et al., 2017; Wiesman et al., 2017). Interestingly, we also observed significantly diminished connectivity between left parietal and frontal cortices following ipsilateral DLPFC stimulation compared to sham, but to a lesser extent than, and not significantly different from, stimulation over the right DLPFC. This contribution of left DLPFC has also been demonstrated in several past neuroimaging studies that employed syllogistic reasoning tasks and may be crucial for monitoring integrative processing (Goel et al., 2000; Kane and Engle, 2002).

Importantly, we also found that the strength of left parieto-frontal functional connectivity predicted behavioral performance (i.e., reaction time) following right prefrontal stimulation, such that increases in connectivity between left parietal and frontal regions was associated with slower response times. This potentially aligns with the NEH, which, as mentioned above, argues that people with lower Gf show stronger connectivity between relevant neural regions and/or stronger activation in these regions to perform similarly on cognitively demanding tasks compared to those with higher Gf (Dunst et al., 2014). As mentioned above, we did not observe the same neuro-behavioral correlation following left prefrontal or sham stimulation, which may reflect the preferential role of the right prefrontal cortices in logical reasoning tasks like those used here.

To close, the current study combined state-of-the-art MEG and high-definition neuromodulation and provides evidence that dynamic functional connectivity between left parietal and frontal regions is altered following electrical perturbation of the DLPFC, with the right active montage showing beneficial impact to a greater extent on the logical reasoning task. This specific modulation of connectivity was significantly correlated with behavioral performance, such that following right DLPFC HD-tDCS, we observed improvements in terms of faster reaction times. Thus, our findings suggest that an optimal level of connectivity in left-lateralized parieto-frontal regions is pertinent for improving logical reasoning ability and perhaps Gf more broadly, and that this may be achieved by targeted stimulation of the prefrontal cortex, especially the right DLPFC. Future studies should examine whether concurrent bilateral PFC stimulation accentuates or degrades this effect, and whether the alterations are more general by using other Gf tasks. Further, our study also corroborates previous reports documenting effects of HD-tDCS lasting at least 90 mins, although the exact duration of the effects and when they completely diminish are not known and warrant further research.

Key points:

  • Logical reasoning is an indispensable component of fluid intelligence and involves multispectral oscillatory activity in parietal and frontal regions.

  • Parieto-frontal integration is well characterized in logical reasoning; however, its direct neural quantification and neuromodulation by brain stimulation remain poorly understood.

  • High-definition transcranial direct current stimulation of dorsolateral prefrontal cortex (DLPFC) had modulatory effects on task performance and neural interactions serving logical reasoning, with right stimulation showing beneficial effects.

  • Right DLPFC stimulation led to a decrease in the response time (i.e., better task performance) and left parieto-frontal connectivity with a marginal positive association between behavioral and neural metrics.

  • Other modes of targeted stimulation of DLPFC (e.g., frequency-specific) can be employed in future studies.

Acknowledgements:

This work was supported by the National Institutes of Health [grants RF1-MH117032 (TWW), R01-MH116782 (TWW), R01-MH118032 (TWW), and R01-DA047828 (TWW)], and the National Science Foundation [grant #1539067 (TWW)]. The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript.

Footnotes

Declaration / Conflict of interest:

The authors of this manuscript acknowledge no conflicts of interest, financial or otherwise.

Data Availability:

The data that support the findings of this study are available from the corresponding author, Dr. Tony W. Wilson, upon reasonable request.

Data Sharing Statement:

The data that support the findings of this study are available from the corresponding author, Dr Tony W. Wilson, upon reasonable request.

References:

  1. Ackerman PL, Beier ME, Boyle MO, 2005. Working memory and intelligence: The same or different constructs? Psychological bulletin 131, 30. [DOI] [PubMed] [Google Scholar]
  2. Arif Y, Spooner RK, Wiesman AI, Proskovec AL, Rezich MT, Heinrichs-Graham E, Wilson TW, 2020. Prefrontal Multielectrode Transcranial Direct Current Stimulation Modulates Performance and Neural Activity Serving Visuospatial Processing. Cerebral Cortex. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bagga D, Singh N, Singh S, Modi S, Kumar P, Bhattacharya D, Garg ML, Khushu S, 2014. Assessment of abstract reasoning abilities in alcohol-dependent subjects: an fMRI study. Neuroradiology 56, 69–77. [DOI] [PubMed] [Google Scholar]
  4. Basten U, Hilger K, Fiebach CJ, 2015. Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence. Intelligence 51, 10–27. [Google Scholar]
  5. Blair C, 2006. How similar are fluid cognition and general intelligence? A developmental neuroscience perspective on fluid cognition as an aspect of human cognitive ability. Behavioral and Brain Sciences 29, 109–125. [DOI] [PubMed] [Google Scholar]
  6. Brem A-K, Almquist JN-F, Mansfield K, Plessow F, Sella F, Santarnecchi E, Orhan U, McKanna J, Pavel M, Mathan S, 2018. Modulating fluid intelligence performance through combined cognitive training and brain stimulation. Neuropsychologia 118, 107–114. [DOI] [PubMed] [Google Scholar]
  7. Brookes MJ, Hale JR, Zumer JM, Stevenson CM, Francis ST, Barnes GR, Owen JP, Morris PG, Nagarajan SS, 2011. Measuring functional connectivity using MEG: methodology and comparison with fcMRI. Neuroimage 56, 1082–1104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Carroll JB, 1993. Human cognitive abilities: A survey of factor-analytic studies. Cambridge University Press. [Google Scholar]
  9. Coffman BA, Clark VP, Parasuraman R, 2014. Battery powered thought: enhancement of attention, learning, and memory in healthy adults using transcranial direct current stimulation. Neuroimage 85, 895–908. [DOI] [PubMed] [Google Scholar]
  10. Cole MW, Ito T, Braver TS, 2015. Lateral prefrontal cortex contributes to fluid intelligence through multinetwork connectivity. Brain connectivity 5, 497–504. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Colom R, Haier RJ, Head K, Álvarez-Linera J, Quiroga MÁ, Shih PC, Jung RE, 2009. Gray matter correlates of fluid, crystallized, and spatial intelligence: Testing the P-FIT model. Intelligence 37, 124–135. [Google Scholar]
  12. Conway AR, Cowan N, Bunting MF, Therriault DJ, Minkoff SR, 2002. A latent variable analysis of working memory capacity, short-term memory capacity, processing speed, and general fluid intelligence. Intelligence 30, 163–183. [Google Scholar]
  13. Corbetta M, Kincade JM, Ollinger JM, McAvoy MP, Shulman GL, 2000. Voluntary orienting is dissociated from target detection in human posterior parietal cortex. Nature neuroscience 3, 292. [DOI] [PubMed] [Google Scholar]
  14. Crone EA, Ridderinkhof KR, 2011. The developing brain: from theory to neuroimaging and back. Developmental cognitive neuroscience 1, 101–109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Datta A, Bansal V, Diaz J, Patel J, Reato D, Bikson M, 2009. Gyri-precise head model of transcranial direct current stimulation: improved spatial focality using a ring electrode versus conventional rectangular pad. Brain stimulation 2, 201–207. e201. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Datta A, Elwassif M, Battaglia F, Bikson M, 2008. Transcranial current stimulation focality using disc and ring electrode configurations: FEM analysis. Journal of neural engineering 5, 163. [DOI] [PubMed] [Google Scholar]
  17. Deary IJ, Strand S, Smith P, Fernandes C, 2007. Intelligence and educational achievement. Intelligence 35, 13–21. [Google Scholar]
  18. Duncan J, 2013. The structure of cognition: attentional episodes in mind and brain. Neuron 80, 35–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Duncan J, Burgess P, Emslie H, 1995. Fluid intelligence after frontal lobe lesions. Neuropsychologia 33, 261–268. [DOI] [PubMed] [Google Scholar]
  20. Duncan J, Seitz RJ, Kolodny J, Bor D, Herzog H, Ahmed A, Newell FN, Emslie H, 2000. A neural basis for general intelligence. Science 289, 457–460. [DOI] [PubMed] [Google Scholar]
  21. Dunst B, Benedek M, Jauk E, Bergner S, Koschutnig K, Sommer M, Ischebeck A, Spinath B, Arendasy M, Bühner M, 2014. Neural efficiency as a function of task demands. Intelligence 42, 22–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Edwards D, Cortes M, Datta A, Minhas P, Wassermann EM, Bikson M, 2013. Physiological and modeling evidence for focal transcranial electrical brain stimulation in humans: a basis for high-definition tDCS. Neuroimage 74, 266–275. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Engle RW, Kane MJ, Tuholski SW, 1999. Individual differences in working memory capacity and what they tell us about controlled attention, general fluid intelligence, and functions of the prefrontal cortex. Models of working memory: Mechanisms of active maintenance and executive control 4, 102–134. [Google Scholar]
  24. Ernst MD, 2004. Permutation methods: a basis for exact inference. Statistical Science 19, 676–685. [Google Scholar]
  25. Ferrer E, O’Hare ED, Bunge SA, 2009. Fluid reasoning and the developing brain. Frontiers in neuroscience 3, 3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Fertonani A, Miniussi C, 2017. Transcranial electrical stimulation: what we know and do not know about mechanisms. The Neuroscientist 23, 109–123. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Filmer HL, Dux PE, Mattingley JB, 2014. Applications of transcranial direct current stimulation for understanding brain function. Trends in neurosciences 37, 742–753. [DOI] [PubMed] [Google Scholar]
  28. Fregni F, Boggio PS, Nitsche M, Bermpohl F, Antal A, Feredoes E, Marcolin MA, Rigonatti SP, Silva MT, Paulus W, 2005. Anodal transcranial direct current stimulation of prefrontal cortex enhances working memory. Experimental brain research 166, 23–30. [DOI] [PubMed] [Google Scholar]
  29. Furnham A, Monsen J, 2009. Personality traits and intelligence predict academic school grades. Learning and individual differences 19, 28–33. [Google Scholar]
  30. Goel V, Buchel C, Frith C, Dolan RJ, 2000. Dissociation of mechanisms underlying syllogistic reasoning. Neuroimage 12, 504–514. [DOI] [PubMed] [Google Scholar]
  31. Grabner R, Fink A, Stipacek A, Neuper C, Neubauer A, 2004. Intelligence and working memory systems: evidence of neural efficiency in alpha band ERD. Cognitive Brain Research 20, 212–225. [DOI] [PubMed] [Google Scholar]
  32. Groß J, Kujala J, Hämäläinen M, Timmermann L, Schnitzler A, Salmelin R, 2001. Dynamic imaging of coherent sources: studying neural interactions in the human brain. Proceedings of the National Academy of Sciences 98, 694–699. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Haier RJ, Siegel BV Jr, Nuechterlein KH, Hazlett E, Wu JC, Paek J, Browning HL, Buchsbaum MS, 1988. Cortical glucose metabolic rate correlates of abstract reasoning and attention studied with positron emission tomography. Intelligence 12, 199–217. [Google Scholar]
  34. Heilman KM, Van Den Abell T, 1980. Right hemisphere dominance for attention The mechanism underlying hemispheric asymmetries of inattention (neglect). Neurology 30, 327–327. [DOI] [PubMed] [Google Scholar]
  35. Hodes RJ, Insel TR, Landis SC, 2013. The NIH Toolbox Setting a standard for biomedical research. Neurology 80, S1–S1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Horn JL, Cattell RB, 1966. Refinement and test of the theory of fluid and crystallized general intelligences. Journal of educational psychology 57, 253. [DOI] [PubMed] [Google Scholar]
  37. Huang Y, Dmochowski JP, Su Y, Datta A, Rorden C, Parra LC, 2013. Automated MRI segmentation for individualized modeling of current flow in the human head. Journal of neural engineering 10, 066004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Huang Y, Liu AA, Lafon B, Friedman D, Dayan M, Wang X, Bikson M, Doyle WK, Devinsky O, Parra LC, 2018. Correction: Measurements and models of electric fields in the in vivo human brain during transcranial electric stimulation. Elife 7, e35178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Hunter MA, Coffman BA, Gasparovic C, Calhoun VD, Trumbo MC, Clark VP, 2015. Baseline effects of transcranial direct current stimulation on glutamatergic neurotransmission and large-scale network connectivity. Brain research 1594, 92–107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Ischebeck A, Zamarian L, Egger K, Schocke M, Delazer M, 2007. Imaging early practice effects in arithmetic. Neuroimage 36, 993–1003. [DOI] [PubMed] [Google Scholar]
  41. Ischebeck A, Zamarian L, Siedentopf C, Koppelstätter F, Benke T, Felber S, Delazer M, 2006. How specifically do we learn? Imaging the learning of multiplication and subtraction. Neuroimage 30, 1365–1375. [DOI] [PubMed] [Google Scholar]
  42. Jang SH, Ahn SH, Byun WM, Kim CS, Lee MY, Kwon YH, 2009. The effect of transcranial direct current stimulation on the cortical activation by motor task in the human brain: an fMRI study. Neuroscience Letters 460, 117–120. [DOI] [PubMed] [Google Scholar]
  43. Jasper HH, 1958. The ten-twenty electrode system of the International Federation. Electroencephalogr. Clin. Neurophysiol 10, 370–375. [PubMed] [Google Scholar]
  44. Jung RE, Haier RJ, 2007. The Parieto-Frontal Integration Theory (P-FIT) of intelligence: converging neuroimaging evidence. Behavioral and Brain Sciences 30, 135. [DOI] [PubMed] [Google Scholar]
  45. Kane MJ, Engle RW, 2002. The role of prefrontal cortex in working-memory capacity, executive attention, and general fluid intelligence: An individual-differences perspective. Psychonomic bulletin & review 9, 637–671. [DOI] [PubMed] [Google Scholar]
  46. Klem GH, Luders HO, Jasper HH, Elger C, 1999. The ten-twenty electrode system of the International Federation. The International Federation of Clinical Neurophysiology. Electroencephalogr Clin Neurophysiol Suppl 52, 3–6. [PubMed] [Google Scholar]
  47. Koshy SM, Wiesman AI, Spooner RK, Embury C, Rezich MT, Heinrichs-Graham E, Wilson TW, 2020. Multielectrode Transcranial Electrical Stimulation of the Left and Right Prefrontal Cortices Differentially Impacts Verbal Working Memory Neural Circuitry. Cerebral Cortex 30, 2389–2400. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Kovach CK, Gander PE, 2016. The demodulated band transform. Journal of neuroscience methods 261, 135–154. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Królicki L, Wróbel A, 2011. A role for the right prefrontal and bilateral parietal cortex in four-term transitive reasoning: an fMRI study with abstract linear syllogism tasks. Acta Neurobiol Exp 71, 479–495. [DOI] [PubMed] [Google Scholar]
  50. Kuncel NR, Hezlett SA, 2007. Standardized tests predict graduate students’ success. Science. [DOI] [PubMed] [Google Scholar]
  51. Kuo H-I, Bikson M, Datta A, Minhas P, Paulus W, Kuo M-F, Nitsche MA, 2013. Comparing cortical plasticity induced by conventional and high-definition 4× 1 ring tDCS: a neurophysiological study. Brain stimulation 6, 644–648. [DOI] [PubMed] [Google Scholar]
  52. Kuo M-F, Nitsche MA, 2012. Effects of transcranial electrical stimulation on cognition. Clinical EEG and Neuroscience 43, 192–199. [DOI] [PubMed] [Google Scholar]
  53. Lachaux JP, Rodriguez E, Martinerie J, Varela FJ, 1999. Measuring phase synchrony in brain signals. Human brain mapping 8, 194–208. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Laidra K, Pullmann H, Allik J, 2007. Personality and intelligence as predictors of academic achievement: A cross-sectional study from elementary to secondary school. Personality and individual differences 42, 441–451. [Google Scholar]
  55. Langer N, Pedroni A, Gianotti LR, Hänggi J, Knoch D, Jäncke L, 2012. Functional brain network efficiency predicts intelligence. Human brain mapping 33, 1393–1406. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Li C, Tian L, 2014. Association between resting-state coactivation in the parieto-frontal network and intelligence during late childhood and adolescence. American Journal of Neuroradiology 35, 1150–1156. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Liebetanz D, Nitsche MA, Tergau F, Paulus W, 2002. Pharmacological approach to the mechanisms of transcranial DC‐stimulation‐induced after‐effects of human motor cortex excitability. Brain 125, 2238–2247. [DOI] [PubMed] [Google Scholar]
  58. Maris E, Oostenveld R, 2007. Nonparametric statistical testing of EEG-and MEG-data. Journal of neuroscience methods 164, 177–190. [DOI] [PubMed] [Google Scholar]
  59. Marshalek B, Lohman DF, Snow RE, 1983. The complexity continuum in the radex and hierarchical models of intelligence. Intelligence 7, 107–127. [Google Scholar]
  60. Masina F, Arcara G, Galletti E, Cinque I, Gamberini L, Mapelli D, 2021. Neurophysiological and behavioural effects of conventional and high definition tDCS. Scientific reports 11, 1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. McCusker MC, Wiesman AI, Schantell MD, Eastman JA, Wilson TW, 2020. Multi-spectral oscillatory dynamics serving directed and divided attention. Neuroimage 217, 116927. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. McDermott TJ, Wiesman AI, Proskovec AL, Heinrichs-Graham E, Wilson TW, 2017. Spatiotemporal oscillatory dynamics of visual selective attention during a flanker task. Neuroimage 156, 277–285. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. McDonald S, 2014. Special series on the cognition battery of the NIH toolbox. Journal of International Neuropsychological Society 20, 487–651. [Google Scholar]
  64. Miasnikova A, Troshkov D, Baklushev M, Perevoznyuk G, 2019. Predicting States of Abstract Reasoning Using EEG Functional Connectivity Markers. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, pp. 2451–2454. [DOI] [PubMed] [Google Scholar]
  65. Neubauer A, Wammerl M, Benedek M, Jauk E, Jaušovec N, 2017. The influence of transcranial alternating current stimulation (tACS) on fluid intelligence: An fMRI study. Personality and individual differences 118, 50–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Neubauer AC, Fink A, 2009. Intelligence and neural efficiency. Neuroscience & Biobehavioral Reviews 33, 1004–1023. [DOI] [PubMed] [Google Scholar]
  67. Neubauer AC, Grabner RH, Freudenthaler HH, Beckmann JF, Guthke J, 2004. Intelligence and individual differences in becoming neurally efficient. Acta psychologica 116, 55–74. [DOI] [PubMed] [Google Scholar]
  68. Nikolaidis A, Baniqued PL, Kranz MB, Scavuzzo CJ, Barbey AK, Kramer AF, Larsen RJ, 2017. Multivariate associations of fluid intelligence and NAA. Cerebral Cortex 27, 2607–2616. [DOI] [PubMed] [Google Scholar]
  69. Nitsche M, Fricke K, Henschke U, Schlitterlau A, Liebetanz D, Lang N, Henning S, Tergau F, Paulus W, 2003. Pharmacological modulation of cortical excitability shifts induced by transcranial direct current stimulation in humans. The Journal of physiology 553, 293–301. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Nitsche MA, Paulus W, 2001. Sustained excitability elevations induced by transcranial DC motor cortex stimulation in humans. Neurology 57, 1899–1901. [DOI] [PubMed] [Google Scholar]
  71. Okamoto M, Dan H, Sakamoto K, Takeo K, Shimizu K, Kohno S, Oda I, Isobe S, Suzuki T, Kohyama K, 2004. Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10–20 system oriented for transcranial functional brain mapping. Neuroimage 21, 99–111. [DOI] [PubMed] [Google Scholar]
  72. Okamoto M, Dan I, 2005. Automated cortical projection of head-surface locations for transcranial functional brain mapping. Neuroimage 26, 18–28. [DOI] [PubMed] [Google Scholar]
  73. Ones DS, Viswesvaran C, Dilchert S, 2005. Cognitive Ability in Selection Decisions.
  74. Pahor A, Jaušovec N, 2014. The effects of theta transcranial alternating current stimulation (tACS) on fluid intelligence. International journal of psychophysiology 93, 322–331. [DOI] [PubMed] [Google Scholar]
  75. Parsons LM, Osherson D, 2001. New evidence for distinct right and left brain systems for deductive versus probabilistic reasoning. Cerebral Cortex 11, 954–965. [DOI] [PubMed] [Google Scholar]
  76. Posner MI, 1980. Orienting of attention. Quarterly journal of experimental psychology 32, 3–25. [DOI] [PubMed] [Google Scholar]
  77. Preusse F, Van Der Meer E, Deshpande G, Krueger F, Wartenburger I, 2011. Fluid intelligence allows flexible recruitment of the parieto-frontal network in analogical reasoning. Frontiers in human neuroscience 5, 22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Raven J, 2003. Raven progressive matrices. Handbook of nonverbal assessment. Springer, pp. 223–237. [Google Scholar]
  79. Ree MJ, Earles JA, 1991. Predicting training success: Not much more than g. Personnel psychology 44, 321–332. [Google Scholar]
  80. Roca M, Parr A, Thompson R, Woolgar A, Torralva T, Antoun N, Manes F, Duncan J, 2010. Executive function and fluid intelligence after frontal lobe lesions. Brain 133, 234–247. [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Salgado JF, Anderson N, Moscoso S, Bertua C, De Fruyt F, Rolland JP, 2003. A meta-analytic study of general mental ability validity for different occupations in the European community. Journal of Applied Psychology 88, 1068. [DOI] [PubMed] [Google Scholar]
  82. Santarnecchi E, Polizzotto NR, Godone M, Giovannelli F, Feurra M, Matzen L, Rossi A, Rossi S, 2013. Frequency-dependent enhancement of fluid intelligence induced by transcranial oscillatory potentials. Current biology 23, 1449–1453. [DOI] [PubMed] [Google Scholar]
  83. Schmidt FL, Hunter JE, 1998. The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings. Psychological bulletin 124, 262. [Google Scholar]
  84. Song M, Zhou Y, Li J, Liu Y, Tian L, Yu C, Jiang T, 2008. Brain spontaneous functional connectivity and intelligence. Neuroimage 41, 1168–1176. [DOI] [PubMed] [Google Scholar]
  85. Spooner RK, Eastman JA, Rezich MT, Wilson TW, 2020. High‐definition transcranial direct current stimulation dissociates fronto‐visual theta lateralization during visual selective attention. The Journal of physiology 598, 987–998. [DOI] [PMC free article] [PubMed] [Google Scholar]
  86. Taulu S, Simola J, 2006. Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements. Physics in Medicine & Biology 51, 1759. [DOI] [PubMed] [Google Scholar]
  87. Taylor BK, Embury CM, Heinrichs-Graham E, Frenzel MR, Eastman JA, Wiesman AI, Wang Y-P, Calhoun VD, Stephen JM, Wilson TW, 2020. Neural oscillatory dynamics serving abstract reasoning reveal robust sex differences in typically-developing children and adolescents. Developmental cognitive neuroscience 42, 100770. [DOI] [PMC free article] [PubMed] [Google Scholar]
  88. Uusitalo MA, Ilmoniemi RJ, 1997. Signal-space projection method for separating MEG or EEG into components. Medical and Biological Engineering and Computing 35, 135–140. [DOI] [PubMed] [Google Scholar]
  89. Vakhtin AA, Ryman SG, Flores RA, Jung RE, 2014. Functional brain networks contributing to the Parieto-Frontal Integration Theory of Intelligence. Neuroimage 103, 349–354. [DOI] [PubMed] [Google Scholar]
  90. Van Veen BD, Van Drongelen W, Yuchtman M, Suzuki A, 1997. Localization of brain electrical activity via linearly constrained minimum variance spatial filtering. IEEE Transactions on biomedical engineering 44, 867–880. [DOI] [PubMed] [Google Scholar]
  91. Vernon PA, 1983. Speed of information processing and general intelligence. Intelligence 7, 53–70. [Google Scholar]
  92. Wang L, Song M, Jiang T, Zhang Y, Yu C, 2011. Regional homogeneity of the resting-state brain activity correlates with individual intelligence. Neuroscience Letters 488, 275–278. [DOI] [PubMed] [Google Scholar]
  93. Wiesman AI, Heinrichs‐Graham E, Proskovec AL, McDermott TJ, Wilson TW, 2017. Oscillations during observations: Dynamic oscillatory networks serving visuospatial attention. Human brain mapping 38, 5128–5140. [DOI] [PMC free article] [PubMed] [Google Scholar]
  94. Wright SB, Matlen BJ, Baym CL, Ferrer E, Bunge SA, 2008. Neural correlates of fluid reasoning in children and adults. Frontiers in human neuroscience 2, 8. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, Dr. Tony W. Wilson, upon reasonable request.

The data that support the findings of this study are available from the corresponding author, Dr Tony W. Wilson, upon reasonable request.

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