Cortical thickness relates to working memory performance after non-invasive brain stimulation

doi:10.21203/rs.3.rs-2235830/v1

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Cortical thickness relates to working memory performance after non-invasive brain stimulation

https://doi.org/10.21203/rs.3.rs-2235830/v1

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Objective

We investigated whether baseline cortical brain thickness of regions of interest (ROIs) was associated with working memory (WM) performance after non-invasive brain stimulation (NIBS) probing the dorsolateral prefrontal cortex (DLPFC).

Method

Structural Magnetic Resonance Imaging data were analyzed from 22 healthy subjects who received transcranial direct current stimulation (tDCS), intermittent theta-burst stimulation (iTBS) and placebo. Bifrontal tDCS was placed over the neuronavigated left and right DLPFC (20 minutes/2mA). iTBS targeted the neuronavigated left DLPFC applying 1620 pulses. A 2-back task was conducted after the end of NIBS sessions. Selected ROIs were the bilateral DLPFC, medial PFC and posterior cingulate cortex (PCC).

Results

Findings revealed an inverse association between cortical thickness of the DLPFC and reaction time for both tDCS (left: p = 0.02; right: p = 0.007) and iTBS (left: p = 0.02) compared to placebo. Moreover, thickness of the right mPFC and the left PCC were inversely and directly associated with reaction time after iTBS, respectively (p = 0.046 and p = 0.03). No association was found for accuracy.

Conclusion

Our findings provide the first evidence that individual cortical thickness of healthy subjects might be associated with WM performance.

non-invasive brain stimulation

cortical thickness

individualization

working memory

voxel-based morphometry

Working memory is a cognitive system that allows the temporary storage and online manipulation of information that enables goal-directed behavior (Baddeley and Baddeley 1986). WM is crucially involved in diverse cognitive functions, such as decision-making, learning, language and reasoning, and plays an important role in our daily activities. WM declines with aging and deficits can be observed in several neuropsychiatric disorders, including schizophrenia, depression and obsessive-compulsive disorder (Lett et al. 2014; Nikolin et al, 2021, van der Wee et al. 2003). The neurobiological underpinnings of WM have been extensively investigated in healthy and patient populations in recent years (Monks et al. 2004; Li et al. 2022). Neuroimaging investigations have consistently shown that non-emotional WM tasks (n-back) are associated with the activation of the dorsolateral portion of the prefrontal cortex (DLPFC), medial prefrontal cortex (mPFC) and the posterior cingulate cortex (PCC) - regions involved in the frontoparietal and default mode networks, which are to a series of cognitive process (Hampson et al. 2010; Hampson et al. 2006).

Since the DLPFC is easily reached compared to deeper cortical regions, a large number of studies using non-invasive brain stimulation (NIBS) interventions, in particular transcranial direct current stimulation (tDCS) and repetitive transcranial magnetic stimulation (rTMS), have probed this area to modulate working memory in both healthy and neuropsychiatric samples (Clarke et al. 2020; Brunoni et al. 2019). tDCS injects a weak electric current into the brain via electrodes placed over the scalp. The technique does not generate action potentials alone, but can modulate brain activity towards an increase or decrease in endogenous neuronal firing (Nitsche and Paulus 2000). In turn, rTMS can induce neuronal depolarization via focal electric currents applied into the brain, being able to increase or decrease neural activity (Hallett 2007). Initial studies have shown a potential increase in working memory performance after a course of rTMS and tDCS (Brunoni and Vanderhasselt 2014). Nonetheless, more recent meta-analyses presented a high variability of NIBS effects when investigating working memory performance in comparison to placebo (de Boer et al. 2021;Wischnewski et al. 2021).

Heterogeneous effects of NIBS over the DLPFC on working memory performance might be explained by variability of NIBS parameters and by inter-individual differences, such as anatomical variability (Polanía et al. 2018). A recent meta-analysis investigated how different tDCS parameters affect WM performance and suggested personalized doses to increase electric field strengths in brain regions of interest (Wischnewski et al. 2021). Moreover, anatomical brain biomarkers, including cortical volume and cortical thickness were respectively found to be promising for NIBS response in depression (Bulubas et al. 2019; Herbsman and Nahas 2011) and for tDCS in decision-making processes (Filmer et al. 2019). Some studies with rTMS also showed that longitudinal cortical thickness changes are associated with rTMS responders in depression (Boes et al. 2018). So far, however, there are no studies assessing the role of baseline individual brain anatomy of healthy volunteers in predicting the working memory performance following different NIBS interventions, even though previous works suggest an association between working memory performance and cortical thickness (Krogsrud et al. 2021;Ehrlich et al. 2012; Metzler-Baddeley et al. 2010).

Therefore, we proposed to investigate the associations between baseline cortical thickness in brain regions of interest (ROIs) and working memory performance of healthy volunteers submitted to tDCS, iTBS and placebo in a within-subjects design. Cortical thickness was the outcome chosen as a previous study showed that it can account for a high variance in tDCS response (Filmer et al. 2019). Based on prior research (Hampson et al. 2010; Hampson et al. 2006), the bilateral DLPFC, mPFC and PCC were selected as ROIs. We hypothesized that the cortical thickness of these ROIs would be directly related to working memory performance following tDCS and iTBS versus placebo (Yuan and Raz, 2014).

This study is based on the data of a previous trial that measured working memory performance of healthy subjects after a course of NIBS protocols. The original study used a factorial, double-blinded, within-subjects design, in which participants were allocated to receive four different NIBS protocols (tDCS, iTBS, combined tDCS + iTBS and placebo) on different days, in randomized order (Razza et al. 2022) (A study’s design figure can be found in the Sup. Material Figure S1). Here, we report results from the tDCS, iTBS, and placebo groups.

Participants

Right-handed subjects, aged 18 to 45 years, without neuropsychiatric disorders and/or clinical diseases were included. Participants were prescreened by e-mail and those who met inclusion criteria underwent on-site screening by a trained psychologist checking for previous or current psychiatric diagnoses based on the DSM-5 (American Psychiatric Association [APA], 2013), the Hamilton Depression Rating Scale (HDRS) (Hamilton 1960), the Beck Depression Inventory (BDI) (Beck et al. 1961), and the Positive and Negative Affect Schedule (PANAS) scales. Exclusion criteria were specific contraindications for NIBS interventions and MRI (e.g., metal implants), habitual smoking (> 10 cigarettes/day) or abuse/dependence on other drugs, pregnancy, and use of psychoactive drugs (including antidepressant drugs, benzodiazepines, and Z-drugs).

Procedure

First, an anatomical T1-weighted acquisition of the brain was performed using a 3T Magnetic Resonance Imaging (MRI) scanner, followed by a real-time MRI-guided neuronavigation system (Brainsight, Rogue Resolutions, Inc) using the T1-weighted to target the left and right DLPFCs (MNI152 stereotaxic coordinates, -38, + 44, +26 and + 38, +44, + 26, respectively) (Fox et al. 2012). The experimental session was composed of prior baseline measures (HDRS and BDI), respectively followed by the NIBS sessions. The working memory task was assessed right after the end of the simulation session.

2-Back task

The 2-back task was programmed in E-prime 2.0 software (Psychology Software, Tools Inc Pittsburgh, Pennsylvania, USA). The visual stimulus consisted of letters (A to Z) that appeared in a pseudo-randomized order on a computer screen of 15 inches. The applied protocol consisted of three blocks of 30 letters. Letters were displayed on the screen for 500ms, with an interstimulus interval of 3000 ms. Each block consisted of 10 ‘target’ letters, representing a total of 30 ‘targets’. Targets were letters identical to the ones presented two steps earlier in the trial sequence. Participants were instructed to press different buttons on the keyboard for target (key ‘2’) and non-target stimuli (key ‘0’). A brief practice containing 20 stimuli was conducted prior to the task. The 2-back was chosen because it was previously associated with working memory improvement after NIBS in healthy participants (Brunoni and Vanderhasselt 2014).

NIBS protocols

Based on previous studies investigating the cognitive performance after tDCS in healthy volunteers (Wischnewski et al. 2021), electrodes were positioned over the left (anode) and right (cathode) DLPFC located via neuronavigation. tDCS was applied with a current of 2mA through saline-soaked sponges of 25cm² and lasted 20 minutes (Neuroconn DC-Stimulator, Ilmenau, Germany). Sham tDCS used the same montage but delivered only an active current of 30 seconds of fade-in and fade-up on the beginning of the tDCS session.

iTBS protocol used a TMS coil applied with an angle of 45 degrees relative to the midline. The protocol consisted of 54 cycles of 10 triplet bursts with a train duration of 2s and an interval of 8s between trains (1620 pulses) at 110% of the resting motor threshold. The protocol lasted 8 minutes and 40 seconds. The coil B65 Active/Placebo Magventure was used for both active and sham protocols. The coil has two identical sides for delivering active or sham stimulation depending on the randomized codes imputed on the device.

MRI-acquisition

All brain structural MRIs were acquired in a 3-Tesla MR system (General Electric PET / MRI equipment). Volumetric images were based on T1-weighted sequences using a 3D fast-field echo pulse sequence with the following parameters: field of view (FOV) of 25.6, time of repetition (TR) of 7.7ms, time of echo (TE) of 3.1ms, and 202 slices.

Neuroimaging processing and cortical thickness quantification

The T1-weighted image of each participant was processed using the CAT12 toolbox (Dahnke et al. 2013) within SPM12 software (Penny et al. 2011) using MATLAB. We performed a voxel-based processing for voxel-based morphometry (VBM), followed by a surface-based processing for surface-based morphometry (SBM) and, finally, a region-based processing for region-based morphometry (RBM) (Destrieux et al. 2010). The VBM analysis incorporates tissue segmentation, spatial registration, adjustments for volume changes due to registration (modulation), as well as a convolution with a Gaussian kernel matrix (spatial smoothing with an 8 mm full-width to half-maximum (FWHM) filter). The latter steps are followed by the SBM, which also incorporates several different steps such as surface creation, surface registration and spatial smoothing (applied using a 15 mm FWHM filter). In the surface creation step, a projection-based thickness method estimates both initial cortical thickness and central surface considering partial volume information, sulcal blurring and asymmetries (Dahnke et al. 2013). The cortical thickness measurement captures the width of the gray matter band as the distance between its inner and outer boundary in thousands of points. Then, topological correction is performed with spherical harmonics, followed by a surface refinement, resulting in the final central, pial and white surface meshes (Yotter et al. 2011). The pial and white matter surfaces are used to refine the initial cortical thickness using the FreeSurfer thickness metric (Yotter et al. 2011). Finally, data was visually inspected.

In the RBM step, the Destrieux atlas (Destrieux et al. 2010) was used to fit individual surfaces using the spherical registration parameters determined during surface-based processing. Cortical thickness was then calculated for each ROI in native space.

According to our a priori hypothesis, the investigated ROIs were the DLPFC, mPFC and PCC of both hemispheres and subregions of the Destrieux atlas were used to parse each of these ROIs. The DLPFC was composed of inferior frontal gyrus, inferior frontal sulcus, middle frontal gyrus, middle frontal sulcus, and superior frontal sulcus. The mPFC consisted of the superior frontal gyrus and the frontal pole. Finally, the PCC was composed of its posterior ventral and posterior dorsal portions (Fig. 1). ROIs thickness was obtained as the average of the sum of all the subregions included in each ROI.

Insert Fig. 1 about here.

Statistical Analysis

Statistical analyses were performed using R Version 4.1.2 (Team and Others 2013). Reaction time (in milliseconds (ms)) and accuracy (binary outcome) of the target stimuli were considered the dependent variable while ROI thickness and protocol were the independent variables. Missed responses were considered errors and reaction times < 200ms and > 2500ms were considered not genuine responses and were excluded (Whelan 2008; Razza et al. 2022). Due to the non-normal distribution of reaction time responses (Sup. Material Figure S2), generalized linear mixed models (GLMM; using the ‘lme4’ package) with an inverse-gaussian error distribution and an inverse link function were used (Lo and Andrews 2015). For accuracy, GLMMs with binomial distributions were employed. All models used the interaction between ROI thickness and protocol and were controlled for the variable session, indicating in which order the different NIBS protocols were performed. The variable subject was included as a random intercept. Analyses were performed using ROIs of both hemispheres, resulting in a total of six models for each outcome.

These analyses were not corrected for multiple comparisons since they were hypothesis-driven. All models were controlled for the effects of age and gender. All results were considered significant at a p threshold of 0.05.

Our prior study included a sample of 24 healthy subjects, of those two subjects were excluded because the T1-acquisition of one subject did not pass the quality check and one subject did not receive sham stimulation. Therefore, 22 subjects were included in this study, presenting a mean age of 28.4 years (standard deviation (SD) = 7.1 years), mean education years of 17 years (SD = 3 years) and 77% were women. All of the 22 participants underwent sessions of tDCS, iTBS and sham in a randomized order, with a total of 66 NIBS sessions being performed. Working memory performance per condition can be found in the Sup. Material Table S1.

Working memory and ROI thickness

Regarding reaction time, results revealed an inverse association between working memory performance following iTBS (versus sham) and cortical thickness in the left DLPFC and right mPFC. In other words, thinner left DLPFC and right mPFC regions were associated with reaction time improvement (Table 1; Fig. 2). In addition, thinner right and left DLPFCs cortical thickness was also associated with faster reaction time after tDCS. Finally, thicker left PCC was associated with reaction time improvement after iTBS. These associations were all significantly different from sham (Table 1; Fig. 2).

Table 1

Effects of brain region of interest cortical thickness on working memory performance.
	iTBS			tDCS
	coef	SE	p-value	coef	SE	p-value
Reaction time
Left DLPFC	0.06	0.03	0.02	0.06	0.03	0.02
Right DLPFC	0.05	0.02	0.08	0.08	0.03	0.007
Left mPFC	0.04	0.03	0.2	0.02	0.03	0.4
Right mPFC	0.06	0.03	0.046	0.002	0.03	0.9
Left PCC	-0.07	0.03	0.03	-0.04	0.03	0.1
Right PCC	0.03	0.03	0.2	0.04	0.03	0.1
Accuracy
Left DLPFC	-0.13	0.1	0.3	-0.2	0.1	0.3
Right DLPFC	-0.02	0.1	0.85	-0.08	0.1	0.6
Left mPFC	0.02	0.2	0.9	0.2	0.2	0.4
Right mPFC	-0.07	0.2	0.7	-0.04	0.2	0.8
Left PCC	-0.01	0.2	0.92	0.04	0.14	0.7
Right PCC	0.08	0.2	0.6	-0.1	0.16	0.5

Coefficients presented in this table are referring to the interaction between the cortical thickness of the ROI and the intervention type in comparison to sham.

Abbreviation: DLPFC: Dorsolateral prefrontal cortex;iTBS: Intermittent Theta-burst Stimulation; mPFC: Medial prefrontal cortex; PCC: Posterior cingulate cortex; SE: Standard Error; tDCS: Transcranial Direct Current Stimulation

Regarding the association between accuracy and ROI thickness, no differences among protocols were found (Table 1). Moreover, demographic variables did not influence the overall results (Sup. Material Tables S2, S3 and S4).

Insert Table 1 about here

Insert Fig. 2 about here

In this study, based on data from our previous trial (Razza et al. 2022), we investigated whether brain cortical thickness of regions involved in working memory processes were associated with working memory performance of healthy subjects submitted to NIBS protocols. To the best of our knowledge, this is the first sham-controlled study that investigated brain cortical thickness and working memory outcomes of tDCS and iTBS. Importantly, all subjects received tDCS, iTBS and sham in random order on different days (with one week in between), so every subject served as its own control. Our findings revealed that cortical thickness of some of the selected ROIs was associated with reaction time performance after NIBS sessions. No association was found for accuracy.

Here, the bilateral DLPFC, mPFC and PCC were selected as our regions of interest because they play important roles in networks associated with working memory performance (Marek and Dosenbach 2018; Raichle 2015). Our results showed that individuals with thinner DLPFC regions showed relatively faster reaction times than individuals with higher cortical thickness after tDCS and iTBS interventions. This same pattern was also seen for the right mPFC when iTBS, but not when tDCS was applied. The PCC, which was the deepest area investigated, presented a positive association between its left portion and reaction time performance after iTBS.

Although our results show the first evidence of the association between anatomical individual brain differences and working memory performance following NIBS intervention, previous studies already suggested that brain anatomy might have an influence on NIBS response. For instance, a recent study suggested that the gray matter volume of the left DLPFC might be associated with tDCS antidepressant effects. Similar results were also found in studies using rTMS (Manes et al. 2001; Jorge et al. 2008). Moreover, a study investigating the association between cortical thickness in the right prefrontal hemisphere and decision making performance of tDCS of healthy subjects (Filmer et al. 2019) cortical thickness of the targeted area accounted for almost 35% of the variance in cognitive performance across subjects. Taken together, our findings reinforce that variation in NIBS efficacy might be explained by anatomical individual differences.

Comparing the protocols, we see that the DLPFC cortical thickness on both sides is associated with tDCS effects, while for iTBS, only the left side shows such relation. Interestingly, this coincides with the targeted stimulation regions. tDCS electrodes were positioned over the left and right DLPFCs, therefore both hemispheres were affected by the electric current. However, iTBS was placed over the left DLPFC, leaving the right DLPFC directly unaffected. We conclude that the cortical morphology in the targeted brain areas play an especially important role in mitigating effectiveness of NIBS on working memory results in healthy subjects’ samples. Therefore, the results presented here reinforce the need of more powerful sample size studies aiming to evaluate the impact of cortical thickness or anatomical brain measures in the variability of NIBS responses over the DLPFC.

Interestingly, the inverse association between cortical thickness and working memory performance was already reported in previous studies using other interventions than NIBS. For instance, a recent trial investigated the changes in cortical thickness of healthy volunteers after two months of working memory training. The authors found that some structural changes were negatively associated with cognitive performance, demonstrating that larger thickness reductions displayed larger training-related behavioral improvements (Metzler-Baddeley et al. 2016). Authors suggested that plastic changes may have occurred in a network brain level of participants that underwent working memory training in comparison to sham. Based on a previous study (Cattaneo et al. 2008) we inferred that our findings might be explained not only, but also by compensatory effects of NIBS in regions with fewer neural populations.

Moreover, we found that iTBS performance, but not tDCS, presented an association with deeper cortical thickness regions, such as the mPFC and PCC. As tDCS delivers a weaker electric current in comparison to iTBS and does not produce action potentials per se, tDCS effects may have not been robust enough to modulate inner cortical structures in comparison to sham, while iTBS stimulation might have been able to reach the mPFC and PCC after only one active session. Although preliminary studies suggest that bilateral tDCS montages over the DLPFC can induce stronger electric fields in the medial part of the PFC (Bai et al. 2014; Indahlastari et al. 2021), this association has only been shown by computational modeling studies. Therefore, future studies are encouraged to investigate the combination of individual electric field strength in the mPFC and the working memory performance following tDCS. Moreover, as iTBS cognitive effects showed a relation with mPFC and PCC thickness, these results also endorse the iTBS mechanisms of action over the left DLPFC being associated with network-level efforts.

Recent studies show null or small effects of NIBS over the DLPFC for working memory performance, and this variability is reflected in a lack of guidelines on how to apply NIBS for cognitive benefits in healthy and clinical samples. According to Polanía et al., 2018 (Polanía et al. 2018), there are many intrinsic factors that can impact the variability of NIBS effects, and individual differences in cortical characteristics are among the most important ones. Therefore, we believe our findings can guide future studies into the individualization and optimization of cognitive effects using NIBS.

Our study has several limitations that should be discussed. First, the sample size is small. Thus, some analyses might have been underpowered and future studies are advisable to use these results as hypothesis-driven. Second, other anatomical measures such as cortical volume could have been used for this study. However, as a previous study suggests that cortical thickness could be associate with tDCS response and other studies with eldarly show that cortical thickness is more sensitive to investigate response confounds (i.e., age and sex) (Schwarz et al. 2016), we considered this outcome more appropriate for the aim of this study. Third, we performed a ROI-based analysis only, therefore other regions associated with working memory performance and NIBS might not have been included in this analysis. Fourth, the working memory paradigm applied in this study (2-back) might have not been challenged enough for the higher education level population included.

In this study we investigated the association between baseline cortical thickness of healthy young adults and working memory performance following both tDCS and iTBS (versus sham) interventions over the DLPFC. Our findings provide initial evidence that individual differences in cortical thickness of the targeted DLPFCs using tDCS and iTBS and deeper regions using iTBS might be associated with reaction time performance. Therefore, the findings of our study can be useful to guide future studies investigating predictors of tDCS probing the DLPFC for working memory performance.

Acknowledgment

This research project was supported by the São Paulo Research Foundation (FAPESP) (Grant: 2018/10861-7). ARB receives grants from the National Council for Scientific and Technological Development (PQ-1B), and FAPESP (Grants: 2018/10861-7, 2019/06009-6). ARB has a small equity of Flow™, whose devices were not used in the present study. The LIM-27 laboratory receives grants from the Associação Beneficente Alzira Denise Hertzog da Silva. MAV receives funding from the Research Foundation Flanders (FWO) and from the Ghent University (Grants: G0F4619N and BOF17/STA/030, respectively). LBR was supported by FAPESP (Grant: 2019/07256-7) and is currently supported by an FWO grant (G0F4619N). MSL and PHRS are supported by FAPESP (Grant numbers: 21/10574-0 and 22/03266-0, respectively). The other authors have no conflicts of interest to disclose.

Author Contributions

Lais B. Razza, Andre R. Brunoni, Carlos A. Buchpiguel, Marie-Anne Vanderhasselt and Geraldo Busatto contributed to the study conception and design. Material preparation, data collection and analysis were performed by Lais B. Razza, Matthias Luethi, Jonathan Repple and Pedro H. da Silva. The first draft of the manuscript was written by Lais B. Razza and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Data availability

The datasets generated and/or analysed during the current study are available in the following link: https://drive.google.com/drive/folders/1-ylg3AIBCf_kd4VCVCUMxvs2H25rV95T?usp=sharing.

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Cortical thickness relates to working memory performance after non-invasive brain stimulation

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