Localized Analysis of Normalized Distance from Scalp to Cortex and Personalized Evaluation (LANDSCAPE): Focusing on Age- and Dementia-Specific Changes

Abstract

Background:

Scalp to cortex distance (SCD), as a key technological parameter, has been highlighted in the guidelines of non-invasive brain stimulation. However, in the context of age-related brain changes, the region-specific SCD and its impact on stimulation-induced electric field remain unclear.

Objective:

This study aimed to investigate the region-specific SCD and its relationship with morphometric features and cognitive function in age- and disease-specific populations.

Methods:

We analyzed the SCD and cortical thickness (CT) of left primary motor cortex (M1) and dorsolateral prefrontal cortex (DLPFC) in 214 cognitively normal adults and 43 dementia patients. CT-adjusted SCD was used to control the influence of CT on SCD. Head model was developed to simulate the impact of SCD on the electric field induced by transcranial electrical stimulation.

Results:

We found age-related increased SCD in the left DLPFC (p < 0.001), but not M1 (p = 0.134), and dementia-related increased SCD in both left DLPFC (p < 0.001) and M1 (p < 0.001). CT-adjusted SCD showed greater region-specific impact on left DLPFC rather than M1. The electric field induced by stimulation was consequently decreased with the increased SCD across normal aging and dementia groups.

Conclusions:

Age and dementia have differential impacts on the SCDs of left DLPFC and M1. The findings suggest that it is important to be aware of region-specific distance measures when conducting neuromodulation in individuals with old age and dementia.

Keywords

Cortical thickness dementia dorsolateral prefrontal cortex electric field neuromodulation primary motor cortex scalp to cortex distance

INTRODUCTION

Non-invasive brain stimulation (NIBS), including transcranial electrical stimulation and transcranial magnetic stimulation (TMS), encompasses a broad array of diagnoses and treatments that target a variety of brain regions to achieve desired outcomes in psychiatric disorders [1, 2]. However, given the documented data on overall treatment effect, considerable discrepancies in the efficacy of NIBS have been found across different populations. For instance, the efficacy of repetitive TMS (rTMS) in young adults with major depressive disorder is better than the cases with old age. Although age has not been proven to constitute a significant predictor of treatment responses to rTMS [3], there is a growing consensus that the discrepancies in response rates may be partially attributed to the morphometric variances within the predefined target [4]. Rather, the treatment responses to NIBS are influenced by several key factors, such as targeting approach, age and cortical morphometry [5].

With the advances in neuronavigation, the localization of therapeutic targets has been highly improved; meanwhile, response-related pretreatment parameters, such as motor threshold (MT), are determined by single pulse TMS. However, it should be noted that TMS and transcranial electrical stimulation use coils or electrodes placed on the scalp to deliver a magnetic or electrical current through the scalp to the cortex where the power levels are supposed to be attenuated with the distance [6 –8]. Thus, scalp to cortex distance (SCD), as a key parameter, has been highlighted in the newly updated NIBS guidelines [9]. Notably, recent simulation studies have shown that SCD can critically influence the focality and strength of electric fields induced by NIBS [8, 10].

Overall, the SCDs of left dorsolateral prefrontal cortex (DLPFC) and primary motor cortex (M1) are relatively higher than those of the other regions across the cortex [11], which highlight the emerging needs to in-depth identify the region-dependent SCDs and combine the measures to improve the NIBS practice. Prior evidence showed that the MT measured at the scalp with single pulse TMS is highly dependent on the SCD of M1 [13 –15]. Yet, the absence of the link between the SCDs of M1 and DLPFC raises the concerns about the dose (or pulse power) of stimulating left DLPFC using the MT determined by stimulating M1 in clinical populations [16], not even mentioned the seniors and dementia patients with cortical atrophy [12].

Collectively, how SCD differs between left M1 and DLPFC and to what extent this distance influences the electric fields in healthy adults and dementia patients remain less studied. In this study, we would answer the questions with three experiments: 1) in the context of morphometric mapping, we would investigate the age- and dementia-related effects on the SCDs of left M1 and DLPFC in cognitively normal adults (CN) and Alzheimer’s disease (AD) patients; 2) we would validate the SCDs with Euclidean Distance; and 3) we would construct head model to examine the impact of SCD on the electric field.

METHODS

Participants

Two hundred and sixty-eight right-handed participants across the adulthood (aged from 25–85 years) were drawn from the Open Access Series of Imaging Studies (OASIS) (http://www.oasis-brains.org) [17]. Six cases were excluded due to the failures in the processing, including skull stripping (n = 2), segmentation (n = 1), and cortical reconstruction (n = 3). The remaining 262 cases were classified with regard to the chronological age and cognitive status, including young (aged from 25–35 years), middle age (aged from 36–60 years), young-old (aged from 61–75 years), old-old (aged over 75 years), and AD patients. Age, gender, and available score of cognitive function for individuals in each of these groups were obtained directly from the OASIS dataset.

Ethics statement

For the purposes of this study, we used the structural images of the OASIS that was previously collected under several study protocols at Washington University. This study was carried out in accordance with the recommendations of the University’s Institutional Review Board (IRB). The protocol was approved by the University’s IRB. All subjects gave written informed consent in accordance with the Declaration of Helsinki. All subjects were given written informed consent at the time of study participation. The University’s IRB also provided explicit approval for open sharing of the anonymized data.

MRI data acquisition

As described by Marcus et al. [17], structural magnetic resonance imaging (MRI) images of OASIS database were acquired on a 1.5T Vision scanner (Siemens, Erlangen, Germany) within a single session during which cushioning and thermoplastic face mask were employed to minimize head movements. T1-weighted magnetization prepared rapid gradient echo (MPRAGE) sequence was empirically optimized for the gray-white contrast, with repetition time (TR) = 9.7 ms, echo time (TE) = 4.0 ms, inversion time = 20 ms, delay time = 200 ms, flip angle = 10°, orientation = sagittal, resolution = 256×256 matrix, slices = 128 and thickness = 1.25 mm.

Study 1: Surface-based morphometry

Cortical morphometry, including cortical thickness (CT), was analyzed by BrainSuite 14.0 (http://brainsuite.org/) [18]. BrainSuite is an automatic cortical surface identification integrated toolbox with the updated version of Brain Surface Extraction (BSE) [19, 20], which is commonly used in aging studies [21, 22]. As shown in Fig. 1a, we mapped the individual CT on the basis of Automated Anatomical Labeling (AAL) template by applying the following procedures: Firstly, we corrected the motion and removed the non-brain voxels. Secondly, we segmented brain into gray matter, white matter, and cerebrospinal fluid. Thirdly, we coregistered individual MRI data to AAL template using a similarity transformation. Next, region-specific CT was calculated as the smallest distance (in millimeters) of each point on the external surface of gray matter from the outermost surface of the white matter. At each step, we visually checked the outputs and manually corrected when there are segmentation errors (i.e., non-brain tissue).

Fig.1

Analytic approach of LANDSCAPE study. In step 1, based on high-resolution structural T1 data, surface-based morphometry analysis was performed to measure cortical thickness. In step 2, T1 data was imported to Brainsight neuronavigation system for constructing cerebral cortex, locating the targets and measuring the scalp-to-cortex distance (SCD). In step 3, the SCD calculated in step 2 is validated by three-dimensional Euclidean distance. Finally, the simulation of SCD-dependent electric field is conducted across age- and dementia-specific groups.

Study 2: A reconstructive approach to measure SCD

Image-guided system

Brainsight neuronavigation system (Rogue Resolutions Ltd, UK) was employed to integrate structural MRI images to perform automatically three-dimensional (3D) reconstruction of head and cerebral cortex. For each subject, we firstly performed an MRI-to-head co-registration, and then identified and adjusted the coregisted brain by the AC-PC line into Montreal Neurological Institute (MNI) space. After the normalization, the location of predefined target was labelled with the coordinates in MNI space (x, y, z) (Fig. 1b, c).

Localizing left M1 and DLPFC

Based on the cortical surface reconstruction, we identified the locations of left DLPFC and M1 individually. In NIBS practice, the hand representation within left M1 was determined according to the anatomical criteria with the MNI coordinates as [x = –42, y = –16, z = 68] [23], representing as “hook sign” on sagittal plane. The location of left M1 was verified within the gray matter on the top of paracentral gyrus (i.e., Brodmann area 4, BA4). With regard to the conservative cortical landmarks approximating the cytoarchitectonic definitions of the prefrontal junction (BA9/46) [24], we targeted the location of left DLPFC with the MNI coordinates as [x = –46, y = 45, z = 38] [25, 26], being carefully to locate this region within the gray matter on the top of middle frontal gyrus (MFG).

Measurement of SCD

To better mimic the realistic brain stimulation, the corresponding locations on the scalp are conducted in Brainsight neuronavigation system with pointing back the cursor to the scalp and adjusted with the orientation of the coil from the midline at 45 degrees. Each target location and the angle with coil were checked visually and individually. The accuracy of the two targets was further verified in the planes of axial, coronal and sagittal. The SCD was directly measured in the Brainsight system as the distance from scalp to cortex (Fig. 1b).

Study 3: Validating SCD by Euclidean distance

Euclidean distance (D_i), as a geometric measure, is addressed to measure the distance between two points locating on the scalp (x_s, y_s, z_s) and on the cortex (x_c, y_c, z_c) in 3D MNI space with the following formula: $D_{i} = \sqrt{{(x_{s} - x_{c})}^{2} + {(y_{s} - y_{c})}^{2} + {(z_{s} - z_{c})}^{2}}$ (Fig. 1c).

Study 4: Simulating the effect of SCD on electric field

To further examine the impact of SCD on electric field (E-field), a head model was created based on structural MRI images (1 mm³ isotropic resolution) by SPHERES. The SPHERES 1.0 is a stand-alone graphical user interface application that allows the considerations of arbitrary montages and adjustment of brain parameters on a concentric sphere model by leveraging an analytical solution (Available at http://www.parralab.org/spheres/) [27 –29]. The surface meshes were obtained from the T1 data adopted from BrainSuite. Next, we used SPHERES to simulate the effect of SCD on electric field in a scenario of anodal tDCS over left DLPFC.

Statistical analysis

Group differences of demographics and cognitive measures were tested either with chi-square test for categorical variable or with ANOVA for continuous variables. Cortical thickness was calculated individually. The comparisons of cortical thickness between groups were conducted using the code (http://neuroimage.usc.edu/neuro/Resources/BST_SVReg_Utilities) embedded in MATLAB. Multiple comparison correction was used by the above code using false discovery rate (FDR) estimation [30]. Pearson correlation coefficient was used to detect the relationship between age, cognitive score, cortical thickness and distance measures. Bonferroni correction was addressed to reduce the chances of obtaining false-positive results of correlation analysis [31, 32]. The χ² test, one-way ANOVA and Pearson correlation analysis were performed by IBM SPSS Statistics (Version 20).

RESULTS

Study 1: Morphometric features of left M1 and DLPFC

Overall, 262 participants contained 214 CN adults and 43 AD patients. Following the classification of World Health Organization (WHO) [33], 214 CN adults were divided into four age-specific groups, including young (aged from 25–35 years), middle age (aged from 36–59 years), young-old (aged from 60–75 years), and old-old (aged over 75 years). As (Table 1) shows, gender ratio (F = 2.35, p = 0.078) across five groups was similar. The scores of Mini-Mental State Examination (MMSE) between middle age, young-old, and old-old groups were comparable (F = 1.45, p = 0.15). Compared to non-demented cases, AD patients showed declined global cognition (MMSE: CN: 29.47±0.75, AD: 20.21±3.61, t = 14.69, p < 0.001).

Table 1

Demographics, cognitive function and morphometric features across groups

	Young (n = 40)	Middle age (n = 62)	Young-old (n = 62)	Old-old (n = 55)	AD (n = 43)	F (χ²)	p
Age (y)	29.43±3.97	50.16±5.35	72.06±3.91	86.24±3.87	75.95±6.82	1030.49	<0.001
Gender (M:F)	24:16	42:20	42:20	38:17	25:18	2.35	0.078
MMSE	–	29.47±0.75	29.2±0.8	28.6±5.0	20.21±3.61	–	–
TIV (×10³ mm³)	1670.1±302.7	1665.9±105.1	1667.3±95.2	1662.4±99.7	1358.8±200.1	123.95	<0.001
CT of M1 (mm)	4.02±0.26	3.93±0.23	3.72±0.33	3.58±0.31	3.33±0.49	31.78	<0.001
CT of DLPFC (mm)	4.03±0.21	3.99±0.15	4.15±0.24	4.15±0.42	3.74±0.59	10.87	<0.001

Data are raw scores and presented as mean±SD. AD, Alzheimer’s disease; MMSE, Mini-Mental State Examination; TIV, total intracranial volume; CT, cortical thickness.

Within non-demented cases, group-wise differences were found in the CT of left M1 (F = 24.18, p < 0.001) and DLPFC (F = 5.32, p = 0.001), of which the old-old group showed thinner CT of left M1 than young, middle age and young-old groups. Between CN and AD patients, AD group showed significant reduced CT in left M1 (F = 31.78, p < 0.001) and DLPFC (F = 10.87, p < 0.001) than CN group.

Using gender and global CT as covariates, age was inversely related with the CT of left M1 (r = –0.539, p < 0.001) and positively correlated with the CT of left DLPFC (r = 0.22, p = 0.001).

Study 2: SCD of left M1 and DLPFC

Significant group-wise differences of SCDs were found in left DLPFC (F = 13.71, p < 0.001, (Fig. 2a) and M1 (F = 7.98, p < 0.001, (Fig. 2b). Within CN adults, prominent age-related effect was only found on the SCD of left DLPFC (F = 15.41, p < 0.001), not M1 (F = 1.88, p = 0.134). Using gender and global CT as covariates, age was modestly correlated with the SCD of left M1 (r = 0.142, p = 0.037) and strongly correlated with the SCD of left DLPFC (r = 0.415, p < 0.001). The main effect of age was only found on the SCD of left DLPFC (F = 16.17, p < 0.001) (Fig. 2c, d). Generally, the SCD of M1 was longer than the SCD of DLPFC (Table 2), of which the ratio of the two SCDs (i.e., M1/DLPFC) across groups showed a U-shaped curve.

Fig.2

Comparisons of three-dimensional SCD across age- and dementia-specific groups. Marked age-related and AD-related effect was found in the SCD of left dorsolateral prefrontal cortex (DLPFC) (a); only AD-related effect was found in the SCD of left primary motor cortex (M1) (b). The cortical location of left DLPFC in 3D MNI space with individuals (c) and group-wise (d) plots.

Table 2

Scalp to cortex distance of left M1 and DLPFC across groups

Distance Measures	Young (n = 40)	Middle age (n = 62)	Young-old (n = 62)	Old-old (n = 55)	AD (n = 43)	F (χ²)	p
Left M1	14.33±2.49	14.79±2.62	15.35±2.91	15.49±2.73	18.51±4.87	7.98	<0.001
Left DLPFC	12.73±2.32	13.64±2.02	15.04±2.63	15.55±2.17	16.09±2.89	13.71	<0.001
M1/DLPFC	1.14±0.19	1.09±0.19	1.04±0.21	1.01±0.19	1.15±0.24	4.21	0.003

Data are raw scores and presented as mean±SD. AD, Alzheimer’s disease; M1, primary motor area; DLPFC, dorsal lateral prefrontal cortex.

Within CN adults, region-specific SCD was correlated with worse MMSE score (left DLPFC: r = –0.2, p = 0.014; left M1: r = –0.207, p = 0.011). Within AD patients, there was no significant association between MMSE score and distance measures.

Cortical thickness-adjusted SCD

Because the stimulation, as delivered in tDCS and TMS, may stimulate the surface of cortex or a specific layer of cerebral cortex (Fig. 3b), cortical thickness-adjusted SCD was developed to support image-guided transcranial studies by combining an individual CT and SCD (i.e., CT plus SCD). As Table 3 shown, significant differences of CT-adjusted SCD were found in left DLPFC (F = 11.22, p < 0.001, Fig. 3c), rather than left M1 (F = 2.41, p = 0.05). Similarly, the ratio of the two CT-adjusted SCDs (i.e., M1/DLPFC) across groups also showed a U-shaped curve, of which young adults and AD patients had the highest M1/DLPFC ratio, and old-old group has the lowest M1/DLPFC ratio.

Fig.3

An individualized model of SCD with cortical thickness considered. a) Illustration of reconstructive SCD. b) Heterogeneity in five layers of SCD and six layers of cerebral cortex. c) CT-adjusted SCD of left dorsolateral prefrontal cortex (DLPFC), rather than left primary motor cortex (M1) presents better discriminative utility than SCD across age- and dementia-specific groups.

Table 3

CT-adjusted scalp to cortex distance of left M1 and DLPFC across groups

Distance Measures	Young (n = 40)	Middle age (n = 62)	Young-old (n = 62)	Old-old (n = 55)	AD (n = 43)	F (χ²)	p
Left M1	18.35±2.53	18.72±2.65	19.08±2.89	19.07±2.77	20.26±4.37	2.41	0.05
Left DLPFC	16.76±2.34	17.63±2.02	19.19±2.66	19.69±2.22	18.74±3.22	11.22	<0.001
M1/DLPFC	1.11±0.14	1.07±0.15	1.01±0.16	0.98±0.15	1.09±0.18	6.38	<0.001

Data are raw scores and presented as mean±SD. CT, cortical thickness; AD, Alzheimer’s disease; M1, primary motor area; DLPFC, dorsal lateral prefrontal cortex.

Study 3: Validation SCD in geometric distance

The Euclidean distance (Di) of left M1 and DLPFC was calculated based on MNI coordinates on the scalp (x_s, y_s, z_s) and the ones on the cortex (x_c, y_c, z_c) identified the precise location in Brainsight neuronavigation system visually (Fig. 1c). As shown in Table 4, similar patterns and values were found between SCD and Di across the groups. The Di of left M1 and DLPFC was highly correlated with the SCD (left M1: r = 0.897, p < 0.001; left DLPFC: r = 0.755, p < 0.001).

Table 4

Euclidean distance of left M1 and DLPFC across groups

Distance Measures	Young (n = 40)	Middle age (n = 62)	Young-old (n = 62)	Old-old (n = 55)	AD (n = 43)	F (χ²)	p
Left M1	15.44±2.99	15.57±2.76	16.85±3.24	16.97±3.21	18.61±4.54	6.782	<0.001
Left DLPFC	13.11±2.69	14.31±2.43	16.49±2.89	17.46±2.96	16.89±3.21	20.636	<0.001
M1/DLPFC	1.21±0.25	1.11±0.22	1.04±0.22	0.99±0.21	1.12±0.25	6.019	<0.001

Data are raw scores and presented as mean±SD. AD, Alzheimer’s disease; M1, primary motor area; DLPFC, dorsal lateral prefrontal cortex.

Study 4: Simulation of SCD-dependent electric field

In accordance of realistic practice, a head model was created from T1-weighted data (1 mm³ isotropic resolution). Structural images were registered and normalized to MNI space and then surface meshes were reconstructed based on the processed images from BrainSuite. Head model was prepared for anode tDCS E-field simulation by adding a rectangular 5×5 cm² electrode on the scalp centered over left DLPFC (i.e., F3 in 10/20 international EEG system) (Fig. 4a).

Fig.4

Head model of anode tDCS over left dorsolateral prefrontal cortex (DLPFC) with a 5×5 cm² electrode, depicting the surface of the scalp and cortex (a) and the distribution of the electric field’s magnitude in young group (b), middle age group (c), young-old group (d), old-old group (e), and dementia group (f).

Given the significant age- and dementia-related effects on DLPFC, the SCD of left DLPFC were addressed as the parameter of interest in SPHERES. The isotropic conductivity values of the tissues in this model were adopted with default setting, including: 0.3 S/m for the scalp, 0.03 S/m for the skull, 2 S/m for the CSF and 0.03 S/m for the cortical tissue [34 –36]. To quantify the effect of SCD on E-field, the tab of depth toward scalp was filled with the group-specific SCD of left DLPFC. As depicted in Fig. 4, the spatial distributions of the E-field across five groups were prominently decreased in old-old group and AD group.

DISCUSSION

Using a localized and normalized approach, we investigated the age- and dementia-related effects on the SCD of left M1 and DLPFC and its associations with CT and global cognition. As a consequence of aging and AD, individuals with advanced age and dementia demonstrate region-specific increased SCDs of left DLPFC and M1. When taking CT into account, age-related effects on CT-adjusted SCDs were only found in left DLPFC, rather than left M1. Additionally, the SCDs between age-dependent and AD groups follow a U-shaped curve, indicating that the region-specific SCDs might due to the inconsistent changes across different brain regions.

It is not surprising that age- and dementia-related reductions were observed in morphometric measures [37, 38]. Yet, the critical question we pinpointed here is whether the indices with same measurement scale (i.e., mm) reflect the comparable indices in geometric space. The importance of this issue is that we can combine individual cortical features with pre-intervention parameter setting and further guide the treatment and efficacy evaluation. For instance, compared with SCD, CT-adjusted SCD (i.e., SCD plus CT) had equivalent discriminative power for older adults, but showed marked region-specific effect (i.e., left DLPFC). The observations not just support the complex relationship between SCD and CT in late adulthood, but also highlight the rational for correcting the effect of CT on SCD. Cerebral cortex, or neocortex, contains six layers of cells with specific cytoarchitecture and neurotransmitters [24, 39]. Although the measurement of CT reflects the sum of the six layers (Fig. 3b), the computational process is highly depended on the cortical curvature [40]. Particularly, age-related increased gyrification has been found in frontal lobe [41], which might explain the positive association between age and CT of DLPFC. Thus, considering the cortical complexity, measuring SCD only becomes insufficient and CT-adjusted SCD should be recommended as an optimal measure for assisting NIBS, particular in the cases with old age.

Given the validation from Euclidean distance, another highlighted result here is that SCD, as a line with connection points at the scalp and cortex, could be conceptualized as a vector in 3D space. Overall, SCD shows two vector-like features: 1) length: the value of SCD represents the length of the vector; 2) direction: SCD is a line that starts from the point on the scalp (x_s, y_s, z_s) and ends at the point on the cortex (x_c, y_c, z_c). The age- and dementia-related changes in cerebral cortex address the starting and ending points of SCD measurement with a direction-dependent property of SCD.

The vector-like SCD has potential impact on modeling and optimizing the NIBS practice. Recent studies found that the directionality (or orientation) of current injection could critically influence the local field potential of the targeted region during transcranial electrical stimulation [42, 43]. Importantly, the electric field of frontal tDCS are more variable than those of motor cortex [44]. Therefore, region-specific SCD not only reflect the personalized features, but also provide a useful and dynamic parameter to optimize the therapeutic protocol.

Taken together, SCD, as a key technological parameter of NIBS, should be addressed in the context of cortical mapping. Postulating the individualized distance measures (e.g., CT and SCD) in non-clinical and clinical populations might represent the next frontier in decoding the complex interrelations among age, cognitive function, and brain morphometry. Stemming from the results of LANDSCAPE study, we recommend for developing a thoughtful process of disclosure for the personalized NIBS. Beyond SCD-adjusted MT [45, 46], we should optimize this parameter with regard to age- or dementia-specific morphometric feature. A prominent SCD-E-field linkage also illustrates the need for integrating individual neuroimaging data to NIBS practice in the future.

Limitations and future directions

The results of this study should be interpreted with caution due to several limitations: 1) the data of other key variables, including years of education, genetic risks, domain-specific cognitive function and medical history were not provided in this dataset. Therefore, the ‘SCD-behavior’ association was unable to present; 2) the SCD measured in this study was based on a ‘point-to-point’ approach; however, cortical complexity (i.e., gyrification) of cortical surface dynamically changes during aging process as well; and 3) the T1 MRI images were derived from cross-sectional data, which have very limited interpretation on ageing effect.

Given the above limitations, LANDSCAPE 2.0 will be launched to examine the aging-related effect on SCD and cortical thickness in another cohort. Furthermore, region-specific SCD from multiple sites will be addressed in a well-characterized brain template. The critical parameters, including brain morphometry and SCD, will be tested across multiple modules, such as tDCS, tACS and TMS.

Footnotes

ACKNOWLEDGMENTS

The authors would like to thank the Washington University Alzheimer’s Disease Research Center directed by John C. Morris for providing clinical and imaging data and thank Daniel Marcus for his contributions to the OASIS project (). The authors would like to express their gratitude to Professor. Ning Yuping from the Affiliated Brain Hospital of Guangzhou Medical University and Helene Janine HOPMAN from the Neuromodulation Lab, Department of Psychiatry, The Chinese University of Hong Kong. The authors also thank all the reviewers and editors for their valuable comments and suggestions to improve the quality of this paper.

The corresponding author had full access to all data in the study and had final responsibility for the decision to submit for publication.

Authors’ disclosures available online ().

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