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. 2022 Nov 29;32(4):e1955. doi: 10.1002/mpr.1955

White‐matter correlates of anxiety: The contribution of the corpus‐callosum to the study of anxiety and stress‐related disorders

Rotem Saar‐Ashkenazy 1,2,✉, Jonathan Guez 3,4, Yael Jacob 5, Ronel Veksler 6, Jonathan E Cohen 7, Ilan Shelef 8, Alon Friedman 2,6,9, Mony Benifla 10
PMCID: PMC10698819  PMID: 36448238

Abstract

Objectives

Traumatic stress has been associated with increased risk for brain alterations and development of anxiety disorders. Studies conducted in posttraumatic patients have shown white‐mater volume and diffusion alterations in the corpus‐callosum. Decreased cognitive performance has been demonstrated in acute stress disorder and posttraumatic patients. However, whether cognitive alterations result from stress related neuropathology or reflect a predisposition is not known. In the current study, we examined in healthy controls, whether individual differences in anxiety are associated with those cognitive and brain alterations reported in stress related pathologies.

Methods

Twenty healthy volunteers were evaluated for anxiety using the state‐trait inventory (STAI), and were tested for memory performance. Brain imaging was employed to extract volumetric and diffusion characteristics of the corpus‐callosum.

Results

Significant correlations were found between trait anxiety and all three diffusion parameters (fractional‐anisotropy, mean and radial‐diffusivity). Associative‐memory performance and corpus‐callosum volume were also significantly correlated.

Conclusion

We suggest that cognitive and brain alterations, as tested in the current work and reported in stress related pathologies, are present early and possibly persist throughout life. Our findings support the hypothesis that individual differences in trait anxiety predispose individuals towards negative cognitive outcomes and brain alterations, and potentially to stress related disorders.

Keywords: anxiety/anxiety disorders, corpus‐callosum, PTSD, trait anxiety

1. INTRODUCTION

Anxiety is a psychological state characterized by high levels of tension, persistent worry or excessive fear regarding an adverse event that may occur in the future and/or threatening/stressful situations (Kazdin, 2000). While anxiety is considered an adaptive response driving individuals when facing dangers, it can become dysfunctional when it becomes frequent and prolonged, disturbing well‐being and daily‐life, paving the way to the development of mood and anxiety disorders (Dean, 2016; Grillon, 2008; Weger & Sandi, 2018). Experiencing major and repeated traumatic events during childhood as well as chronic stress have also been associated with high levels of anxiety and increased risk for mood and anxiety disorders in adulthood (McLaughlin & Hatzenbuehler, 2009a, 2009b; Schmidt et al., 2000; Zavos et al., 2012).

Recent studies highlighted the importance of white‐matter (WM) brain architecture (i.e., myelinated fiber tracts connecting cortical areas within and between hemispheres as‐well‐as cortical areas with caudal parts of the brain and spinal cord; Lavrador et al., 2019) in anxiety/stress related atypical/pathological behaviors. WM is often characterized by three diffusion parameters; fractional‐anisotropy (FA) which represent the directional coherence of water diffusion and is considered a quantitative indicator of WM integrity (Le Bihan, 2003; Pfefferbaum et al., 2003), mean‐diffusivity (MD), an indicator of diffusion magnitude and radial‐diffusivity (RD), an indicator of de‐myelination processes (Beaulieu, 2002). Increased harm‐avoidance was found to be associated with decreased FA and increased MD and RD in corticolimbic circuits involved in emotional processing and reappraisal (Westlye et al., 2011). Differences in FA (Lu et al., 2018) and fiber number weighted networks (Yang et al., 2020) in widely distributed regions were detected between healthy highly anxious and low anxious individuals. Data published from the Human Connectome Project have showed that higher trait anxiety‐depression levels are predicitve of lower WM integrity in networks involving the amygdala in individuals with higher trait anxiety‐depression (De Witte & Mueller, 2017).

The above findings in healthy subjects mirror those previously reported in stress related pathologies, including post‐traumatic stress disorder (PTSD). PTSD may occur following exposure to one or more life threatening traumatic events and according to the Diagnostic and Statistical Manual of Mental Disorders (5th ed., DSM‐5; American Psychiatric Association, 2013) is characterized by four symptom clusters, including re‐experiencing, avoidance, hyper‐arousal and negative alterations in cognitions and mood. The mechanisms underlying the development of PTSD are not fully understood, but imaging studies suggest an association between PTSD and/or trauma exposure to WM dis‐integrity in various tracts, with WM volume and diffusion decrease being common finding (reviewed in Daniels et al., 2013).

Among the WM tracts that have been reported to be altered in PTSD, reports consistently point to alterations in the Corpus‐Callosum (CC). The CC is the principal WM fiber bundle connecting neocortical areas and plays an integral role in relaying sensory, motor and cognitive information (Gazzaniga, 2000). Alterations in the architecture of CC fibers were reported in non‐human primates exposed to early life stress (Coe et al., 2002; Sanchez et al., 1998), and in PTSD patients (De Bellis et al., 2002; Kitayama et al., 2007; Siehl et al., 2020; Villarreal et al., 2004). Previously, we reported CC volume reduction in PTSD that was associated with deficits in associative memory (Saar‐Ashkenazy et al., 2014). In addition, we have showed that CC FA values were associated with associative memory performance and core PTSD clusters, as expressed in correlation to levels of re‐experiencing, avoidance and hyper‐arousal in PTSD patients (Saar‐Ashkenazy et al., 2016).

The study of stress related mental illness sheds light on disorder characteristics and phenotype; however these human studies are largely unable to address causality and chronology; thus the question whether reported brain differences are a result of the stress or represent an existing predisposition for psychiatric disorders remains unanswered. Previous studies positively linked the occurrence of lifetime stressors and anxiety sensitivity, in a way that anxiety sensitivity can be regarded as a mechanism connecting stressful life events to the development of anxiety disorders (McLaughlin & Hatzenbuehler, 2009a, 2009b; Schmidt et al., 2000; Zavos et al., 2012). Notwithstanding, the relationship between the reported brain imaging findings to anxiety sensitivity, and more specifically, state and trait anxiety, is poorly understood. Trait anxiety is a continuum followed at its extreme by stress related pathology, thus studying WM brain correlates of state and trait anxiety offers potential insight into the etiology and maintenance of stress related disorders and may provide targets at which preventive interventions can be aimed before a mental illness develops.

To determine the association (if at all) of state and trait anxiety in healthy individuals and WM brain architecture (and specifically, the CC) and memory performance, twenty bachelor's degree first year students were recruited. Based on previous literature in patients, we hypothesized that higher trait and state anxiety levels will be associated with lower WM volume and dis‐integrity of WM diffusion characteristics. In addition, we hypothesized that higher trait and state anxiety levels will be associated with lower associative memory performance; both were previously reported to negatively alter in PTSD.

2. METHODS

2.1. Participants

Twenty students from Achva Academic College (10 males and 10 females) were recruited to the current study. Exclusion criteria included past psychiatric or neurological disorders (including mild traumatic brain injury), alcohol abuse, or the use of illicit drugs affecting the central nervous system, as was confirmed by the participants by self report. Participants whose body incorporated any metal or other contraindication to MRI were excluded from the study. All participants reported good health and no present illness and were rewarded for their participation with course credit. All procedures were approved by the Soroka University Medical Center Institutional Review Board and local institutional review board of Achva Academic College. All participants gave their written informed consent for participation.

2.2. Anxiety assessment

To assess anxiety levels, we used the Spielberger's State‐Trait Anxiety Inventory (STAI, Spielberger, 1983); a self report questionnaire that measures state and trait anxiety. The STAI questionnaire contains 20 items measuring state anxiety (for example “I am tense”) and 20 items measuring trait anxiety (for example “I worry too much over something that really doesn't matter”). Each item is rated on a 4‐point Likert scale (range from '1' = not at all to '4' = very much). The total scores of this measure are obtained by summing the values assigned to each item, and range from a minimum of 20 to a maximum of 80, with higher scores indicating more severe anxiety. Participants were given standardized instructions before completing the STAI questionnaire.

2.3. Memory assessment

To probe for memory performance, we employed a previously used memory paradigm that assess memory recognition for single versus associated stimuli (see Guez et al., 2011; Guez et al., 2013; Guez et al., 2016; Saar‐Ashkenazy et al., 2014; Saar‐Ashkenazy et al., 2016). We used one task that included two types of memory tests (words and pictures) with a similar construct (two blocks of words and another two for pictures, different stimuli were used in each learning phase and list). Prior to the actual memory assessment phase, all participants were given standardized instructions and performed one block of training (which included a learning list, followed by one item and one association recognition tests) that was not included in the statistical analysis.

2.4. Learning phase

The learning phase included a list of 19 pairs of unrelated emotionally neutral items, comprising 38 words/line‐draw pictures, presented on a computer screen (presentation duration = 4 s per pair), randomized across participants. Stimuli were compiled from high frequency common Hebrew nouns of unrelated (visually, semantically, or rhythmically) objects (Rubinstein et al., 2005). Learning was intentional: participants were asked to study both the individual items and the associated pairs. The learning phase was followed by a 30 s distraction task (counting backward in sevens from a randomly selected number) to prevent rehearsal between the learning phase and memory test. Following the learning phase, participants performed an item recognition task, followed by an associative recognition task.

2.5. Item recognition test

In this task, participants were tested on 12 stimuli, one at a time. Of these, 6 were targets (i.e., had appeared in the learning list), and 6 were distracters (i.e., new stimuli that had not appeared in the learning list), mixed randomly. Participants were informed that the list included targets and distracters, and were instructed to respond to each stimulus with a designated “yes” key (“1”) for targets and a “no” response key (“0”) for distracters.

2.6. Associative recognition test

In this task, participants were tested on 12 stimuli pairs; of those 6 were intact pairs from the learning list (i.e., the same pairs that appeared in the learning list) and another 6 pairs that served as distracters (i.e., recombined pairs that were created from items that appeared in the learning list, but were now recombined and presented as new pairs). Participants were informed that the list included similar and recombined pairs, and were again instructed to respond using the same keys. Stimuli that were used in the item test were not used in the associative test and vice‐versa.

2.7. Memory performance measures calculation

To assess performance, we computed two outcome measures: for items performance and for associative performance for each experimental block (i.e., two blocks of words and two blocks of pictures). Both outcome measures were calculated by the difference between the proportions of hits (responding “yes” to a target that had appeared in learning list of that specific block) minus the proportion of false alarms (responding “yes” to a distracter that had not appeared in learning list of that specific block). Thus, two item and two association performance measurements for each task (words and pictures) were created. Following this step, we averaged each measure (items and associative performance) across each task's blocks (words and pictures), thus remained with one representative item measure and one representative associative measure for each task (words and pictures) separately. Subsequently, we created an associative deficit index (ADI) reflecting the difference between the averaged item recognition and the averaged associative recognition performance separately for each task (words, pictures). The ADI was calculated as the averaged items recognition proportion minus the averaged associative recognition proportion; higher ADI scores reflect greater associative deficit.

2.8. Imaging

2.8.1. MRI data acquisition

Structural MRI scans were acquired on a 1.5‐Tesla scanner (Intera, Philips Medical Systems, Best), using a 6‐channel SENSE head‐coil. The pulse sequence used was a TIw‐FFE (spoiled gradient echo). A T1‐weighted whole brain anatomical scan was collected (twice) for each subject using the following parameters: repetition time = 15 ms, echo time = 4.6 ms, flip angle = 30°, matrix size 256 × 256, field of view 25.6 cm, 150 sagittal slices (1 × 1 × 1 mm resolution).

2.8.2. MRI volumetric data processing

Images were preprocessed before analysis by employing the Freesurfer software (3.0.2) on a linux platform. We averaged the two T1‐weighted whole brain anatomical scans for each subject. Affine registration with the Talairach space, followed by an initial volumetric labeling, was performed. Subsequently, skull stripping was performed (manual corrections were applied if needed), and the image was automatically sub‐cortically segmented and volumes of various anatomical structures were automatically calculated (Fischl et al., 2002, 2004). The calculated volumes were then divided by total intracranial volume (ICV) to enable cross subject comparison. According to our a priori hypothesis, the total CC volume ans its sub‐portions (anterior, mid‐anterior, central, mid‐posterior and posterior) were defined as regions of interest (ROIs) for analysis.

2.8.3. DTI data acquisition

DTI data were acquired on the same scanner using a single‐shot echo planar imaging sequence with SENSE parallel imaging (reduction factor = 2.5). Axial slices of 3.0 mm thickness were acquired parallel to the anterior‐posterior commissure line (AC‐PC). The in‐plane acquisition resolution was 2.88 × 3.58 mm. 42 slices were acquired with zero gap to cover the entire brain and brainstem. The DTI data were acquired along 16 directions with b = 1000 s/mm2. A TR/TE = 5711/95 ms was used without cardiac gating.

2.8.4. DTI data preprocessing & fiber tractography

All fiber tracking was performed using DtiStudio (H. Jiang, S. Mori; Johns Hopkins University, cmrm.med.jhmi.edu). At the first stage, we performed automatic image registration, that is, registered the diffusion weighted images to the b0‐image (b‐value = 1000) by applying affine linear registration (Woods et al., 1998), in order to correct distortions induced by eddy currents. Following this step, we applied the tensor calculation, which generated the apparent diffusion coefficient map. CC shapes were outlined upon the 0‐color map at the mid‐sagittal level and binary 2D images were generated. Segmentation of the CC (performed using an in‐house written Matlab script, see also Saar‐Ashkenazy et al., 2016) corresponds to the scheme proposed by Witelson (1989) that defines five vertical callosal segments based on specific arithmetic fractions of the maximum anterior–posterior extent. In particular, the CC is subdivided into regions comprising the anterior third, the anterior and posterior midbody, the posterior third, and the posterior one‐fifth. For each segment, fiber tracking was performed with the following parameters: fractional‐anisotropy (FA) threshold 0.25 and an inner product threshold of 0.7, which prohibited angles larger than 70° during tracking. This step yielded at an average FA, mean and radial‐diffusivity (MD, RD, respectively) value for each CC portion, for each subject that were defined as ROIs.

2.9. Statistical analysis

Data were analyzed using IBM Statistical Package for Social Sciences (SPSS, version 24©). Pearson correlation coefficients were calculated to assess the correlation between state and trait anxiety to the mentioned ROIs. Due to the co‐linearity between state and trait anxiety (r = 0.529, p = 0.016) correlations between state anxiety and imaging measurements and memory performance were controlled for trait anxiety levels using partial correlations coefficients. Due to technical problems in the volumetric analysis of one participant (caused by robust head movements), volumetric results are reported for 19 participants. One (other) subject was removed from the analysis of the memory task due to significant outliers in his performance, thus correlation analysis between memory performance and imaging results are reported for 18 participants.

3. RESULTS

Descriptive statistics for all variables, including demographic information, are presented in Table 1. Participants' age range was 22–26 years (mean age = 23.82, SD = 1.13), all had 12 years of education. Reliability of the STAI was acceptable (Cronbach α = 0.66 & α = 0.62, for state and trait, respectively). No significant correlations were found between anxiety (state/trait) and memory performance, thus, correlations are not reported.

TABLE 1.

Descriptive statistics of measured variables

Mean SD N
Demographic information Age (years) 23.82 1.13 20
Education (years) 12 0 20
STAI scores State anxiety 27.92 5.35 20
Trait anxiety 34.6 9.74 20
Memory (words) Item memory 0.58 0.31 19
Associative memory 0.27 0.29 19
ADI 0.31 0.38 19
Memory (pictures) Item memory 0.82 0.18 19
Associative memory 0.59 0.32 19
ADI 0.23 0.24 19
Imaging CC total volume 0.20 0.02 19
CC total FA 0.57 0.01 19
CC total MD 0.0007 0.00002 19
CC total RD 0.0004 0.00002 19
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Abbreviations: ADI, Associative Deficit Index; CC, Corpus‐Callosum; FA, Fractional‐anisotropy; MD, Mean‐diffusivity; RD, Radial‐diffusivity; STAI, State/Trait Anxiety Inventory.

3.1. Anxiety and CC imaging characteristics

Trait anxiety scores were significantly correlated to all three diffusion parameters; FA (r = −0.63, p = 0.003), MD (r = 0.46, p = 0.04) and RD (r = 0.58, p = 0.008) values of the total CC (see Figure 1). State anxiety scores were not correlated to any of the three diffusion parameters of the total CC (r = −0.004, 0.03 and 0.008 for FA, MD and RD, respectively, NS). Trait anxiety scores were not correlated to total CC volume (r = −0.13, p = 0.57), nor to its sub‐portions (r = −0.06, −0.07, 0.01, −0.05 and −0.21, for the anterior, mid‐anterior, central, mid‐posterior and posterior portions, respectively, NS). State anxiety scores were not correlated to total CC volume (r = 0.15, p = 0.53). A significant correlation between state anxiety scores to the volume of the mid‐anterior CC portion was found (r = 0.67, p = 0.002) but not to the other sub‐portions (r = −0.16, 0.36, 0.25 and −0.29, for the anterior, central, mid‐posterior and posterior portions, respectively, NS). Table 2 presents Pearson correlation coefficients between state/trait anxiety and CC sub‐portions’ diffusion characteristics.

FIGURE 1.

FIGURE 1

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Correlation analysis for trait anxiety levels and Corpus‐Callosum diffusion characteristics. Significant linear correlations were observed between trait‐anxiety and fractional‐anisotropy (a), mean‐ (b) and radial (c) diffusivity values of the Corpus‐Callosum (CC)

TABLE 2.

Pearson Correlation Coefficients between state/trait anxiety and Corpus‐Callosum sub‐portions’ diffusion characteristics

CC anterior CC mid‐anterior CC central CC mid‐posterior CC posterior
FA MD RD FA MD RD FA MD RD FA MD RD FA MD RD
STAI State anxiety a −0.12 0.04 0.00 −0.04 0.10 0.10 0.07 −0.04 −0.01 −0.26 −0.00 0.03 0.25 0.05 −0.13
Trait anxiety −0.40 b 0.24 0.38 −0.35 0.36 0.38 −0.63** 0.50* 0.58** −0.51* 0.21 0.34 −0.44 c 0.57** 0.69**
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Note: N = 19 for all correlations.

Abbreviations: ADI, Associative Deficit Index; CC, Corpus‐Callosum; FA, Fractional‐anisotropy; MD, Mean‐diffusivity; RD, Radial‐diffusivity; STAI, State/Trait Anxiety Inventory.

a

Partial correlations, controlling for trait anxiety.

b

Trend towards significance, p = 0.08.

c

Trend towards significance, p = 0.05.

*p < 0.05, **p < 0.01.

3.2. Memory performance and CC imaging characteristics

Correlation results for memory measures and CC imaging characteristics are presented in Figure 2. This correlation analysis revealed a significant positive correlation between CC total volume and associative memory recognition of pictures (r = 0.54, p = 0.019). Planned correlation analysis showed a significant correlation between associative memory recognition of pictures and the volume of the posterior portion (r = 0.64, p = 0.004), while trends were found for the mid‐posterior (r = 0.46, p = 0.054) and the central (r = 0.41, p = 0.08) portions. CC total volume also showed a significant negative correlation to the ADI scores of the pictorial test (r = −0.57, p = 0.012). Trends were found for the posterior (r = −0.44, p = 0.06), central (r = −0.45, p = 0.056) and anterior (r = −0.40, p = 0.09) portions.

FIGURE 2.

FIGURE 2

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Correlation analysis for memory performance and Corpus‐Callosum volume. Significant linear correlations were observed between Corpus‐Callosum (CC) total volume to associative memory for pictures (a) and associative deficit index (ADI) for pictures

4. DISCUSSION

In the current study we examined whether individual differences in state and trait anxiety (Spielberger, 1983) in a group of healthy participants are associated with associative memory performance and CC volume and diffusion characteristics; both have been shown to be negatively altered in stress related mental illness. We show that findings previously reported in PTSD, such as the correlation between CC WM characteristics (decreased volume and diffusion) and lower associative memory (Saar‐Ashkenazy et al., 2014, 2016), are evident in healthy young participants differing in their anxiety levels. Importantly, we were able to show significant correlations between CC WM diffusion characteristics and trait anxiety in healthy individuals that mirror the correlation previously reported between CC WM diffusion characteristics and PTSD symptoms' severity in PTSD patients (Saar‐Ashkenazy et al., 2016). Trait anxiety levels were negatively associated with CC WM integrity as observed by low FA values which represent low CC directional coherence of water diffusion. Trait anxiety levels were positively associated with high MD values, an indicator of diffusion magnitude and high RD values that may indicate de‐myelination.

Reductions in CC size have been reported in nonhuman primates exposed to early life stress (Coe et al., 2002), with changes in CC size correlating to cognitive deficits (Sanchez et al., 1998). Volume, area and micro‐structural integrity of the CC were previously associated with traumatic stress and PTSD diagnosis (De Bellis et al., 2002; Kitayama et al., 2007; Siehl et al., 2020; Villarreal et al., 2004). Altered CC WM intactness may affect functional connectivity across hemispheres and thus may lead to cognitive dysfunction as has been previously reported in PTSD patients (see meta analysis by Brewin et al., 2007). Indeed, the findings in the current study demonstrate the importance of intact CC volume to correct associative recognition, which has been reported to be altered in PTSD (Geuze et al., 2008; Golier et al., 2002; Guez et al., 2011; Saar‐Ashkenazy et al., 2014, 2016). Moreover, disturbed diffusion was detected in the central to mid‐posterior and posterior CC portions. These CC portions contain inter‐hemispheric projections from brain structures involved in brain circuits that regulate and mediate both emotional and cognitive processing core disturbances that were previously reported as associated with trauma and PTSD (Jackowski et al., 2008).

Although it is not clear whether WM anatomical connectivity architecture abnormality in PTSD is a developmental/genetic preexisting predisposition or is an outcome of the traumatic exposure, the results of the current study support the former. While previous studies conducted with posttraumatic patients (De Bellis et al., 2002; Kitayama et al., 2007; Saar‐Ashkenazy et al., 2014, 2016; Siehl et al., 2020; Villarreal et al., 2004) and other stress related disorders (Baur et al., 2011; Brambilla et al., 2012; Kim et al., 2017; Liao et al., 2014; Wang et al., 2016; Zhang et al., 2013; see also a review by Lee & Lee, 2020) reported various WM brain alterations, the major drawback of the majority of these studies is that they cannot determine causality, that is, they lack answer to the question whether the reported differences are a result of the trauma/disorder or represent an existing predisposition for stress disorders.

The current study suffers from several limitations; Firstly, sample was small and relatively homogeneous with regard to demographic nature. Further studies are warranted to test the relationship between anxiety, memory and CC white‐matter using larger heterogeneous samples. This will allow the implementation of complex quantitative models due to greater statistical power resulting from a larger sample size. Secondly, the current study in healthy individuals has showen that trait based individual differences in anxiety levels predispose individuals towards negative cognitive outcomes and brain alterations, as seen in PTSD patients however the cross‐sectional nature of this study prohibits confirmation of this possibility. Notwithstanding, it is supported by a recent review that targeted high trait anxiety as a key vulnerability phenotype for stress induced depression (Weger & Sandi, 2018) and empirical studies probing for the relationship between high‐trait anxiety (De Witte & Mueller, 2017; Lu et al., 2018; Yang et al., 2020) and/or anxiety related atypical behaviors (Westlye et al., 2011) to WM alterations. Additional support can be found in studies of healthy adolescences with family histories of depression that demonstrated abnormal WM architectural connectivity in at risk populations (Keedwell et al., 2012). Moreover, studies suggest WM microstructural alterations can be considered as endophenotypes candidates of stress related disorders, as they are heritable and co‐segregated within families with genetic vulnerability for these disorders (Roelofs et al., 2020). Due to the growing evidence of significant genetic involvement in WM intactness and the results of the current study that demonstrated an association between CC WM diffusion characteristics and trait anxiety levels, it is possible to postulate that dysfunctional WM can serve as a biological marker of genetic risk in stress related pathologies. Future prospective studies are warrant to further explore this hypothesis.

5. CONCLUSION

To conclude, our findings support the view that individual differences in CC WM characteristics may reflect trait based changes predisposing individuals towards higher anxiety sensitivity, and potentially to stress related disorders. Our data provide new insights into the structural mechanisms through which anxiety sensitivity, regardless of PTSD, might prompt vulnerability for psychopathology by promoting the understanding of the relationship between anxiety sensitivity (specifically, state and trait anxiety) to the reported brain imaging findings in humans. While further work in this field is required, the results of the current study address anxiety related individual differences in WM myelination mechanisms of the CC as potential contributors for stress related pathologies already at healthy stage. Identification of bio‐markers targeting individuals at‐risk can contribute and improve the understanding of the mechanisms underlying stress related pathology as‐well‐as the development of new intervention methods aimed at reducing mental illness.

CONFLICT OF INTEREST

All authors declare no conflict of interest.

ACKNOWLEDGEMENTS

This work was supported by the Deutsche Forschungsgemeinschaft (DFG): KI 537/29‐2, and the Israeli Ministry of Health (MOH): 87365411 to Prof. Alon Friedman and Prof. Ilan Shelef. We thank Ashkelon Academic College for their support in publication fees. All procedures were approved by the Soroka University Medical Center Institutional Review Board and local institutional review board of Achva Academic College.

Saar‐Ashkenazy, R. , Guez, J. , Jacob, Y. , Veksler, R. , Cohen, J. E. , Shelef, I. , Friedman, A. , & Benifla, M. (2023). White‐matter correlates of anxiety: The contribution of the corpus‐callosum to the study of anxiety and stress‐related disorders. International Journal of Methods in Psychiatric Research, 32(4), e1955. 10.1002/mpr.1955

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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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 upon reasonable request.

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