Attention Deficit/Hyperactivity Disorder in Children with Multiple Sclerosis

Abstract

Background:

Multiple sclerosis (MS) is a chronic neurological autoimmune disease; pediatric-onset multiple sclerosis (POMS) represents 5% to 10% of total MS population. Children with POMS may experience attention difficulties due to the disease’s impact on the central nervous system. However, little is known regarding Attention Deficit Hyperactivity Disorder (ADHD) in POMS, and its relation to cognitive performance.

Methods:

A retrospective case review was conducted using medical records of 66 children and adolescent patients diagnosed with POMS between 2012 and 2021 in a MS center of a tertiary medical center. All patients had undergone routine clinical neurological examinations and had been assessed for a diagnosis of ADHD by a department pediatric neurologist. In addition, sociodemographic data, disease-related variables, and cognitive performance were collected.

Results:

Of the 66 patients, 31 (47%) had a diagnosis of ADHD; 29 (44%) had cognitive impairment. Moreover, we identified four different profiles of POMS: those with only ADHD (17, 26%); only cognitive impairment (15, 23%), ADHD and cognitive impairment (14, 21%), and only POMS (20, 30%). A significant difference in disease duration was found among the four profiles [F(3,65) = 8.17, p < .001, η² = 0.29], indicating that patients with ADHD and cognitive impairment were characterized by longer disease duration.

Conclusions:

ADHD may be prominently involved in POMS, even during the early stages of the disease and early diagnosis is crucial in order to provide appropriate interventions and support.

Keywords

ADHD comorbidity adolescent ADHD cognition children

Introduction

Multiple sclerosis (MS) is a chronic neurological autoimmune disease where the body’s immune system mistakenly attacks the protective myelin sheath surrounding nerve fibers in the central nervous system. This demyelination leads to communication problems between the brain and the rest of the body, resulting in a wide range of neurological symptoms. This condition primarily affects adults, but may also occur in children, leading to pediatric-onset multiple sclerosis (POMS) (Alroughani & Boyko, 2018).

POMS represent 5% to 10% of the total population with MS (Krupp et al., 2013). While symptoms of POMS vary widely, cognitive dysfunction is one of the most common manifestations. The percentage of patients with POMS with at least a mild cognitive deficit range from 30% to 50% (Amato et al., 2008; Banwell & Anderson, 2005; Bobholz & Rao, 2003; Julian et al., 2013; MacAllister et al., 2005; Parrish et al., 2014). The most affected cognitive domains are complex attention, information processing speed, executive functions, verbal and visual memory, reasoning and problem-solving (Amato et al., 2008; Banwell & Anderson, 2005; Bobholz & Rao, 2003; Julian et al., 2013; MacAllister et al., 2005; Parrish et al., 2014). A Canadian study comparing the cognitive performance of POMS patients compared with demographically matched healthy controls over a 1-year period, found that 25% of patients showed clinically significant decline compared with controls (Parrish et al., 2013). In contrast, an Italian longitudinal study demonstrated that cognitive impairment in POMS tended to worsen after a mean period of 2 years from baseline evaluation. At follow-up, 70% of the cases were classified as having a deteriorating cognitive performance. Changes were prominent in tests of verbal memory, complex attention, verbal fluency, and receptive language (Amato, Goretti et al., 2010).

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental condition characterized by persistent inattention, hyperactivity, and impulsivity patterns that may have a significant impact on a child’s daily functioning (Tamm et al., 2021). Although the exact causes of ADHD are not fully understood, both genetic and environmental factors are believed to play a role (Eilertsen et al., 2019).

According to one comprehensive systematic review, approximately 5% of the pediatric population has been diagnosed with ADHD (Polanczyk et al., 2007) yet little is known as to the occurrence and/or effect this condition may have on pediatric patients with POMS. To the best of our knowledge, only one study has been conducted, using psychiatric interview with a small sample (N = 45); They found an incidence of 26% (Dwolatzky et al., 2003).

Children with POMS may experience attention deficit/hyperactivity difficulties due to the disease’s impact on the central nervous system. Understanding the ADHD effects on POMS and their association with cognitive impairment is needed to improve the recognition and possible prompt treatment of these issues. This study aims to evaluate the frequency of ADHD diagnosis among patients with POMS and to evaluate cognitive performance in patients with POMS using a validated computerized battery of cognitive tests.

Methods

Data were collected retrospectively, using information from assessments conducted between January 2012 and December 2022 at a large central tertiary medical center in Israel. Inclusion criteria were: POMS diagnosed according to revised McDonalds’ criteria (Thompson et al., 2018); aged <18 years; Expanded Disability Status Scale (EDSS) score (0–5) (Kurtzke, 1983). Exclusion criteria were: psychiatric diagnosis (i.e., major depression, anxiety disorders, obsessive-compulsive disorder, psychotic disorder); patients previously exposed or treated with psychotropic medications including ADHD medications such as methylphenidate and others; alcohol or substance use; ongoing relapse or steroid treatment during the 90 days preceding assessment.

Also, as part of their initial evaluation, patients underwent an evaluation to rule out additional underlying diseases (such as vasculitis, rheumatological diseases and coagulation disorders), vision tests, blood tests, and a specialized MRI used for MS diagnosis. In addition, all patients received a clinical neurological examination and ADHD assessment by a qualified and experienced pediatric neurologist, as part of their regular evaluation in the MS department of the tertiary medical center.

This study was conducted with the approval of the ethics institutional review board [5596-08].

Measurements

Sociodemographic and medical background information collected included gender, age, Disease-modifying treatments (DMTs), Disease duration in years elapsed from symptom onset to time of evaluation, as well as EDSS. EDSS is a method of quantifying disability levels in MS over time. The scale ranges from 0 to 10 in 0.5 -unit increments that represent higher levels of disability. Scoring is based on an examination by a neurologist. EDSS steps 1.0 to 4.5 refer to patients who are able to walk without assistance and is based on measures of impairment in eight functional systems, pyramidal, cerebellar, brainstem, sensory, bowel and bladder, visual function, cerebral functions, and cognition. DSS steps 5.0 to 9.5 are defined mainly by walking impairment (Kurtzke, 1983).

ADHD diagnosis: The diagnosis of ADHD was made according to the DSM-5 criteria by a qualified and experienced pediatric neurologist. The diagnosis including (1) a clinical interview and neurological examination to verify symptoms presentation prior to age 12; functioning interference in at least two settings and exclusion of other medical condition; (2) symptoms assessment using the long version of Conners’ Parent Rating Scale (Conners et al., 1998a) and the long version of Conners’ Teacher Rating Scale (Conners et al., 1998b); (3) A MOXO-CPT test, is a d-CPT (distractors-Continuous Performance Test), a standard computer-based and language-free test, which has been found both valid and utile in diagnosing ADHD in both pediatric and adult studies (Berger et al., 2017). MOXO was used as an additional tool for diagnosing ADHD, but was not essential for final diagnosis. Those who were diagnosed with ADHD (a) met all the criteria according to the DSM-5 symptoms, as reflected in both parent and teacher rating scales; (b) presented with functioning impairment and symptoms presentation prior to age 12. In addition to parents’ reports about other psychiatric disorders, all subjects met with a neuropsychologist for a comprehensive psychological evaluation to ensure that ADHD symptoms were not actually the manifestation of another disorder.

Cognitive assessment: (NeuroTrax: NT; NeuroTrax Corp, NY), a computerized battery of cognitive tests to assess cognitive abilities in various domains (Dwolatzky et al., 2003). The battery is easily administered and found to have good test–retest reliability and construct validity relative to paper-based tests in MS (Golan et al., 2019). Testing included the following cognitive domains: memory (verbal and nonverbal), executive function, attention, information processing speed, visual spatial processing, verbal function and motor skills. Each cognitive score was standardized relative to age-/education. stratified cognitively intact norms and presented in an IQ style scale. Each cognitive domain/index is normalized (M = 100, SD = 15), with the higher scores indicating better performance. Cognitive domain scores were computed as the average scores from particular tests (Achiron et al., 2007, 2013). Indices were composed of the following variables: Executive Function (EF)- Composite scores (accuracy divided by average response time) for the interference phase of the Stroop test and Go-NoGo test, as well as the mean accuracy score for the Catch Game. Memory- Mean accuracies for learning and delayed recognition phases of verbal and visuo-spatial perception. Visuo-spatial perception- The mean accuracy for the Visual Spatial Processing test. Verbal functions- The accuracy score for the verbal rhyming test. Attention- This variable included mean response times for the Go-NoGo test and no interference phase of the Stroop test, mean response time for a low-loaded stage of the Staged Information Processing test, mean standard deviation of response time for the Go-NoGo test, and mean accuracy for a medium-loaded stage of the Information Processing test. Information Processing Test ( IPS)- The composite scores (accuracy divided by average response time) for the various low and medium-loaded stages of the Staged Information Processing test. Motor skills- The mean time until the first move for the Catch Game as well as the mean inter-tap interval and standard deviation of the inter-tap interval for the Finger Tapping test. Global Cognitive Score (GCS)- An average of all of the indices.

The NT is widely used and validated in cognitively healthy individuals (Sasson et al., 2013), those with mild cognitive impairment, and those with MS (Achiron et al., 2007; Golan et al., 2019). In addition, it was found that NT detected cognitive impairment among PwMS with 85% sensitivity and 70% specificity (Golan et al., 2019). The test was found to have good construct validity when compared with paper-based tests, including the Weschler Memory Scale, Wechsler Adult Intelligence Scale, Rey Auditory-Verbal Learning Test and the Stroop test (Dwolatzky et al., 2003).

Testing time was approximately 45 min. We adopted the traditional benchmark definition for cognitive impairment when a score falls at least one standard deviation below the normative mean, in at least two tests (Achiron & Barak, 2003; Amato, Portaccio et al., 2010; Hancock et al., 2023; Lezak et al., 2012).

Statistical Analysis

Continuous variables were described as mean and standard deviation (M ± SD), categorical variables with frequency and percentage. The Chi-square goodness of fit test was performed to check the frequencies of ADHD in the sample, and then compared them with statistics based on general population data. Group comparison has been performed using the Student’s t test, and when the assumptions are not met the criteria for assuming a normal distribution, we performed Mann-Whitney U test and the Welch’s t test. Fisher’s exact test was conducted for the categorial variable. One-way analysis of covariance (ANCOVA) was conducted to compared between the groups with age at disease onset as a covariate, followed by Tukey’s HSD post hoc tests. A grading system was applied to individual cognitive tests for each patient, based on the number of SDs below the normalized mean. Statistical analysis was performed by using SPSS software (v. 26, SPSS, Chicago). The statistical significance was determined at p < .005.

Results

Overall, 66 patients with POMS were included in the study; 43 were female (65.2%), with a mean age of 15 ± 2.87 years, mean EDSS score of 2.72 ± 1.28, and a mean disease duration of 1.39 ± 3.35 years. From this group, 50 had been treated with DMT (75.8%) for a period of at last 1 year, and 16 (24.2%) had received no treatment (Table 1).

Table 1.

Demographic and Clinical Characteristics (N = 66).

Age at disease onset in years (mean)	15.0 (SD = 2.87); (Range 5–18)
Disease-modifying treatments (DMT)
°DMT	50 (75.8%)
°Female	33
°Male	17
°No DMT	16 (24.2%)
°Female	10
°Male	6
EDSS (Mean)	2.72 (SD = 1.28; Range 0–5)
Disease Duration in years (mean)	1.39 (SD = 3.35)

Of the full sample, 31 (47%) met the criteria for ADHD and were included in the study; 19 (61%) were females. A Chi-Square Goodness of Fit Test was performed to compare the proportion of ADHD among POMS to the proportion of ADHD in the general population, 5% occurrence in the general population, as determined in the literature (Li et al., 2023; Polanczyk et al., 2007). which showed a significant difference in proportions (X²(1) = 244.75, p = .000), The probability of ADHD occurring in our sample was four times higher than in the general population. In addition, regarding ADHD specifiers in our sample – of the 31 who met the criteria for ADHD, 23 (74%) met the criteria for inattentive, 4 (13%) met the criteria for hyperactivity, and 4 (13%) met the combined criteria.

Those participants with a diagnosis of ADHD were compared to those who did not demonstrate ADHD, looking at gender and age at POMS onset. Welch’s t-test revealed a significant difference in age at disease onset (t(64) = −2.19, p < .05) showing that those with ADHD were significantly older (15.77 ± 1.91) than those who did not demonstrate ADHD (14.31 ± 3.38). There were no differences between genders.

Using a computerized cognitive battery of testing (the Neurotrax battery), we found significant differences in attention ability (t(59) = 2.14, p < .05), showing that POMS with ADHD performed lower in the cognitive domain of attention (88.44 ± 18.29) than POMS with no ADHD (96.36 ± 10.65) (Table 2).

Table 2.

Mean and Standard Deviation Scores of Cognitive Performance According to ADHD Diagnoses.

Cognitive Domain	TotalN = 61	ADHDN = 29	No-ADHD N = 32	Statistic value	p-Value	Effect size (Cohen’s d)
GCS	91.03 + 12.88	89.62 + 15.21	92.30 + 10.42	0.796	.430	0.21
Memory	92.09 + 17.56	89.14 + 19.41	94.76 + 15.59	1.251	.216	0.32
EF	93.34 + 15.62	91.51 + 17.2	95.0 + 14.11	0.868	.389	0.22
Visual	95.50 + 16.80	96.86 + 20.33	94.26 + 13.01	−0.589	.559	0.15
Verbal	83.80 + 23.51	84.43 + 25.72	83.23 + 21.72	−0.196	.845	0.05
Attention	92.45 + 15.11	88.44 + 18.29	96.36 + 10.65	2.135	.038*	0.53
IPS	89.15 + 16.87	87.72 + 12.46	90.44 + 20.18	0.626	.534	0.16
Motor	93.70 + 18.09	90.22 + 21.26	96.85 + 14.26	1.442	.155	0.37

Note. We are missing data from five patients with no cognitive assessment (n = 5). GCS = global cognitive score; EF = executive functions; IPS = information processing speed.

p < .05.

Among the total sample, verbal function (83.8) was the most impaired domain, with participants scoring one SD below the average score (>85) (Table 2). Twenty-nine (44%) participants were defined as cognitively impaired. There was no significant difference in gender and age of disease onset among any of the groups. We determined four different profiles accordingly: patients with only ADHD (17, 26%), only cognitive impairment (15, 23%), ADHD and cognitive impairment (14, 21%), no ADHD or cognitive impairment (20, 30%) (Figure 1).

Figure 1.

Profiles frequencies according to ADHD and cognitive impairment status.

The ANCOVA analysis comparing the four profiles’ differences in disease variables (EDSS, disease duration, and DMT status) identified a significant difference among those with both ADHD and cognitively impaired versus all other groups when comparing disease duration with age at disease onset as a covariate [F(3,65) = 8.17, p < .001, η² = 0.29]. No other differences were found among the groups (Table 3).

Table 3.

Disease Characteristics by Profiles.

	No presence of ADHD or cognitive impairment (n = 20)	Only cognitive impairment (n = 15)	Only ADHD (n = 17)	ADHD+ cognitive impairment (n = 14)	Statistic	p-Value
EDSS (M ± SD)	2.62 ± 1.55	2.90 ± 0.93	2.29 ± 1.25	3.18 ± 1.14	F = 1.50	.223
Disease Duration (M ± SD)	0.60 ± 1.09	0.33 ± 1.05	0.41 ± 1.06	4.86 ± 5.93	F = 8.17	.000
DMT
°Yes	5 (25%)	1 (6.7%)	7 (41.2%)	3 (21.4%)	Χ² = 5.24	.155
°No	15 (75%)	14 (93.3%)	10 (58.8%)	11(78.6%)	Χ² = 5.24

Discussion

ADHD and POMS are medical conditions that affect children’s well-being and cognitive functioning. While a diagnosis of ADHD and POMS may seem unrelated at first glance, one study (Weisbrot et al., 2014) found an intriguing connection between these two conditions, as they both share some similar neurological mechanisms and overlapping symptoms that involve abnormalities in the central nervous system. In a population-based study in Norway that evaluated millions of Norwegian children born between 1967 and 2008, the researchers found that within the ADHD children’s group, the frequency of mothers with inflammatory chronic diseases was higher, showing an association between ADHD in offspring and maternal disorders with underlying immune factors, including multiple sclerosis. Maternal multiple sclerosis was associated with 80% higher odds of diagnosing ADHD in children (Instanes et al., 2017). In our study, we found that 47% of the participants with POMS also exhibited ADHD, which is significantly greater than the average of the 5% of children with ADHD reported in the general population (Polanczyk et al., 2007). This may be explained partially by the similar pathology of both POMS and ADHD within the brain.

In POMS, similar in ADHD, there exists a neurotransmitter imbalance, particularly dopamine and norepinephrine, which play a crucial role in regulating attention, impulsivity, and hyperactivity (Del Campo et al., 2011; Ulke et al., 2019). In POMS, there is also an over-activation of the immune system during the adolescent period, as can be seen by a higher relapse rate of elevated levels of inflammatory markers in individuals with ADHD. This may indicate a shared immune dysregulation happening similarly in both processes (Menascu et al., 2018, 2021). As the main pathology in MS is the immune system’s attack on myelin, leading to impaired neural transmission and disruption of neural pathways at a period when brain development of attention and other cognitive processing takes part (Wekerle & Lassmann, 2005). This may later lead to an acceleration of ADHD formation. Our finding that participants with ADHD were significantly older than those with no ADHD, strengthens the assumption of a common pathways, as the older group had more time to be affected by an imbalanced and over-activated immune system.

From a neuropsychological perspective, the application of the cognitive-energetic model (Sergeant, 2000) is another possible approach to explaining the higher ADHD frequency. This model argues that ADHD has effects at three levels: cognitive mechanisms, such as response output, energetic mechanisms, such as activation and effort, and control systems of executive function (Bobholz & Rao, 2003). Unlike traditional models that claim that ADHD is a prefrontal deficit and solely explainable by disinhibition (Barkley, 1997) the cognitive-energetic model is an attempt to encompass both top-down and bottom-up processes. In the case of POMS, it takes into account not only neural and immune mechanisms but also state-based factors, including insufficient motivation (i.e., effort). The emotional and physical challenges associated with living with POMS, lead to low self-reliance and reduced adaptive functioning and motivation (Till et al., 2012) and can contribute to difficulties in maintaining focus and attention, which is particularly striking in POMS, since they occur during their formative years, therefore affecting their academic and social activities (Rocca et al., 2014).

The empirical literature dealing with cognitive deficits in POMS points to a range from 30% to 50% (Amato et al., 2008; Banwell & Anderson, 2005; Bobholz & Rao, 2003; Julian et al., 2013; MacAllister et al., 2005; Parrish et al., 2014), depending on type cognitive tests, sample size, and disease characteristics (Portaccio et al., 2021). In line with a previous study (Rocca et al., 2014), our study found that 44% were classified as having cognitive impairment. Notably, verbal function (83.8) was the most impaired domain in our sample. As shown in a review by Ekmekci (2017), many studies among POMS identified impairments in verbal functions, mostly reflected in verbal fluency tests (Amato et al., 2010a). Interestingly, our study extends the literature by examining verbal rhyming in POMS, which is a higher-order ability to form an association among similar-sounding words. Both verbal fluency and verbal rhyming require similar visual scanning of the stimuli, sustained attention, and response selection. Whereas verbal fluency often reflects frontal lobe functions (Abe et al., 2004; Gaillard et al., 2000) and is commonly interpreted as a measure of “executive function” (Lezak et al., 2012), fMRI study found that verbal rhyming has more “specific” activation of language regions (Lurito et al., 2000).

Additionally, Rocca et al. (2014) performed a structural and functional MRI examination to investigate the mechanisms responsible for the presence and severity of cognitive impairment in pediatric patients with MS. The researchers used voxel-based analysis with advanced structural MRI techniques to determine the patterns of regional involvement of white and gray matter according to patient’s cognitive profile. Of the 35 participants with POMS, 45% were classified as cognitively impaired; the spatial, verbal memory abilities and language were the most significantly involved cognitive areas. The results showed that cognitively impaired patients with POMS had a higher occurrence of T2 lesions and white and gray matter damage, including atrophy and diffusivity abnormalities in the posterior region of the parietal lobes close to midline (precuneus, posterior cingulum, and corpus callosum (Rocca et al., 2014). It is important that further research in cognitive impairments in POMS use multiple tests to obtain purer objective and subjective measures and improve reliability, also by combining theory-driven and research of specific cognitive abilities (Snyder et al., 2015); Moreover, to explore the relations of ADHD to different cognitive domains in POMS by a variety of statistical methods, that consider the risk for false positive (type I) and false negative (type II) results.

The role of disease duration is another notable finding that emerged from our research; those participants with ADHD and cognitive impairment, differed significantly in disease duration (4.86 + 5.93 (from those with only ADHD (0.41 + 1.06), or those with only cognitive impairment (0.33 + 1.05). This suggests that in the early stage, some patients present with ADHD while others present cognitive impairment, which may reflect different manifestations of brain damage.

As the disease progresses over time, researchers identify more comprehensive cognitive deficits (Prakash et al., 2008). Several longitudinal studies among adults with MS showed that the proportion of cognitively preserved patients decreased from 74% at baseline to 44% after 10 years from diagnosis, while the proportion of patients with mild or moderate impairment increased (Amato et al., 1995, 2001). Longitudinal studies in patients with POMS have provided conflicting results about the evolution of cognitive deficits (Amato et al., 2016); however, the trend seems that longer disease duration is associated with greater cognitive deterioration, even only after 1 year period, lesion volume increased and associated with slower psychomotor speed (Till et al., 2013). Cognitive functions tend to worsen over time, as an Italian study demonstrated, cognitive deterioration was observed in 56% of the patients affecting the patient’s academic and professional achievements (Amato et al., 2014). These findings support the existence of a therapeutic window in the first 5 years, (Achiron et al., 2013; Harel et al., 2019), highlighting the importance of evaluating ADHD in the early stages after diagnosis of POMS, and not only focusing on cognitive performance.

Children with chronic illnesses like POMS, often experience emotional and psychosocial challenges due to the nature of their condition. Dealing with a chronic illness can cause stress, anxiety, and mood disturbances (Amato et al., 2008; Till et al., 2013). These emotional factors can, in turn, contribute to attention difficulties and impulsive behavior, even further complicating the management of ADHD. Moreover, both conditions can lead to problems with executive functioning skills, such as planning, organization, and working memory. In addition, children with MS might experience “brain fog” or reduced cognitive processing speed, which can manifest similarly to inattention in those with ADHD. It is essential to recognize that the impact of POMS extends beyond physical disability; cognitive functions play a crucial role in a child’s daily life, education, and overall well-being.

Another intriguing aspect may lie in the association between inattention and increased disease severity; as other studies have also suggested (Amato et al., 2001; Till et al., 2013) inattention may serve as both an indicator and a contributor to the progression of POMS. This association may be due to the demyelinating lesions affecting regions responsible for attention and cognitive functions (Harel et al., 2019), as well as the fact that the age at which POMS manifests can influence neurodevelopmental outcomes, and early-onset cases may have a more profound impact on cognitive functions, including attention. However, the EDSS scale is not a sensitive measure for cognitive assessment in POMS; in most cases, the EDSS scale and cognitive function are not correlated. These shared symptoms go beyond the traditional focus on physical disability, and emphasize the need for a more inclusive approach to assist healthcare professionals to exercise caution and conduct comprehensive assessments when evaluating children with POMS.

To conclude, the relationship between ADHD and POMS is a complex and evolving area of research. Early and accurate diagnosis is crucial in order to provide appropriate interventions and support for children with both conditions. Shared neurological mechanisms and overlapping symptoms suggest a possible connection between the two conditions, but the precise nature of this relationship requires further investigation. Proper diagnosis and treatment planning are essential to ensure that POMS receive appropriate support for their attention difficulties, leading to improved academic and social outcomes. By continuing to explore the intersection of ADHD and MS, medical professionals can better address the unique needs of affected children, ultimately enhancing their overall well-being and quality of life. Future studies may consider evaluating cognitive reserve in POMS, as higher cognitive reserve was found to be the major predictor of cognitive stability or improvement over a follow-up longer than 4 years in patients with POMS (Pastò et al., 2016). Further research into the relationship between these conditions will undoubtedly contribute to a better understanding and improved management of both MS and ADHD in this unique group of pediatric patients.

Supplemental Material

sj-docx-1-jad-10.1177_10870547241232710 – Supplemental material for Attention Deficit/Hyperactivity Disorder in Children with Multiple Sclerosis

Supplemental material, sj-docx-1-jad-10.1177_10870547241232710 for Attention Deficit/Hyperactivity Disorder in Children with Multiple Sclerosis by Roy Aloni, Alon Kalron, Assaf Goodman, Amichai Ben-Ari, Talya Yoeli-Shalom and Shay Menascu in Journal of Attention Disorders

Footnotes

Acknowledgements

The authors would like to thank Hila Cherpakov for her data collection assistance and Aviva Yoselis for her editorial assistance

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Roy Aloni

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Roy Aloni is a senior lecturer and Head of the “trauma and chronic diseases” lab in the Department of Psychology at Ariel University, Israel. He is also a rehabilitation psychologist and head of the psychological unit at Multiple Sclerosis Center, Sheba Medical Center, Israel. His research topics include interactions of physical, cognitive and psychological aspects of Multiple Sclerosis as well as PTSD and various aspects of trauma consequences.

Alon Kalron is an Associate Professor and Chair of the Department of Physical Therapy, School of Health Professions, Faculty of Medicine and Health Sciences, Tel-Aviv University. He also serves as Head of the Physical Rehabilitation Research Unit at the Multiple Sclerosis Center, Sheba Medical Center, Tel-Hashomer, Israel.

Assaf Goodman is research assistant at Cohen-Harris Resilience Center. He is a Psychologist in training during master’s studies at Ruppin Academic Center.

Amichai Ben-Ari is a rehabilitation psychologist, a lecturer in the psychology department at Ariel University, a researcher in the field of PTSD, and the director of the neuropsychiatric clinic at the Hadassah Medical Center in Jerusalem.

Talya Yoeli-Shalom is a Rehabilitation Psychologist and research coordinator at the Multiple Sclerosis Center, Sheba Medical Center, Israel.

Shay Menascu is a senior pediatric neurologist at the Multiple Sclerosis Center, Sheba Medical Center at Tel-Ashomer and at the Department of Pediatrics at the School of Medicine, Faculty of Medical and Health Sciences, Tel Aviv University, Israel. He is the director of the service for children with Multiple Sclerosis with a multidisciplinary team that supports pediatric and adolescent patients with this complex disease. He also works at the Neuroimmunology clinic where children with other White Matter diseases, Myestenia Gravis and other diseases are being treated.

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