Volume 11, Issue 1 (February 2024)                   Avicenna J Neuro Psycho Physiology 2024, 11(1): 27-31 | Back to browse issues page

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Khanlarzade F, Asgari K, Rezaei S, Saberi A. Striatal Beat Frequency Model in Multiple Sclerosis: Evidence for the Role of Thalamus. Avicenna J Neuro Psycho Physiology 2024; 11 (1) :27-31
URL: http://ajnpp.umsha.ac.ir/article-1-473-en.html
Striatal Beat Frequency Model in Multiple Sclerosis: Evidence for the Role of Thalamus
Faeze Khanlarzade1 , Karim Asgari2 , Sajjad Rezaei3 , Alia Saberi4
1- PhD student in psychology, Department of Psychology, University of Isfahan, Isfahan, Iran.
2- Associate professor in neuropsychology, Department of Psychology, University of Isfahan, Isfahan, Iran. , k.asgari@edu.ui.ac.ir
3- Associate professor, Department of Psychology, University of Guilan, Guilan, Rasht, Iran
4- Professor, Department of Neurology, Guilan University of Medical Sciences, Guilan Iran.
Keywords: Meck's model, Multiple sclerosis, Neuroanatomical, Reaction time
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Background
From a voluminous perspective, all forms of animal and human behaviors are associated with time, and the capacity to measure time in seconds to minutes and hours is a cornerstone to delineate different forms of behavior. Accordingly, all aspects of human behavior, including talking, memorizing, remembering, thinking, perception, and reaction time, are somehow interwoven with the ability to mentally manipulate, predict, and alter time. Timing behavior, in turn, is interrelated to the cortical and subcortical parts of the brain, which have been extensively investigated in recent decades. One of the most compelling theories in characterizing timing behavior was proposed by Matell and Meck. It is based on the notion that timing is an emergent activity arising from thalamo-cortico-striatal loops; therefore, it has become famous as striatal beat frequency (SBF) model of timing behavior [1-2]. The theoretical framework of our study is generally based on the presumptions of SBF model; nonetheless, detailed discussion of the model is beyond the scope of this paper.
Multiple sclerosis (MS) is described as the most common disorder of the nervous system in young adults and is thought to be associated with a defect of the autoimmune system, in which neural tissue is targeted by two important types of immune cells, mainly T cells and B cells [3]. The symptoms of MS range from fatigue to bladder and bowel control, depression, pain, and cognitive dysfunctions. Mild cognitive impairment was reported in 40% of patients during the first stage of their disease; nonetheless,  it may become more profound as the disease progresses into its severe stages [4-5-6]. A wide array of studies have pointed to the speed of information processing as one of the most affected aspects of cognition in MS. In recent years, reaction time has been proposed and utilized as a marker for cognitive impairments following neurological conditions, and some recent studies suggested that it may serve as a beneficial alternative in the assessment of information processing [7-8-9].
The current study aimed to assess the possible effects of MS on simple and selective reaction time. It was hypothesized that MS lesions in cortical and subcortical areas may have a role in estimating and responding to visual stimuli through simple and selective forms of reaction time. The author's purpose was to explore the SBF model as a framework for interpreting MS lesions in relation to time estimation and defects in reaction time response. A considerable body of research in recent years has examined different aspects of timing processes, including timing differentiation and discrimination both in animals and humans; nevertheless, some other important issues remained to be explored, among which reaction time merited further investigation. Our argument is based on the ground that there might be a relationship between lesions in different parts of the brain, according to the SBF model, and the ability of MS patients to respond correctly to visual stimuli.

Objectives
The present study aimed to assess the timing function of patients with multiple sclerosis (MS) according to the Striatal Beat Frequency (SBF) model of timing behavior, with particular regard to reaction time.

Materials and Methods
Subjects
This was a retrospective study in which 44 patients were recruited from the MS Society of Pursina Hospital in Guilan province, Rasht, through a purposive sampling method from December 2021 to June 2022. The inclusion criteria were: diagnosis of MS by a specialist in neurology, reading literacy, and basic ability to work with computers. On the other hand, the exclusion criterion was having another neurological or psychiatric disorder other than MS. Demographic data of the patients was gathered, their disability was measured, and their MRIs were inspected by a neurologist in order to find the location of the lesions. The patients were requested to give written consent for their participation in the study, and the design of the study was approved by the Ethics Committee of Isfahan University in accordance with the Helsinki Declaration (IR.UI.REC.1401.123).

Experimentation and Instrument
In order to measure simple and selective reaction time, we used a reaction timer apparatus, which was designed and developed at the Department of Physics at Sharif University and manufactured by Sina Psychological Corporation. The software is able to measure reaction time in a range of 1-3000 milliseconds. The test-retest reliability of the measures was obtained at 0.84 in previous studies [10].
At the commencement of the experiment, the task was explained to each patient, and he/she was then asked to press certain buttons on the keyboard following the onset of visual stimuli. There were 10 trials for each patient, elapsed time after the exposure of stimuli was measured by the computer in milliseconds, and the number of errors was calculated as well. Each patient's task lasted for eight minutes.

Data analysis
The data were analyzed in SPSS software (version 26) using the Mann-Whitney U test, analysis of variance (ANOVA), analysis of covariance (ANCOVA), student's t-test, and logistic regression.
Transparency and open

Table 1. Mean and Standard Deviation of the sample
Dependent variable N male Female mean Std. Deviation Age Mean
simple 28 15 13 560.30 340.42 37.75
reaction 16 3 13 587.82 268.28 39.00
time 44 570.30 313.13
selective 28 718.93 272.31
reaction 16 1322.50 2445.46
اtime 44 938.41 1489.62
Results
Based on the ANCOVA test, after controlling the gender variable, there was a statistically significant difference between patients with and without lesions in the thalamus and basal ganglia in their selective reaction time; however, there was not a significant difference in patients' simple reaction time. Descriptive statistics are illustrated in Table 1. As depicted in Table 2, MS plaques were remarkably observed in the parietal and frontal lobes, respectively. The lesions were both cortical and subcortical, located in the frontal, occipital, and parietal lobes and in the thalamus as well. Table 3 demonstrates the statistical frequency of lesions in different parts of patients' brains.

Table 2. Percentage and locations of MS plaques
Location of plaque No Cortical Subcortical both
N Percent N Percent N Percent N Percent
Right Frontal Lobe 28 65.1 0 0.0 13 30.2 2 4.7
Left Frontal Lobe 26 59.1 0 0.0 15 34.1 3 6.8
Right Parietal Lobe 11 25.0 0 0.0 30 68.2 3 6.8
Left Parietal Lobe 4 9.1 1 2.3 35 79.5 4 9.1
Right Temporal Lobe 33 75.0 3 6.8 6 13.6 2 4.5
Left Temporal Lobe 35 79.5 4 9.1 4 9.1 1 2.3
Right Occipital Lobe 39 88.6 0 0.0 5 11.4 0 0.0
Left Occipital Lobe 41 93.2 0 0.0 3 6.8 0 0.0
no yes  
N Percent N Percent  
 Left Thalamus 40 90.9 4 9.1  
Right Thalamus 44 100.0 0 0.0  

Table 3. Statistical frequency of lesions in different parts of the patient's brains
variable Regression with
Simple RT		 P-value Regression with Selective RT P-value
Age .405** .006 .397** .008
Education -.507** <.001 -.536** <.001
Age of onset .226 .141 .228 .137
Duration of MS .191 .215 .226 .140
EDSS .503** .001 .546** <.001
Brain plaqes -.059 .705 -.061 .694
symptom .496** .001 .442** .003
BMI .366* .014 .329* .029

Table 4. Statistical values of the relationship between reaction time and individual and clinical characteristics of the patients
Dependent Variable Independent variable group N Mn interquartile range Mean Rank z statistic P-value
Simple RT Gender male 18 441.40 649.53 25.17 -1.146 .252
female 26 399.20 261.53 20.65
Location Town/city 38 403.20 436.33 22.37 -.171 .864
Village 6 432.10 293.35 23.33
Family History no 37 421.10 457.80 23.84 -1.588 .112
yes 7 386.10 111.60 15.43
Hospitalization History no 11 344.60 83.70 13.27 -2.751 .006
yes 33 439.20 550.55 25.58
Selective RT Gender male 18 665.0 352.50 24.06 -1.365 .172
female 26 610.0 232.50 21.42
Location town/city 38 645.0 247.50 21.70 -1.241 .214
village 6 755.0 273.00 27.58
Family History no 37 710.00 365.00 23.72 84.50 .148
yes 7 610.00 100.0 16.07
Hospitalization History no 11 550.00 230.0 17.50 -1.492 .136
yes 33 710.00 315.0 24.17
Table 4 displays the statistical values of the relationship of reaction time with the individual and clinical characteristics of the patients. Table 5 illustrates the results of the Man Whitney-U test for the difference between simple and selective reaction time of patients.

Table 5. Results of Man Whitney-U test for difference between simple and selective reaction time of patients
variable Correlation Coefficient P-value
number of Hospitalization .398 ** <.001
Age of onset .07 .653
Duration of MS .296 .051
Selective R T .546** .001
Simple R T .503 ** .001
Number of Brain plaques .065 .677
number of symptoms .58** .001
BMI .092 .554

Table 6. Relationship between physical disability and clinical features of patients
variable group mn interquartile range Mean Rank z statistic P-value
Gender male 3.5 2.25 23.5 -0.433 .665
female 3 2. 21.81
Hospitalization History no 2 1.5 15.64 -2.061 .039
yes 3 1.5 24.79
Table 6 presents the relationship between physical disability and clinical features of patients.

Discussion
The current study aimed to assess the neuropathological lesions following MS and their effect on reaction time with a glance at the SBF neuroanatomical model of timing behavior. In this section, we first discuss the findings regarding the simple and selective reaction time, and we will then consider the implication of our findings in regard to the SBF model. The reaction time speed was lower in older patients; nonetheless,  this finding seems insignificant since both patients and normal people may become slower in reacting to stimuli with increasing age [11]. This finding is concordant with a study by Turgeon, Lustig, and Meck (2016), [12] which emphasized that age difference is crucial in interpreting the findings of timing experiments on neurological and psychiatric disorders. 
Inconsistent with the report of Martola et al. (2008) [13], we found no relationship between the number of lesions, age of onset, duration of illness, and reaction time in patients. However, it is in line with a study by Matell and Meck (2004) [2], which reported that while cortico-striatal circuits are the most sensitive part of neural networks in timing behavior, age-related differences may be compensated by "degeneracy," which help the organism to protect itself against loss of a vital function. According to the SBF model [2-14], cortical damage is expected to alter the network input to coincidence detectors, which in turn may lead to the disruption of their capacity to respond appropriately during reaction time. In agreement with the findings of the studies by Khanlarzadeh et al. (2015), Loerding et al. (2016), and Chiaravalloti and Deluca (2008) [10-15-16], the simple reaction time was negatively correlated with total number of symptoms, body mass index, disability, and academic level of patients. In line with a study by Vanessa et al. (2018) [17], we found no relationship between family history of MS and reaction time. Nevertheless, in accordance with the studies by Amato et al. (2001, 2006) and Król et al. (2015) [4-5-18], the relationship of reaction time with a history of illness and hospitalization was found to be significant.
In agreement with a study by Rogers et al. (2007) [19], the speed of selective reaction time was significantly related to the age of patients; however, it was related neither to gender nor living in rural and urban areas. Moreover, in line with the findings of Bisecco et al. (2018) [20], selective reaction time was not related to the total number of lesions; instead, it was correlated with the location of lesions, including the thalamus. This finding is basically congruent with the theoretical role of the thalamus in Meck's neuroanatomical model of timing. As stated by Meck, thalamus glutamatergic pathways to the cortex provide a main substrate for differential responses toward stimuli [21].
Another important factor in relation to selective reaction time was the level of disability. The more disabled patients had an increased reaction time toward selective stimuli. Feuillet et al. (2007) [3]  reported similar results in MS patients. In the present study, the locations of MS plaques were found to be more related to reaction time in comparison with the total number of lesions. That may be due to different underlying mechanisms which are thought to be responsible for those effects. For instance, Paton and Buonomano (2018) [22]  argue that there might be different substrates for subsecond versus suprasecond timing in the brain areas. In the same line, in their study on the pathogenesis of demyelination, Lucchnetti et al. (2000) [23]  stated that autoimmune reactions through B-cells may lead to an increase in the plaques, while inflammatory reactions through T-cells correspond to the location of lesions [23] [18]. The same findings were reported by Gioia et al. (2007) and Bisecco et al. (2018) [24, 19].

Conclusions
In conclusion, we found that the simple and selective reaction time of MS patients seems to be affected differently by the disease. This finding suggests that there may be different pathways or substrates for the simple versus selective reaction time, which is consistent with some other reports suggesting different networks for diverse forms of timing behavior in other neurological disorders, including dementia and Alzheimer's disease [25]. Furthermore, we found that the thalamus and parts of basal ganglia were the most important structures related to reaction time in MS. This is concordant with the SBF model regarding the role of subcortical nuclei as the vital underlying parts of timing behavior. Among the notable limitations of this study are the inaccessibility of patients from other parts of the country and the sampling during the COVID-19 pandemic. Therefore, great caution should be exercised when generalizing the results of other populations. Furthermore, we were not able to have an encompassing overview of the SBF model of timing due to project limitations.

Compliance with ethical guidelines
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Acknowledgments
The authors would like to express their deepest gratitude to the patients who participated in this study for their patience and support.

Authorsʼ contributions
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Funding/Support
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Conflicts of Interest
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Article Type: Research Article | Subject: Cognition
Received: 2023/11/27 | Accepted: 2024/06/8 | Published: 2024/06/10
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