Working memory is a system that temporarily stores and manipulates information used in tasks like decision-making and reasoning. Patients with multiple sclerosis – a condition that can affect the brain and spinal cord – often have impaired working memory, which can negatively affect their quality of life.

Traditionally, working memory has been evaluated using tests that determine whether a patient can recall an item or not. In this approach, an incorrect response implies a complete absence of information regarding the specific item, resulting in a binary evaluation. More recently, researchers have shown that the precision of the memories people recall degrades gradually as they are asked to remember more things and that focusing on an item negatively affects recall precision for other items. This implies that working memory is reorganised flexibly between memorised items, a so-called ‘resource model’.

Unlike previous research, which favoured a binary model, Motahharynia et al. used a resource model to study visual working memory impairment in multiple sclerosis. The study participants consisted of healthy volunteers and patients with two subtypes of multiple sclerosis. Each participant completed one of two different types of test. In one, they were shown targets for short periods of time and then asked to pinpoint their position after they disappeared. In the other, participants were asked to memorise the orientation and colour of consecutively presented bars.

The findings confirmed that multiple sclerosis patients had worse memory recall than people without the disease. However, computer modelling provided insights into the sources of error in working memory dysfunction, showing that the memory deficiency was due to imprecision in recalling information and ‘swap errors’, the phenomenon of mistakenly reporting the property of other memorised items. This rise in swap errors is likely due to an increase in unwanted signals, or noise, in the brains of multiple sclerosis patients.

Motahharynia et al. have presented a sensitive way of measuring working memory deficiency. Importantly, the measurements were able to distinguish between different stages of multiple sclerosis. This could help doctors detect disease progression earlier, allowing for more timely and effective treatment interventions. This method could also be useful in the development and testing of drugs for therapy.

Multiple sclerosis (MS) is a debilitating inflammatory disorder characterized by demyelinating central nervous system (CNS) plaques creating a progressive neurodegenerative state with heterogeneous clinical characteristics (Benedict et al., 2020; Dobson and Giovannoni, 2019). Impairment in cognitive function is a common clinical manifestation of MS, which detrimentally affects different aspects of patients’ daily life, from decreased physical performance and productivity to unemployment (Benedict et al., 2020; Clemens and Langdon, 2018). One of the frequently affected domains of cognition in MS is working memory (WM) (Vacchi et al., 2017), which, due to its essential role in several cognitive processes (Miller et al., 2018; Nee and D’Esposito, 2016), is one of the main areas of MS research (Costers et al., 2021; Covey et al., 2011; Hulst et al., 2017; Rocca et al., 2014; Vacchi et al., 2017).

Multiple neuropsychological cognitive paradigms, such as paced auditory serial addition test (PASAT), n-back, and delayed-match to sample, were developed to investigate different aspects of WM deficit in MS (Costers et al., 2021; Parmenter et al., 2006; Pourmohammadi et al., 2023; Rocca et al., 2014; Stojanovic-Radic et al., 2015; Vacchi et al., 2017). The basis of these change detection paradigms is the slot model of WM (Ma et al., 2014). In this quantized model, WM is considered a short-term storage for a limited number of items (Cowan, 2001; Ma et al., 2014; Miller, 1956), storing the information in a binary format. This assumption creates an all-or-none condition in which only the stored items in these limited slots will be remembered (Ma et al., 2014). Nonetheless, recent observations from analog recall paradigms assessing the precision of WM determined dynamic allocation of WM resources (Bays et al., 2009; Bays and Husain, 2008; Gorgoraptis et al., 2011; Schneegans and Bays, 2016). Each stored item in this framework possesses a fraction of WM storage in which the allocated space changes dynamically between them (Bays and Husain, 2008; Ma et al., 2014; Schneegans and Bays, 2016). This concept is the foundation for the resource-based model of WM.

The analog nature of inputs in resource-based model paradigms makes it possible to investigate the resolution and variability of stored memory (Peich et al., 2013; Zokaei et al., 2015). Also, assessing the distribution of error in these paradigms further helped in uncovering the underlying structure of the visual WM system (Liang et al., 2016; Lugtmeijer et al., 2021; McMaster et al., 2022; Peich et al., 2013; Zokaei et al., 2020). An analog recall task is a paradigm in which subjects need to simultaneously recall multiple features of items in a continuous space, hence requiring encoding the information of connected features in addition to their distinct value (e.g., object, location, and object-location binding information) (McMaster et al., 2022; Peich et al., 2013; Zokaei et al., 2020). According to the study of Bays et al., there are three different sources of error for recalling information in visual WM tasks with connected features (Bays et al., 2009). They are identified as (i) the Gaussian variability in response around the target value (target response proportion), (ii) Gaussian variability in response around the non-target value, that is, mistakenly reporting feature of other presented items (swap error), and (iii) random responses (uniform response proportion) (Bays et al., 2009; Zokaei et al., 2020).

Studies based on analog recall paradigms unraveled new insights into the sources of recall error in neurodegenerative disorders. It was determined that random response and swap error contribute to the impairment of visual WM in Parkinson’s and Alzheimer’s diseases, respectively (Liang et al., 2016; Zokaei et al., 2020). However, regardless of the influential impact of these study designs on the discovery of novel mechanistic insights into the organization of visual WM system, it has not gotten enough attention in the field of MS research.

This study followed our previous study in which the quantity of MS-related visual WM was assessed based on the slot model (Pourmohammadi et al., 2023). Here, we aimed to evaluate recall precision (quality) based on the resource-based model paradigms. In this regard, we developed two analog recall paradigms, a memory-guided localization (MGL) and two analog recall tasks with sequential bar presentation (3 bar and 1 bar). Primarily using the simplistic design of MGL, recall error (absolute error) and precision of visual WM were assessed. Similarly, recall error and precision were evaluated using the two designed analog recall paradigms with sequential presentation, that is, the low memory load, 1 bar, and high memory load, 3 bar conditions, respectively. Furthermore, the classifying performance of these paradigms in distinguishing different groups was assessed. Finally, the distribution of recall error in analog paradigms with sequential presentation was further investigated using the probabilistic model of Bays, Catalao, and Husain (Bays et al., 2009). In both MGL and sequential paradigms (low and high memory load conditions), recall error and precision were impaired in MS. The dissociable function of these paradigms in classifying MS subtypes (relapsing-remitting and secondary progressive) gave some clues about the underlying structure of WM deficit in progressive states of the disease. Investigation into sources of error in the high memory load condition revealed that target response proportion and swap error (non-target response proportion) contribute to visual WM dysfunction in MS.

In the MGL paradigm (Figure 1A), 45patients (19 relapsing-remitting MS [RRMS] and 26 secondary progressive MS [SPMS]) and 24 healthy controls participated. As mentioned in the ‘Materials and methods’ section, the mean data of five participants (one healthy control, three RRMS, and one SPMS) were excluded from further analysis. In the sequential paradigms (Figure 1B and C), from a total of 76patients (42 RRMS and 34 SPMS) and 49 healthy controls who participated, three healthy controls, three RRMS, and two SPMS were excluded. The demographic and clinical data of participants are summarized in Tables 1 and 2.

Figure 1

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Recall error in multiple sclerosis

In the MGL paradigm (Figure 1A), recall error was evaluated as a function of distance using a mixed-model ANOVA. Recall error was significantly different between groups (F(2,61) = 14.57, p<10^–5) and distances (F(2,61) = 85.03, p<10^–23, Figure 2A). A significant interaction was also observed between group and distance (F(4,61) = 7.24, p<10^–4). Tukey post hoc test determined that recall error was significantly higher in SPMS (1.86° ± 0.92° visual degree) compared to healthy control (0.97 ± 0.26, p<10^–4) and RRMS (1.09 ± 0.27, p<10^–3). No significant difference was detected between RRMS and healthy control (p=0.83). Similarly, recall error as a function of delay interval was also evaluated. Recall error was significantly different between delay intervals (F(4,61) = 18.89, p<10^–12, Figure 2D). No significant interaction was observed between group and delay interval [F(8,61) = 0.69, p=0.70]. After adjusting for cognitive ability, assessed by the Montreal Cognitive Assessment (MoCA) screening tool (cognitive performance was significantly different between groups), the effect of group on recall error remained significant (Supplementary file 1).

Figure 2

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Statistical reports corresponding to Figure 2.

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While reaction time (RT) was significantly different between groups (F(2,61) = 26.44, p<10^–8) and distances (F(2,61) = 25.94, p<10^–9, Figure 2C), it was not significantly different between delay intervals (F(4,61) = 0.97, p=0.43, Figure 2F). No significant interaction was observed between group and distance (F(4,61) = 2.06, p=0.09) or group and delay interval (F(8,61) = 0.86, p=0.55). The statistical results of RT are summarized in Supplementary file 2.

Recall error was evaluated for sequential tasks using the same method. In the ‘high memory load’ condition (i.e., sequential paradigm with 3 bar, Figure 1B), recall error was significantly different between groups (F(2,114) = 28.18, p<10^–9) and bar orders (F(2,114) = 48.74, p<10^–17, Figure 2G). No significant interaction was observed between them (F(4,114) = 1.21, p=0.31). Tukey post hoc test showed that recall error was significantly higher in RRMS (0.68 ± 0.20 radian) and SPMS (0.76 ± 0.17) compared to healthy control (0.48 ± 0.16, p<10^–5, p<10^–8, respectively). However, no significant difference was detected between RRMS and SPMS groups (p=0.14). After adjusting for gender, age, education, and cognitive ability (they were significantly different between groups), the group’s effect on recall error remained significant (Supplementary file 3). Moreover, while RT was also significantly different between groups (F(2,114) = 12.95, p<10^–5) and bar orders (F(2,114) = 5.92, p<0.004, Figure 2I), no significant interaction was observed between group and bar order (F(4,114) = 0.16, p=0.96). The statistical results of RT are summarized in Supplementary file 4.

In the ‘low memory load’ condition (i.e., 1 bar paradigm, Figure 1C), recall error was significantly different between groups (F(2,114) = 36.85, p<10^–12, Figure 2G). Tukey post hoc test showed that recall error was significantly higher in RRMS (0.33 ± 0.12 radian) and SPMS (0.43 ± 0.12) compared to healthy control (0.22 ± 0.08, p<10^–4, p<10^–8, respectively). We also observed a significant difference between RRMS and SPMS (p<10^–3). After adjusting for gender, age, education, and cognitive ability, the group’s effect on recall error remained significant (Supplementary file 5). Correspondingly, RT differed significantly between groups (F(2,114) = 12.59, p<10^–4, Figure 2I). The statistical results of RT are summarized in Supplementary file 4.

Distribution of error in recalling information in multiple sclerosis

The distribution of error in recalling information was assessed further to investigate the underlying mechanisms of WM impairment in MS. In this regard, the recorded data from the sequential paradigms was fitted to a probabilistic model developed by Bays et al., 2009. In line with the results of recall error and precision, for the high memory load condition, circular standard deviation (SD) of von Mises distribution of recall error was significantly different between groups (F(2,114) = 26.79, p<10^–9) and bar orders (F(2,114) = 14.95, p<10^–6, Figure 3A). At the same time, they had no significant interaction (F(4,114) = 1.19, p=0.31). von Mises SD of recall error was lower in healthy control (0.51 ± 0.12) than both RRMS (0.69 ± 0.20, p<10^–5) and SPMS (0.76 ± 0.15, p<10^–8). There was no difference between RRMS and SPMS in the von Mises SD parameter (p=0.16). In the low memory load condition, von Mises SD was affected by groups (F(2,114) = 33.07, p<10^–11, Figure 3A). Again, von Mises SD was lower in healthy control (0.25 ± 0.10) than both RRMS (0.38 ± 0.14, p<10^–5) and SPMS (0.47 ± 0.12, p<10^–8). There was a significant difference between RRMS and SPMS in von Mises SD (p<0.02). After adjusting for gender, age, education, and cognitive ability, the group’s effect on von Mises SD remained significant in both high and low memory load conditions (Supplementary file 3 and Supplementary file 5).

Figure 3 with 1 supplement see all

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Statistical reports corresponding to Figure 3 and Figure 3—figure supplement 1.

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According to the study by Bays et al., 2009, there are three sources of error for recalling information. The sources of these errors were defined as Gaussian variability in response around (i) target (target response proportion, Figure 3B) and (ii) non-target values (swap error, Figure 3C) and (iii) random responses (uniform response proportion, Figure 3D). In the high memory load condition, target response proportion was significantly different between groups (F(2,114) = 11.04, p<10^–4) and bar orders (F(2,114) = 10, p<10^–4, Figure 3B). No significant interaction was observed between group and bar order (F(4,114) = 0.43, p=0.78). Target proportion was higher in healthy control (0.88 ± 0.11) than both RRMS (0.79 ± 0.12, p<0.003) and SPMS (0.76 ± 0.14, p<10^–4). There was no significant difference in target proportion between RRMS and SPMS groups (p=0.54). After adjusting for gender, age, years of education, and cognitive ability, the effect of group on target proportion remained significant (Supplementary file 3). Moreover, the nearest-neighbor analysis was performed to further evaluate the effect of target proportion in the absence of swap error. Removing the effect of swap error allowed us to assess the isolated effect of orientation recall. The findings from the nearest-neighbor analysis showed a similar pattern of results (Figure 3—figure supplement 1). The isolated effect of orientation was significantly different between groups (F(2,114) = 29.26, p<10^–10) among different bar orders (F(2,114) = 7.07, p<0.002). No significant interaction was observed between group and bar order (F(4,114) = 0.58, p=0.67). After adjusting for gender, age, education, and cognitive ability, the results of nearest-neighbor analysis remained significant (Supplementary file 3).

In the low memory load condition, group significantly affected target response proportion (F(2,114) = 3.11, p<0.049, Figure 3B). While target proportion in the healthy group (0.98 ± 0.03) was significantly higher than SPMS patients (0.94 ± 0.09, p<0.04), after adjusting for gender, age, years of education, and cognitive ability, this effect became insignificant (Supplementary file 5). In addition, the target proportion did not significantly differ between healthy vs. RRMS (0.96 ± 0.08, p=0.48) and RRMS vs. SPMS population (p=0.37).

In line with the above findings, swap error was higher in the MS population. In the high memory load condition, swap error was significantly different between groups (F(2,114) = 7.11, p<0.002) and bar orders (F(2,114) = 31.05, p<10^–11, Figure 3C). No significant interaction was observed between group and bar order (F(4,114) = 1.45, p=0.22). Swap error was lower in healthy control (0.07 ± 0.06) than in both RRMS (0.11 ± 0.09, p<0.05) and SPMS patients (0.14 ± 0.09, p<0.002). There was no significant difference in swap error between RRMS and SPMS (p=0.41). After adjusting for gender, age, years of education, and cognitive ability, the group’s effect on swap error remained significant (Supplementary file 3). Moreover, while in the high memory load condition the uniform response proportion was different between groups (F(2,114) = 5.50, p<0.006, Figure 3D), no such differences were observed between bar orders (F(2,114) = 0.81, p=0.45) or the interaction between them (F(4,114) = 0.18, p=0.95). Post hoc analysis revealed that uniform proportion was lower in healthy control (0.05 ± 0.08) than both RRMS (0.09 ± 0.08, p<0.03) and SPMS (0.10 ± 0.08, p<0.02). Moreover, there was no significant difference in uniform response proportion between RRMS and SPMS groups (p=0.95). Additionally, although after adjusting for gender, the effect of group on uniform proportion remained significant, adding age, years of education, or cognitive ability made this effect insignificant (Supplementary file 3). The result of uniform response proportion in the low memory load condition is mathematically same as the target proportion (uniform proportion = 1 – target proportion, since there was no swap error in the 1 bar condition).

Dissociable function of MGL and sequential paradigms

The classifying ability of MGL and sequential paradigms in differentiating healthy control from MS patients was assessed based on recall error parameters using the receiver operating characteristic (ROC) analysis (Figure 4A–C). The accuracy of MGL and sequential paradigms with 3 bar and 1 bar in differentiating MS patients from healthy participants was 80% (Figure 4A), 83.4% (Figure 4B), and 86.2% (Figure 4C), respectively. A closer look at Figure 2A, D and G suggested that these paradigms differentiate MS and healthy control with distinct patterns. Hence, we separately applied ROC analysis to healthy control vs. RRMS, healthy control vs. SPMS, and RRMS vs. SPMS for MGL (Figure 4D) and sequential paradigms with 3 bar (Figure 4E) and 1 bar (Figure 4F). While the MGL paradigm had good performance in differentiating SPMS from healthy control (90.1%) and SPMS from RRMS (84.7%), it had poor ability in distinguishing healthy control from RRMS (64.1%, Figure 4D). Accordingly, although the 3 bar paradigm also had good accuracy in differentiating healthy control from SPMS (88.4%) and better results (compared to MGL) in discriminating healthy control from RRMS (79.3%), it did a poor job in discriminating MS subtypes (62%, Figure 4E). Complementary to the above findings, the 1 bar paradigm showed an in-between pattern of results. The 1 bar paradigm accurately discriminates healthy control from SPMS (94.4%), while it also performed well in differentiating healthy control from RRMS (79.6%). However, compared to MGL, it had a weaker ability to discriminate MS subtypes (72.3%, Figure 4F).

Figure 4

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Statistical reports corresponding to Figure 4.

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In this study, we investigated the visual WM deficits in MS using two continuous reproduction paradigms: MGL and analog recall paradigms with sequential presentation. Our results align with the previous reports regarding WM deficit in MS (Costers et al., 2021; Parmenter et al., 2006; Pourmohammadi et al., 2023; Stojanovic-Radic et al., 2015; Vacchi et al., 2017). Complementary to these reports, which envisaged a binary model for storing information (slot-based model), we assessed recall precision in WM using analog reproduction paradigms (resource-based model). Further, by utilizing the unique design of our sequential paradigm, which allows us to assess the distribution of error in recalling information, we introduced a new mechanistic insight into the visual WM dysfunction in MS.

Although the results from both MGL and sequential paradigms showed an overall decrease in recall precision and increased recall error in MS population, the post hoc analysis demonstrated inconsistent results. In the MGL paradigm, while SPMS patients performed worse than other groups, no significant difference was observed between healthy control and RRMS. This result contrasted with the sequential paradigm with 3 bar (high memory load condition), in which MS subtypes (RRMS vs. SPMS) were not significantly different, and healthy control performed better than RRMS and SPMS. The situation also varied for the 1 bar paradigm (low memory load condition), in which all three groups performed with different levels of precision. ROC analysis further confirmed these results, which determined a dissociation between the classifying performance of MGL and sequential paradigms. Additionally, although the low memory load condition was better than the high memory load condition in distinguishing MS subtypes, its classifying performance was not as good as the MGL paradigm. It seems that these paradigms evaluated distinct aspects of WM dysfunction in MS.

The dissociable function of our paradigms could arise from using different types of stimuli (location in MGL vs. orientation in sequential paradigms), in which spatial WM was assessed in the MGL paradigm. As the spatial WM process was associated with the function of the hippocampus (Rolls, 2018), the observed difference could indicate more hippocampal disruption in SPMS patients. This finding is in line with previous studies that showed more hippocampal regional loss (Sicotte et al., 2008) and increased hippocampus neuroinflammatory activity in SPMS compared to RRMS (Cree et al., 2021), suggesting that assessment of the spatial WM could be a specific marker for disorganization of the WM system in SPMS.

Another explanation is the long delay interval in MGL, which assessed the maintenance of information. Therefore, the observed difference could be due to additional impairment of SPMS patients in keeping that information. This finding is in line with our previous study, which showed that change detection paradigms with long delay intervals were promising in differentiating MS subtypes (Pourmohammadi et al., 2023). At the same time, one may debate that this difference was related to the longer stimulus presentation time in the MGL paradigm (1000 ms vs. 500 ms in the sequential paradigm). However, since the stimulus presentation time was adequate in sequential paradigms and the stimuli were presented consecutively, it did not seem that the inadequate time for encoding information was responsible for this difference (Peich et al., 2013; Zokaei et al., 2015).

Additionally, one may argue that the observed dissociation could be due to the extra binding process needed in the sequential paradigm with 3 bar. However, the results from the low memory load condition and our findings from the sequential paradigm nearest-neighbor analysis, which provided a proxy to assess the isolated effect of orientation, demonstrated a similar pattern of dissociation in the absence of a binding effect. Based on these findings, we concluded that the binding process was not responsible for the observed dissociation. Nevertheless, since the evaluated binding process was an intra-term association (i.e., conjunctive binding), we could not be assured that the same results would be reached for an inter-term association (i.e., relational binding) (Parra et al., 2015). This issue becomes more interesting when we realize that the relational binding function is mainly centered on the hippocampus (Liang et al., 2016; Parra et al., 2015), the structure we presumed was responsible for the observed dissociation in the MGL paradigm.

Finally, due to the diffuse pattern of involved brain areas in MS and evidence demonstrating that brain networks accounted for different WM processes, it is reasonable to assume that distinct WM-related networks, instead of a single region, were responsible for the observed dissociable patterns (Bastin et al., 2019; Dobson and Giovannoni, 2019; Figueroa-Vargas et al., 2020; Lugtmeijer et al., 2021; Vacchi et al., 2017).

We applied a probabilistic model to disentangle the error distribution in recalling information in the sequential paradigms to further investigate the underlying mechanism behind the visual WM dysfunction in MS. Our finding revealed that in addition to imprecision in decoding information, swap error (mistakenly reporting a non-target feature) contributed to WM dysfunction in individuals with MS. Swap error has been associated with various mechanisms, including the variability in cue feature dimension, cue-independent sources, and strategic guessing (McMaster et al., 2022). The recent study by McMaster et al. comprehensively investigated these hypotheses and determined that the variability in cue feature dimension could solely account for the swap error mechanism (McMaster et al., 2022). The neural correlates of swap error could be explained by the imprecision in decoding information in the cue feature dimension (i.e., color in this study) due to the noise in neural activity. Hence, a non-cued item may mistakenly be considered as the actual cue, leading to the reporting of the corresponding non-target feature as a response (Schneegans and Bays, 2017). Therefore, the swap error observed in this study can be attributed to the variability in the color dimension. The neural underpinning of this observation in MS population could be related to their impaired neural activity. Recent studies have demonstrated disturbed neural activity during WM tasks in MS (Costers et al., 2021; Figueroa-Vargas et al., 2020). For instance, Costers et al. showed a disturbed theta band oscillation, which plays a crucial role in WM encoding information, in the hippocampal area of MS population (Costers et al., 2021). While this evidence provides a primary insight into the possible underlying mechanisms of WM dysfunction in MS, future electrophysiological studies using the cued recall paradigms could further elucidate this matter.

Swap errors (misbinding errors) have been observed in various neurological disorders, including different types of Alzheimer’s disease (Cecchini et al., 2023; Della Sala et al., 2012; Liang et al., 2016; Zokaei et al., 2020), epileptic patients with temporal lobe lobectomy (Zokaei et al., 2019), and voltage-gated potassium channel complex antibody (VGKC-Ab) limbic encephalitis (Pertzov et al., 2013). From a neuroanatomical perspective, previous studies proposed that impairment in hippocampus and medial temporal lobe regions contributes to problems in relational binding (Della Sala et al., 2012; Liang et al., 2016; Pertzov et al., 2013; Zokaei et al., 2019). Furthermore, whereas regions related to conjunctive binding have yet to be better understood, occipital and parietal regions seem to be mainly related to conjunctive processing (Cecchini et al., 2023). Additionally, a recent study by Valdés Hernández and colleagues also demonstrated the possible role of globus pallidus in conjunctive binding (Valdés Hernández et al., 2020). On the other hand, a study by Vacchi et al. demonstrated less activation of the superior and inferior parietal lobule during an N-back fMRI task in individuals with MS (Vacchi et al., 2017). Moreover, globus pallidus was shown to be involved in MS (Fujiwara et al., 2017). Hence, considering the convergence of brain regions involved in conjunctive processing (assessed through the current sequential paradigm) with those shown to be affected in MS, the parietal regions and globus pallidus could be the candidate regions responsible for the observed impairment. Additionally, based on the evidence showing the involvement of hippocampal regions in MS (Rocca et al., 2018; Sicotte et al., 2008), we also expect to see relational processing impairment; however, this study’s design did not allow us to evaluate this condition. Further neuroimaging studies are needed to validate these findings. Eventually, the insignificant results from the low memory load condition suggested more impairment in visual WM under high memory load situations, which was not unexpected.

Despite these findings, our study had some limitations that should be addressed in future research. This study only assessed WM dysfunction using behavioral paradigms. Further structural and functional evaluations should be performed to confirm our suggested brain areas associated with conjunctive binding and spatial WM deficit in MS. Concurrent assessment of brain networks using fMRI and EEG or volumetric studies alongside behavioral paradigms could address this issue. Furthermore, although we hypothesized that relational binding could be a specific marker for the progressive state of the disease based on the more disruption of hippocampal-related areas in SPMS, this study’s design did not allow us to evaluate this assumption. Future studies assessing the source of WM deficit regarding relational binding could elucidate this statement. Additionally, although clinical and cognitive assessments were performed to mitigate the possible confounding effects of cognitive, visual, and motor impairments on the outcomes of the study, it cannot be concluded that no confounding effects occurred. In future studies, including a control condition matched to the experimental paradigm where only the memory components are removed could better clarify this issue. Finally, considering the aim of this study, which was to develop a practical apparatus for WM assessment in clinical settings, we did not use an eye tracker. Although we attempted to minimize the effect of eye movement in both paradigms by instructing the participants to fixate on a central fixation point, using an eye tracker is necessary to validate these findings further.

In summary, using the resource-based model paradigms, we demonstrated that recall error and precision were impaired in MS. We provided new insight regarding the progressive state of the disease by assessing the plausible mechanisms related to the dissociable behavior of MGL and sequential paradigms in classifying MS subtypes. Furthermore, applying a computational model capable of disentangling error distribution in recalling information allowed us to uncover new insight regarding WM deficit in the MS population. Our results determined that decreased signal-to-noise ratio and swap error were responsible for WM deficit in MS. Overall, this study provided a sensitive measure for assessing WM impairment and gave new insight into the organization of WM dysfunction in MS.

All data generated or analyzed during this study are included in the manuscript and supporting files; Source Data files have been provided for psychophysics data, Figures 2, 3, and 4, Figure 3-figure supplement 1, and Tables 1 and 2. The source codes for the analog report MATLAB toolbox (Bays Lab, 2020) are available here.

1. 1. Badrkhahan SZ
   2. Sikaroodi H
   3. Sharifi F
   4. Kouti L
   5. Noroozian M

   (2020) Validity and reliability of the Persian version of the Montreal Cognitive Assessment (MoCA-P) scale among subjects with Parkinson’s disease

   Applied Neuropsychology. Adult 27:431–439.

   https://doi.org/10.1080/23279095.2019.1565762

   • PubMed
   • Google Scholar
2. 1. Bastin C
   2. Besson G
   3. Simon J
   4. Delhaye E
   5. Geurten M
   6. Willems S
   7. Salmon E

   (2019) An integrative memory model of recollection and familiarity to understand memory deficits

   The Behavioral and Brain Sciences 42:e281.

   https://doi.org/10.1017/S0140525X19000621

   • PubMed
   • Google Scholar
3. 1. Bays PM
   2. Husain M

   (2008) Dynamic shifts of limited working memory resources in human vision

   Science 321:851–854.

   https://doi.org/10.1126/science.1158023

   • PubMed
   • Google Scholar
4. 1. Bays PM
   2. Catalao RFG
   3. Husain M

   (2009) The precision of visual working memory is set by allocation of a shared resource

   Journal of Vision 9:7.

   https://doi.org/10.1167/9.10.7

   • PubMed
   • Google Scholar
5. Software

   1. Bays Lab

   (2020) Analogue Report Toolbox

   Bayslab Computational Cognition Group.

   https://www.paulbays.com/toolbox/
6. 1. Benedict RHB
   2. Amato MP
   3. DeLuca J
   4. Geurts JJG

   (2020) Cognitive impairment in multiple sclerosis: clinical management, MRI, and therapeutic avenues

   The Lancet. Neurology 19:860–871.

   https://doi.org/10.1016/S1474-4422(20)30277-5

   • PubMed
   • Google Scholar
7. 1. Brainard DH

   (1997)

   The Psychophysics Toolbox

   Spatial Vision 10:433–436.

   • PubMed
   • Google Scholar
8. 1. Cecchini MA
   2. Parra MA
   3. Brazzelli M
   4. Logie RH
   5. Della Sala S

   (2023) Short-term memory conjunctive binding in Alzheimer’s disease: A systematic review and meta-analysis

   Neuropsychology 37:769–789.

   https://doi.org/10.1037/neu0000825

   • PubMed
   • Google Scholar
9. 1. Clemens L
   2. Langdon D

   (2018) How does cognition relate to employment in multiple sclerosis? A systematic review

   Multiple Sclerosis and Related Disorders 26:183–191.

   https://doi.org/10.1016/j.msard.2018.09.018

   • PubMed
   • Google Scholar
10. 1. Costers L
    2. Van Schependom J
    3. Laton J
    4. Baijot J
    5. Sjøgård M
    6. Wens V
    7. De Tiège X
    8. Goldman S
    9. D’Haeseleer M
    10. D’hooghe MB
    11. Woolrich M
    12. Nagels G

    (2021) The role of hippocampal theta oscillations in working memory impairment in multiple sclerosis

    Human Brain Mapping 42:1376–1390.

    https://doi.org/10.1002/hbm.25299

    • PubMed
    • Google Scholar
11. 1. Covey TJ
    2. Zivadinov R
    3. Shucard JL
    4. Shucard DW

    (2011) Information processing speed, neural efficiency, and working memory performance in multiple sclerosis: differential relationships with structural magnetic resonance imaging

    Journal of Clinical and Experimental Neuropsychology 33:1129–1145.

    https://doi.org/10.1080/13803395.2011.614597

    • PubMed
    • Google Scholar
12. 1. Cowan N

    (2001) The magical number 4 in short-term memory: A reconsideration of mental storage capacity

    The Behavioral and Brain Sciences 24:87–114.

    https://doi.org/10.1017/s0140525x01003922

    • PubMed
    • Google Scholar
13. 1. Cree BAC
    2. Arnold DL
    3. Chataway J
    4. Chitnis T
    5. Fox RJ
    6. Pozo Ramajo A
    7. Murphy N
    8. Lassmann H

    (2021) Secondary progressive multiple sclerosis: New insights

    Neurology 97:378–388.

    https://doi.org/10.1212/WNL.0000000000012323

    • PubMed
    • Google Scholar
14. 1. Della Sala S
    2. Parra MA
    3. Fabi K
    4. Luzzi S
    5. Abrahams S

    (2012) Short-term memory binding is impaired in AD but not in non-AD dementias

    Neuropsychologia 50:833–840.

    https://doi.org/10.1016/j.neuropsychologia.2012.01.018

    • PubMed
    • Google Scholar
15. 1. Dobson R
    2. Giovannoni G

    (2019) Multiple sclerosis - a review

    European Journal of Neurology 26:27–40.

    https://doi.org/10.1111/ene.13819

    • PubMed
    • Google Scholar
16. 1. Figueroa-Vargas A
    2. Cárcamo C
    3. Henríquez-Ch R
    4. Zamorano F
    5. Ciampi E
    6. Uribe-San-Martin R
    7. Vásquez M
    8. Aboitiz F
    9. Billeke P

    (2020) Frontoparietal connectivity correlates with working memory performance in multiple sclerosis

    Scientific Reports 10:9310.

    https://doi.org/10.1038/s41598-020-66279-0

    • PubMed
    • Google Scholar
17. Book

    1. Fisher NI

    (1993) Statistical Analysis of Circular Data

    Cambridge University Press.

    https://doi.org/10.1017/CBO9780511564345

    • Google Scholar
18. 1. Fujiwara E
    2. Kmech JA
    3. Cobzas D
    4. Sun H
    5. Seres P
    6. Blevins G
    7. Wilman AH

    (2017) Cognitive implications of deep gray matter iron in multiple sclerosis

    AJNR. American Journal of Neuroradiology 38:942–948.

    https://doi.org/10.3174/ajnr.A5109

    • PubMed
    • Google Scholar
19. 1. Goodkin DE
    2. Hertsgaard D
    3. Seminary J

    (1988)

    Upper extremity function in multiple sclerosis: improving assessment sensitivity with box-and-block and nine-hole peg tests

    Archives of Physical Medicine and Rehabilitation 69:850–854.

    • PubMed
    • Google Scholar
20. 1. Gorgoraptis N
    2. Catalao RFG
    3. Bays PM
    4. Husain M

    (2011) Dynamic updating of working memory resources for visual objects

    The Journal of Neuroscience 31:8502–8511.

    https://doi.org/10.1523/JNEUROSCI.0208-11.2011

    • PubMed
    • Google Scholar
21. 1. Hulst HE
    2. Goldschmidt T
    3. Nitsche MA
    4. de Wit SJ
    5. van den Heuvel OA
    6. Barkhof F
    7. Paulus W
    8. van der Werf YD
    9. Geurts JJG

    (2017) rTMS affects working memory performance, brain activation and functional connectivity in patients with multiple sclerosis

    Journal of Neurology, Neurosurgery & Psychiatry 88:386–394.

    https://doi.org/10.1136/jnnp-2016-314224

    • Google Scholar
22. 1. Liang Y
    2. Pertzov Y
    3. Nicholas JM
    4. Henley SMD
    5. Crutch S
    6. Woodward F
    7. Leung K
    8. Fox NC
    9. Husain M

    (2016) Visual short-term memory binding deficit in familial Alzheimer’s disease

    Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 78:150–164.

    https://doi.org/10.1016/j.cortex.2016.01.015

    • PubMed
    • Google Scholar
23. 1. Lugtmeijer S
    2. Schneegans S
    3. Lammers NA
    4. Geerligs L
    5. de Leeuw FE
    6. de Haan EHF
    7. Bays PM
    8. Kessels RPC

    (2021) Consequence of stroke for feature recall and binding in visual working memory

    Neurobiology of Learning and Memory 179:107387.

    https://doi.org/10.1016/j.nlm.2021.107387

    • PubMed
    • Google Scholar
24. 1. Ma WJ
    2. Husain M
    3. Bays PM

    (2014) Changing concepts of working memory

    Nature Neuroscience 17:347–356.

    https://doi.org/10.1038/nn.3655

    • PubMed
    • Google Scholar
25. 1. McMaster JMV
    2. Tomić I
    3. Schneegans S
    4. Bays PM

    (2022) Swap errors in visual working memory are fully explained by cue-feature variability

    Cognitive Psychology 137:101493.

    https://doi.org/10.1016/j.cogpsych.2022.101493

    • PubMed
    • Google Scholar
26. 1. Miller GA

    (1956)

    The magical number seven plus or minus two: some limits on our capacity for processing information

    Psychological Review 63:81–97.

    • PubMed
    • Google Scholar
27. 1. Miller EK
    2. Lundqvist M
    3. Bastos AM

    (2018) Working Memory 2.0

    Neuron 100:463–475.

    https://doi.org/10.1016/j.neuron.2018.09.023

    • PubMed
    • Google Scholar
28. 1. Nasreddine ZS
    2. Phillips NA
    3. Bédirian V
    4. Charbonneau S
    5. Whitehead V
    6. Collin I
    7. Cummings JL
    8. Chertkow H

    (2005) The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment

    Journal of the American Geriatrics Society 53:695–699.

    https://doi.org/10.1111/j.1532-5415.2005.53221.x

    • PubMed
    • Google Scholar
29. 1. Nee DE
    2. D’Esposito M

    (2016) The representational basis of working memory

    Current Topics in Behavioral Neurosciences pp. 213–230.

    https://doi.org/10.1007/7854_2016_456

    • Google Scholar
30. 1. Parmenter BA
    2. Shucard JL
    3. Benedict RHB
    4. Shucard DW

    (2006) Working memory deficits in multiple sclerosis: comparison between the n-back task and the Paced Auditory Serial Addition Test

    Journal of the International Neuropsychological Society 12:677–687.

    https://doi.org/10.1017/S1355617706060826

    • PubMed
    • Google Scholar
31. 1. Parra MA
    2. Fabi K
    3. Luzzi S
    4. Cubelli R
    5. Hernandez Valdez M
    6. Della Sala S

    (2015) Relational and conjunctive binding functions dissociate in short-term memory

    Neurocase 21:56–66.

    https://doi.org/10.1080/13554794.2013.860177

    • PubMed
    • Google Scholar
32. 1. Peich MC
    2. Husain M
    3. Bays PM

    (2013) Age-related decline of precision and binding in visual working memory

    Psychology and Aging 28:729–743.

    https://doi.org/10.1037/a0033236

    • PubMed
    • Google Scholar
33. 1. Pelli DG

    (1997)

    The VideoToolbox software for visual psychophysics: transforming numbers into movies

    Spatial Vision 10:437–442.

    • PubMed
    • Google Scholar
34. 1. Pertzov Y
    2. Miller TD
    3. Gorgoraptis N
    4. Caine D
    5. Schott JM
    6. Butler C
    7. Husain M

    (2013) Binding deficits in memory following medial temporal lobe damage in patients with voltage-gated potassium channel complex antibody-associated limbic encephalitis

    Brain 136:2474–2485.

    https://doi.org/10.1093/brain/awt129

    • PubMed
    • Google Scholar
35. 1. Pourmohammadi A
    2. Motahharynia A
    3. Shaygannejad V
    4. Ashtari F
    5. Adibi I
    6. Sanayei M

    (2023) Working memory dysfunction differs between secondary progressive and relapsing multiple sclerosis: Effects of clinical phenotype, age, disease duration, and disability

    Multiple Sclerosis and Related Disorders 69:104411.

    https://doi.org/10.1016/j.msard.2022.104411

    • PubMed
    • Google Scholar
36. 1. Rocca MA
    2. Valsasina P
    3. Hulst HE
    4. Abdel-Aziz K
    5. Enzinger C
    6. Gallo A
    7. Pareto D
    8. Riccitelli G
    9. Muhlert N
    10. Ciccarelli O
    11. Barkhof F
    12. Fazekas F
    13. Tedeschi G
    14. Arévalo MJ
    15. Filippi M
    16. MAGNIMS fMRI Study Group

    (2014) Functional correlates of cognitive dysfunction in multiple sclerosis: A multicenter fMRI Study

    Human Brain Mapping 35:5799–5814.

    https://doi.org/10.1002/hbm.22586

    • PubMed
    • Google Scholar
37. 1. Rocca MA
    2. Barkhof F
    3. De Luca J
    4. Frisén J
    5. Geurts JJG
    6. Hulst HE
    7. Sastre-Garriga J
    8. Filippi M
    9. MAGNIMS Study Group

    (2018) The hippocampus in multiple sclerosis

    The Lancet. Neurology 17:918–926.

    https://doi.org/10.1016/S1474-4422(18)30309-0

    • PubMed
    • Google Scholar
38. 1. Rolls ET

    (2018) The storage and recall of memories in the hippocampo-cortical system

    Cell and Tissue Research 373:577–604.

    https://doi.org/10.1007/s00441-017-2744-3

    • PubMed
    • Google Scholar
39. 1. Schneegans S
    2. Bays PM

    (2016) No fixed item limit in visuospatial working memory

    Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 83:181–193.

    https://doi.org/10.1016/j.cortex.2016.07.021

    • PubMed
    • Google Scholar
40. 1. Schneegans S
    2. Bays PM

    (2017) Neural architecture for feature binding in visual working memory

    The Journal of Neuroscience 37:3913–3925.

    https://doi.org/10.1523/JNEUROSCI.3493-16.2017

    • PubMed
    • Google Scholar
41. 1. Sicotte NL
    2. Kern KC
    3. Giesser BS
    4. Arshanapalli A
    5. Schultz A
    6. Montag M
    7. Wang H
    8. Bookheimer SY

    (2008) Regional hippocampal atrophy in multiple sclerosis

    Brain 131:1134–1141.

    https://doi.org/10.1093/brain/awn030

    • PubMed
    • Google Scholar
42. 1. Stojanovic-Radic J
    2. Wylie G
    3. Voelbel G
    4. Chiaravalloti N
    5. DeLuca J

    (2015) Neuroimaging and cognition using functional near infrared spectroscopy (fNIRS) in multiple sclerosis

    Brain Imaging and Behavior 9:302–311.

    https://doi.org/10.1007/s11682-014-9307-y

    • PubMed
    • Google Scholar
43. 1. Thompson AJ
    2. Banwell BL
    3. Barkhof F
    4. Carroll WM
    5. Coetzee T
    6. Comi G
    7. Correale J
    8. Fazekas F
    9. Filippi M
    10. Freedman MS
    11. Fujihara K
    12. Galetta SL
    13. Hartung HP
    14. Kappos L
    15. Lublin FD
    16. Marrie RA
    17. Miller AE
    18. Miller DH
    19. Montalban X
    20. Mowry EM
    21. Sorensen PS
    22. Tintoré M
    23. Traboulsee AL
    24. Trojano M
    25. Uitdehaag BMJ
    26. Vukusic S
    27. Waubant E
    28. Weinshenker BG
    29. Reingold SC
    30. Cohen JA

    (2018) Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria

    The Lancet. Neurology 17:162–173.

    https://doi.org/10.1016/S1474-4422(17)30470-2

    • PubMed
    • Google Scholar
44. 1. Vacchi L
    2. Rocca MA
    3. Meani A
    4. Rodegher M
    5. Martinelli V
    6. Comi G
    7. Falini A
    8. Filippi M

    (2017) Working memory network dysfunction in relapse-onset multiple sclerosis phenotypes: A clinical-imaging evaluation

    Multiple Sclerosis 23:577–587.

    https://doi.org/10.1177/1352458516656809

    • PubMed
    • Google Scholar
45. 1. Valdés Hernández MC
    2. Clark R
    3. Wang SH
    4. Guazzo F
    5. Calia C
    6. Pattan V
    7. Starr J
    8. Della Sala S
    9. Parra MA

    (2020) The striatum, the hippocampus, and short-term memory binding: Volumetric analysis of the subcortical grey matter’s role in mild cognitive impairment

    NeuroImage. Clinical 25:102158.

    https://doi.org/10.1016/j.nicl.2019.102158

    • PubMed
    • Google Scholar
46. 1. World Medical Association

    (2013) World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects

    JAMA 310:2191–2194.

    https://doi.org/10.1001/jama.2013.281053

    • PubMed
    • Google Scholar
47. 1. Zokaei N
    2. Burnett Heyes S
    3. Gorgoraptis N
    4. Budhdeo S
    5. Husain M

    (2015) Working memory recall precision is a more sensitive index than span

    Journal of Neuropsychology 9:319–329.

    https://doi.org/10.1111/jnp.12052

    • PubMed
    • Google Scholar
48. 1. Zokaei N
    2. Nour MM
    3. Sillence A
    4. Drew D
    5. Adcock J
    6. Stacey R
    7. Voets N
    8. Sen A
    9. Husain M

    (2019) Binding deficits in visual short-term memory in patients with temporal lobe lobectomy

    Hippocampus 29:63–67.

    https://doi.org/10.1002/hipo.22998

    • PubMed
    • Google Scholar
49. 1. Zokaei N
    2. Sillence A
    3. Kienast A
    4. Drew D
    5. Plant O
    6. Slavkova E
    7. Manohar SG
    8. Husain M

    (2020) Different patterns of short-term memory deficit in Alzheimer’s disease, Parkinson’s disease and subjective cognitive impairment

    Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 132:41–50.

    https://doi.org/10.1016/j.cortex.2020.06.016

    • PubMed
    • Google Scholar

Author details

1. Ali Motahharynia

   1. Center for Translational Neuroscience, Isfahan University of Medical Sciences, Isfahan, Islamic Republic of Iran
   2. Isfahan Neuroscience Research Center, Isfahan University of Medical Sciences, Isfahan, Islamic Republic of Iran

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Ahmad Pourmohammadi

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1140-3257

2. Ahmad Pourmohammadi

   1. Center for Translational Neuroscience, Isfahan University of Medical Sciences, Isfahan, Islamic Republic of Iran
   2. Isfahan Neuroscience Research Center, Isfahan University of Medical Sciences, Isfahan, Islamic Republic of Iran
   3. School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Islamic Republic of Iran

   Contribution

   Conceptualization, Data curation, Software, Investigation, Methodology, Writing – review and editing

   Contributed equally with

   Ali Motahharynia

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4996-0569

3. Armin Adibi

   1. Center for Translational Neuroscience, Isfahan University of Medical Sciences, Isfahan, Islamic Republic of Iran
   2. Isfahan Neuroscience Research Center, Isfahan University of Medical Sciences, Isfahan, Islamic Republic of Iran

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

4. Vahid Shaygannejad

   1. Center for Translational Neuroscience, Isfahan University of Medical Sciences, Isfahan, Islamic Republic of Iran
   2. Isfahan Neuroscience Research Center, Isfahan University of Medical Sciences, Isfahan, Islamic Republic of Iran
   3. Department of Neurology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Islamic Republic of Iran

   Contribution

   Resources, Funding acquisition, Writing – review and editing

   Competing interests

   No competing interests declared

5. Fereshteh Ashtari

   1. Center for Translational Neuroscience, Isfahan University of Medical Sciences, Isfahan, Islamic Republic of Iran
   2. Isfahan Neuroscience Research Center, Isfahan University of Medical Sciences, Isfahan, Islamic Republic of Iran
   3. Department of Neurology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Islamic Republic of Iran

   Contribution

   Resources, Funding acquisition, Writing – review and editing

   Competing interests

   No competing interests declared

6. Iman Adibi

   1. Center for Translational Neuroscience, Isfahan University of Medical Sciences, Isfahan, Islamic Republic of Iran
   2. Isfahan Neuroscience Research Center, Isfahan University of Medical Sciences, Isfahan, Islamic Republic of Iran
   3. Department of Neurology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Islamic Republic of Iran

   Contribution

   Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Methodology, Writing – review and editing

   For correspondence

   i.adibi@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4409-9404

7. Mehdi Sanayei

   1. Center for Translational Neuroscience, Isfahan University of Medical Sciences, Isfahan, Islamic Republic of Iran
   2. Isfahan Neuroscience Research Center, Isfahan University of Medical Sciences, Isfahan, Islamic Republic of Iran
   3. School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Islamic Republic of Iran

   Contribution

   Conceptualization, Software, Formal analysis, Supervision, Methodology, Writing – review and editing

   For correspondence

   mehdi.sanayei@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3593-8018

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