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. 2023 Nov 8:12:RP87442.
doi: 10.7554/eLife.87442.

A mechanistic insight into sources of error of visual working memory in multiple sclerosis

Ali Motahharynia #  1   2 , Ahmad Pourmohammadi #  1   2   3 , Armin Adibi  1   2 , Vahid Shaygannejad  1   2   4 , Fereshteh Ashtari  1   2   4 , Iman Adibi  1   2   4 , Mehdi Sanayei  1   2   3
Affiliations

A mechanistic insight into sources of error of visual working memory in multiple sclerosis

Ali Motahharynia et al. Elife. .

Abstract

Working memory (WM) is one of the most affected cognitive domains in multiple sclerosis (MS), which is mainly studied by the previously established binary model for information storage (slot model). However, recent observations based on the continuous reproduction paradigms have shown that assuming dynamic allocation of WM resources (resource model) instead of the binary hypothesis will give more accurate predictions in WM assessment. Moreover, continuous reproduction paradigms allow for assessing the distribution of error in recalling information, providing new insights into the organization of the WM system. Hence, by utilizing two continuous reproduction paradigms, memory-guided localization (MGL) and analog recall task with sequential presentation, we investigated WM dysfunction in MS. Our results demonstrated an overall increase in recall error and decreased recall precision in MS. While sequential paradigms were better in distinguishing healthy control from relapsing-remitting MS, MGL were more accurate in discriminating MS subtypes (relapsing-remitting from secondary progressive), providing evidence about the underlying mechanisms of WM deficit in progressive states of the disease. Furthermore, computational modeling of the results from the sequential paradigm determined that imprecision in decoding information and swap error (mistakenly reporting the feature of other presented items) was responsible for WM dysfunction in MS. Overall, this study offered a sensitive measure for assessing WM deficit and provided new insight into the organization of the WM system in MS population.

Keywords: binding; human; multiple sclerosis; neuroscience; resource model; swap error; working memory.

Plain language summary

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.

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Conflict of interest statement

AM, AP, AA, VS, FA, IA, MS No competing interests declared

Figures

Figure 1.
Figure 1.. Schematic design of visual working memory (WM) paradigms.
(A) In the memory-guided localization (MGL) paradigm, participants were asked to memorize and then localize the position of the target circle following a random delay interval of 0.5, 1, 2, 4, or 8 s. Following their response, visual feedback was presented. (B) In the sequential paradigm with 3 bar (high memory load condition), a sequence of three colored bars was presented consecutively. Participants were asked to match the orientation of the probe bar to the previously presented bar with the same color. Visual feedback was displayed following their response. (C) The 1 bar paradigm (low memory load condition) has the same structure as the 3 bar paradigm except for presenting one bar instead of three.
Figure 2.
Figure 2.. Recall error and precision of healthy control and multiple sclerosis (MS) subtypes (relapsing-remitting [RRMS] and secondary progressive [SPMS]) in visual working memory (WM) paradigms.
(A) Recall error, (B) recall precision, and (C) reaction time as a function of distance for the memory-guided localization (MGL) paradigm. (D–F) The same as a function of delay interval. (G) Recall error, (H) recall precision, and (I) reaction time as a function of bar order in the sequential paradigms with 3 bar (left of each subplot) and 1 bar (right of each subplot). Data are represented as mean ± SEM.
Figure 3.
Figure 3.. Sources of recall error in high and low memory load conditions (3 bar and 1 bar, respectively).
(A) von Mises SD (circular standard deviation of von Mises distribution), (B) Target response (probability of response around the target value), (C) swap error (probability of response around the non-target values), and (D) uniform response (probability of random response) for healthy control and multiple sclerosis (MS) subtypes in the sequential paradigms with 3 bar (left of each subplot) and 1 bar (right of each subplot). Data are represented as mean ± SEM.
Figure 3—figure supplement 1.
Figure 3—figure supplement 1.. Isolated effect of orientation in the high and low memory load conditions.
The nearest-neighbor analysis determined the isolated effect of orientation for healthy control, relapsing-remitting multiple sclerosis (RRMS), and secondary progressive multiple sclerosis (SPMS) in the high memory load condition (left of each subplot). The effect of orientation for the same groups in the low memory load condition (right of each subplot).
Figure 4.
Figure 4.. Classifying performance of visual working memory (WM) paradigms in differentiating healthy control from multiple sclerosis (MS) and MS subtypes, and MS subtypes from each other.
Receiver operating characteristic (ROC) curve demonstrated the accuracy of (A) memory-guided localization (MGL) and sequential paradigms with (B) 3 bar and (C) 1 bar in distinguishing healthy control from MS patients. The precision of these paradigms in dissociating healthy control from MS subtypes (relapsing-remitting MS [RRMS] and secondary progressive MS [SPMS]) and MS subtypes from each other is represented as the area under the curve (AUC) for (D) MGL and sequential paradigms with (E) 3 bar and (F) 1 bar.
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Update of

  • doi: 10.1101/2023.02.20.529229
  • doi: 10.7554/eLife.87442.1
  • doi: 10.7554/eLife.87442.2

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