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. 2020 Sep 28;15(9):e0239577. doi: 10.1371/journal.pone.0239577

Functional decline of the precuneus associated with mild cognitive impairment: Magnetoencephalographic observations

Koichi Yokosawa 1,*, Keisuke Kimura 2, Ryoken Takase 2, Yui Murakami 1,3, Jared Boasen 1,4
Editor: Stephen D Ginsberg5
PMCID: PMC7521706  PMID: 32986743

Abstract

Mild Cognitive Impairment (MCI) is a border or precursor state of dementia. To optimize implemented interventions for MCI, it is essential to clarify the underlying neural mechanisms. However, knowledge regarding the brain regions responsible for MCI is still limited. Here, we implemented the Montreal Cognitive Assessment (MoCA) test, a screening tool for MCI, in 20 healthy elderly participants (mean age, 67.5 years), and then recorded magnetoencephalograms (MEG) while they performed a visual sequential memory task. In the task, each participant memorized the four possible directions of seven sequentially presented arrow images. Recall accuracy for beginning items of the memory sequence was significantly positively related with MoCA score. Meanwhile, MEG revealed stronger alpha-band (8–13 Hz) rhythm desynchronization bilaterally in the precuneus (PCu) for higher MoCA (normal) participants. Most importantly, this PCu desynchronization response weakened in correspondence with lower MoCA score during the beginning of sequential memory encoding, a time period that should rely on working memory and be affected by declined cognitive function. Our results suggest that deactivation of the PCu is associated with early MCI, and corroborate pathophysiological findings based on post-mortem tissue which have implicated hypoperfusion of the PCu in early stages of Alzheimer disease. Our results indicate the possibility that cognitive decline can be detected early and non-invasively by monitoring PCu activity with electrophysiological methods.

Introduction

Age-related cognitive decline and associated diseases such as dementia are problems with high individual and social costs. Particularly in Japan’s super-aged society, early detection and rapid intervention against these diseases are extremely important. Currently, detection of a border or precursor state of dementia called, Mild Cognitive Impairment (MCI), can be done behaviorally using a validated screening assessment called the Montreal Cognitive Assessment (MoCA). An understanding of the brain regions and activities involved in MCI and age-based cognitive decline could lead to earlier detection. However, scientific knowledge in this regard remains limited.

One of the cognitive functions known to decline with age is working memory [1]. Memorizing items in a sequence requires working memory, and thus constitutes a way to test its function. In sequential memory tasks, memory performance is higher for memory items presented at the beginning and ending of a sequence, and conversely lower for items presented in the middle of the sequence. This phenomenon is known as serial position effect [2]. The higher memory performance for the beginning items (primacy effect) is attributed to rehearsal effects generated by the phonological loop, a key function in working memory [3, 4]. Accordingly, memory performance for beginning items in a sequence should be affected by aging. Meanwhile, the higher performance for ending items (recency effect) is attributed to immediate memory, deficits to which are not generally associated with aging. Thus, sequential memory tasks are arguably a suitable means to study the effect of aging because primacy and recency effects can be compared.

Because of their high temporal resolutions, electro-physiological methods such as electroencephalography (EEG) or magnetoencephalography (MEG), have been used to record dynamic brain activities during memory processing. Of the numerous spontaneous brain rhythms in various frequency bands that have been used to investigate memory processing, alpha-band rhythm (8–13 Hz; alpha-rhythm, hereafter) is advantageous in that it has a large amplitude and wide modulation. There is considerable evidence pointing to its involvement in short-term memory and working memory processing [5–21]. For example, numerous studies have indicated parametrical enhancement of occipital alpha-rhythm with increasing memory load during memory maintenance; the enhancement is usually explained as active inhibition of task-irrelevant visual inputs [8–11, 13, 15, 22–25]. Additionally, a relationship between alpha-rhythm amplitude during encoding and memory performance has also been observed [14, 16, 19–21]. Meanwhile, the interpretation that alpha-rhythm desynchronization reflects task-directed engagement of the brain region from which it originates is also well known [26–28].

In the present study, 20 healthy elderly participants (mean age, 67.5 years) were given the MoCA test, due to its sensitivity to MCI. They were categorized into either a high or low scoring group based on MoCA criteria. Magnetoencephalograms were recorded while participants performed a sequential memory task. Brain regions in which alpha-rhythm amplitude differed between groups were identified. Subsequent regression analyses revealed that alpha-rhythm amplitude originating from an identified region was significantly related to MoCA score, indicating the relevance of this brain region to cognitive functional decline.

Materials and methods

The MEG recordings were approved by the Ethics Committees of Faculty of Health Sciences, Hokkaido University. Written informed consent was obtained from each participant prior to the experiments.

Participants

Twenty physically healthy men (67.5±4.0 years old (mean±SD), all right-handed) participated in the experiment. They were recruited via an employment service center for independently living elderly people in Sapporo, Japan.

All participants were given the MoCA-J test, a validated Japanese language version of the MoCA test (MoCA test hereafter). One researcher conducted the MoCA test for all participants, receiving guidance from a speech therapist. In accordance with MoCA scoring instructions, one point was added for participants who had 12 or fewer years of formal education, and a score of 26 or above was considered normal. Participants were placed into two groups: those that scored normally (High group, n = 7), and those with below normal scores (Low group, n = 13).

Device and procedure

The MEG device was a 101-channel helmet-shaped magnetometer system (customized; Elekta-Neuromag Oy, Helsinki, Finland) installed at Hokkaido University, Sapporo, Japan. The passband was from 0.03 to 200 Hz, and the MEG signals were sampled at 600 Hz. The visual stimuli were projected by a liquid-crystal projector located outside the shielded room onto the back of a projection screen located within in the magnetically shielded room. All images were presented with visual angles of 2.2°–2.8° (i.e. centered in the participants’ visual field). All stimuli were gray in color, and presented on a black background with low brightness in order to suppress transient brain responses, as this study was focused on brain rhythm. Of note, all participants who required vision correction were provided with MEG compatible glasses fitted with lenses that matched their required correction factors.

Fig 1 shows the time sequence of one epoch of visual stimuli used in the experiment. Each epoch began with the presentation of a start cue for 0.5 s in the form of a cross-shaped fixation target. Then seven directional arrows pointing either left, right, up or down, were presented sequentially as memory items on the right side of the fixation target. Their duration of presentation was 0.1 s with 0.5 s intervals between them. One and half seconds after the final seventh arrow, a numerical recall cue was presented, numbered from 1 to 7 (e.g. “2” in Fig 1). Participants memorized the directions of the seven arrows in order, and answered the direction of the arrow corresponding to the recall cue numeral by pressing one of four directional buttons on a keypad (e.g. the subject should press “right” in the example shown in Fig 1) with their right index finger as soon as possible. This button press caused the recall cue to vanish, and an inter epoch interval began which was randomized between 3 to 4 s. The pressed button, response accuracy, and response time (which is duration between the onset of the recall cue and the moment of button press) were recorded automatically. A box was placed over the keypad and the right hand of the subject in order to screen them from view and prevent brain responses caused by eye saccades targeting hand movement during the button press. The experimental procedure has been described in our previous reports [19, 20].

Fig 1. An example epoch of the sequential memory task.

Fig 1

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Each subject memorized the seven directional arrows pointing either left, right, up or down with 600 ms intervals in between each, and answered the direction of the arrow corresponding to the numeral of the recall cue. In this example, the correct answer is right (direction of the second arrow).

Each epoch was divided into three time periods: baseline period (-1–0 s), memory item presentation period (MIP) (0–4.2 s), holding period (4.2–5.2 s), and recall period (5.2 s–), where 0 s marks the appearance of the first memory item (arrow). As previously mentioned, recall accuracy rate for items presented at the beginning and end of a sequence is higher than that for items presented in the middle of a sequence (serial position effect), indicating that underlying memory processes are different according to when they occur during the MIP. Therefore, the MIP period was further divided into the following sub-periods: beginning (0–1.2 s), middle (1.2–3.0 s), and end (3.0–4.2 s).

The directions of the arrows (left/right/up/down) and numeral of the recall cue (1–7) were randomized. The MEG recording for each participant comprised 140 test epochs, meaning that each recall cue numeral appeared 20 times in average. Additionally, 70 control epochs were also included. Thus, each experiment comprised a total of 210 epochs which were divided into 3 sessions with short breaks in between.

In the control epochs, all seven arrows had the same direction. The color (red/green) of the start cue indicated to the participants whether the epoch was test or control. The color (red/green) was counter balanced among participants. In the present study, the MEG data from the control epochs were not used.

Memory performance

The recall accuracy of each participant was calculated for each recall cue numeral. These values were then averaged for the numerals corresponding to each MIP sub-period: beginning (1 and 2), middle (3–5), and end (6 and 7). Note that although MoCA scores were originally designed for MCI screening, we did not attempt to diagnose any participants. Rather, for the purposes of the present study, MoCA score was used purely as an analytical parameter.

MEG signal processing

MEG signals were processed by using Brainstorm software (https://neuroimage.usc.edu/brainstorm/Introduction). Physiological artifacts and periodic noise were isolated and removed from the MEG signals using independent component analysis. The signals were then band pass filtered from 1–40 Hz, and epoched at -2–7 s relative to the appearance of the first memory item. Cortical current dipoles, which are the sources of MEG signals, were calculated for each epoch using minimum-norm estimation on the surface of a template brain. The surface of the template brain was decomposed to 15002 vertices and current dipoles were estimated on each vertex without orientation constraints. The MEG source signals were time-frequency decomposed into the alpha frequency band (8–13 Hz), upon which Hilbert transform was applied, providing amplitude envelopes of the alpha-rhythm of each current dipole for all 15002 vertices across the time series of all epochs. This source-level alpha-rhythm amplitude time series was averaged across all epochs for each subject, and then standardized as a percent deviation from baseline based on the equation χd = ((χ-μ)/ μ)×100, where μ denotes the averaged amplitude of the baseline period (-1–0 s), and χ denotes alpha-rhythm of each time point in the time series. Standardized alpha-rhythm amplitude was then averaged over the MIP period (0–4.2 s), and the holding period (4.2–5.2 s) for use in comparisons of standardized cortical alpha-rhythm amplitude between High and Low groups.

Time-course of alpha-rhythm amplitude

As described in the results section, the vertices with significantly different standardized alpha-rhythm amplitude between groups were concentrated bilaterally in the precuneus (PCu), based on the boundaries in the Desikan-Killiany cortical atlas. Therefore, standardized source-level alpha-rhythm amplitude was averaged across all vertices in the left and right PCu, and then averaged over the beginning (0–1.2 s), middle (1.2–3.0 s), and end (3.0–4.2 s) MIP sub-periods in each subject. These extracted mean activities for each MIP sub-period were used in multiple linear regression as described below.

Statistical analyses

The difference in age, and response time, between the High and Low MoCA score groups was separately compared via two-tailed independent t test with SPSS (IBM). The difference in standardized alpha-rhythm amplitude at each vertex for the MIP and holding periods were analyzed by double-tailed t-tests between High and Low groups using Brainstorm. The statistical significance threshold was set at p < 0.05. The vertices with significant differences were mapped on the surface of the template brain.

The relationship of MoCA score to memory performance was analyzed by regressing MoCA score against mean recall accuracies in each MIP sub-period (beginning, middle, end) using repeated measure linear regression. Meanwhile, the relationship of MoCA score and memory performance to bilateral PCu brain activity was analyzed by independently regressing MoCA score, and overall accuracy against alpha-rhythm amplitude in each MIP sub-period (beginning, middle, end) using repeated measures linear regression. In the case of significant interactions in the multiple regression analyses, pairwise comparisons are further reported. The significance threshold for interactions was set at p ≤ .01, and that for main factors and pairwise comparisons was set at p ≤ .05. All regression analyses were performed with SPSS (IBM).

Results

There were no significant differences in age between the High and Low groups (65.4 ± 2.9 years old (mean ± SD) vs. 68.6 ± 4.4 years old (mean ± SD), respectively; p = .104). In both groups, recall accuracy corresponding to memory items presented in the beginning and end MIP sub-periods were high, while middle sub-period accuracy was low, showing a clear serial position effect (Fig 2). However, no significant difference was obtained in the response times between those of the High and Low groups (1.57 ± 0.09 s (mean ± SD) vs. 1.49 ± 0.36 s (mean ± SD), respectively; p = .588).

Fig 2. Recall accuracies against the order of presentation in the memory sequence.

Fig 2

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Recall accuracy is high for numerals corresponding to arrows in the beginning (first and second) and end (sixth and seventh) sub-periods, while low for those corresponding to the middle (third–fifth) sub-period. Recall accuracy appears visibly different between High and Low MoCA scoring groups. See the statistical results in Fig 3. Error bar: S.D.

The repeated measures linear regression analysis of the relationship between MoCA score and memory performance revealed a significant effect of MoCA score (F(1, 18) = 5.276, p = .034), and a significant interaction between MoCA score and MIP sub-period on recall accuracy (F(2, 36) = 2.705, p = .08). Pairwise comparisons further revealed that MoCA score had a positive relationship with recall accuracy across all three MIP sub-periods, reaching significance in the beginning sub-period (β = 3.483, p = .008; β = 1.071, p = .112; β = .965, p = .459; for the beginning, middle, and ending sub-periods, respectively). See Fig 3 for plots of MoCA score against recall accuracy in each sub-period.

Fig 3. Relationships between MoCA score and recall accuracy.

Fig 3

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Repeated measures linear regression analysis demonstrated that MoCA score correlated only with the recall accuracy for numerals corresponding to arrows in the beginning sub-period. (n = 20; some data are overlapped each other).

Fig 4 shows cortical surface maps highlighting the vertices in which alpha-rhythm amplitude was significantly lower in the High group compared to the Low group. The vertices were densely clustered bilaterally in the region of the PCu (Fig 4 left below) for both the MIP (Fig 4(A)) and holding periods (Fig 4(B)). The bilateral PCu alpha-rhythm amplitude differences are clearly visible in the time course plot shown in Fig 5, which also reveals a sustained desynchronization from baseline in the High group that was not present in the Low group. Thus the bilateral PCu was chosen as a target area for linear regression analyses of alpha-rhythm amplitude.

Fig 4. Brain regions in which standardized alpha-rhythm amplitude was significantly lower in the High group compared to the Low group.

Fig 4

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Amplitude modulations during the memory item presentation period ((a): 0–4.2 s) and to the hold period ((b): 4.2–5.2 s) were compared between groups by double-tailed t-test (p < 0.05). The predominant region coincided well with the precuneus (PCu) based on the Desikan-Killiany atlas (Left below). A: anterior, P: posterior, L: left, R: right.

Fig 5. Time courses of standardized alpha-rhythm amplitude originating from the left and right PCu.

Fig 5

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Signals are expressed as a percent deviation from baseline (-1–0 s). Time courses of the mean values and standard errors across subjects are shown. Alpha-rhythm amplitude desynchronized markedly during encoding in the High group, but not in the Low group. Laterality (Left/Right) was not markedly different.

The repeated measures linear regression analysis of the relationship between MoCA score and bilateral PCu alpha-rhythm amplitude revealed a significant effect of sub-period (F(2, 17) = 3.864, p = .041), indicating that relationship to alpha-rhythm amplitude differed according to sub-period. There was also a significant effect of MoCA score (F(1, 18) = 4.870, p = .041), indicating a relationship between MoCA score and alpha-rhythm amplitude regardless of sub-period. Additionally, there was a significant interaction between MoCA score and sub-period on alpha-rhythm amplitude (F(2, 17) = 3.574, p = .051). Pairwise comparisons revealed that the relationship of MoCA score to alpha-rhythm amplitude was negative across all sub-periods, and significantly negative in the beginning and middle sub-periods (β = -1.445, p = .015; β = -2.625, p = .028; β = -1.762, p = .121; for the beginning, middle, and ending sub-periods, respectively). In other words, subjects with higher MoCA scores exhibited stronger alpha-rhythm desychronization during the MIP period, particularly during the beginning and middle sub-periods (see Fig 6).

Fig 6. Correlations between MoCA score and standardized PCu alpha-rhythm amplitude.

Fig 6

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Repeated measures linear regression analysis demonstrated that MoCA score significantly correlated alpha-rhythm amplitude in the beginning and middle sub-periods. Note that the analysis only considered bilaterally averaged PCu values for the MIP sub-periods (n = 20; some data points are overlapped). Left and right PCu, and holding values are shown for display purposes.

Interestingly, repeated measures linear regression revealed no relationship between overall memory performance and bilateral PCu brain activity, with no significant effect of sub-period (F(2, 36) = 1.245, p = .30), overall accuracy (F(1, 18) = .129, p = .724), nor significant interaction between sub-period and overall accuracy (F(2, 36) = 1.306, p = .283) on alpha-rhythm amplitude.

Discussion

Surprisingly, two thirds of all participants had below normal MoCA scores, suggesting a decline in cognitive function. Nevertheless, all participants were healthy workers registered in the employment service center for elderly people, suggesting that those with below normal MoCA scores likely had no awareness of cognitive decline. Although both High and Low groups exhibited clear serial position effects in terms of recall accuracies, the High group had higher accuracy for all memory items, particularly during the beginning sub-period (Fig 2). Indeed, regression analysis revealed a significantly positive relationship between MoCA score and recall accuracy during the beginning sub-period. This observation was statistically corroborated by the positive relationship between MoCA score and recall accuracy corresponding to the beginning sub-period (Fig 3). As mentioned in the introduction, memorization of the beginning items in a sequential memory task should invoke the phonological loop, an important function in working memory. Working memory should also be at play in the middle sub-period. However, in the present study, accuracy for items corresponding to the middle sub-period were only slightly above chance level (25%), suggesting that the floor effect may have obscured the relationship. Nevertheless, the relationship between MoCA score and beginning sub-period accuracy indicates that MoCA score is related to working memory function.

The vertices at which alpha-rhythm amplitudes were lower for the High group than those for Low group were densely clustered bilaterally in the PCu region (Fig 4). Since, there was no significant difference in age between groups, the differences in alpha-rhythm amplitude are presumed to relate to the differences in cognitive function detected by the MoCA. Narrowing down the candidate brain region associated with MoCA score to the PCu, the High group indeed exhibited sustained alpha-rhythm desynchronization throughout the MIP period (Fig 5). Conversely, alpha-rhythm in the Low group exhibited little or no change from baseline aside from brief synchronization events, which are present in both groups and thought to represent transient responses to memory items. As alpha-rhythm desynchronization was observed in both the MIP and holding periods, it can be attributed to task-engagement, not default mode activity. Meanwhile, the time course of the PCu alpha-rhythm amplitude (Fig 5) and its time-averaged amplitudes demonstrated a negative relationship to MoCA score, particularly during the beginning and middle MIP sub-periods (Fig 6). Our results suggest a functional weakening of PCu engagement in lower MoCA scorers, especially at the beginning and middle MIP sub-periods.

The relationships between the MoCA score and PCu alpha-rhythm amplitude (Fig 6) coincide well with the relationship between MoCA score and accuracy (Fig 3). The results demonstrating significant relationships only in the beginning and middle sub-periods reinforces the idea that the PCu is involved in working memory. However, studies regarding the PCu’s involvement in working memory are few [29–31]. Meanwhile, considerable studies have observed metabolic reduction [32] or hypoperfusion [33, 34] of the PCu in the early stages of Alzheimer’s disease (AD). Love and Miners argue in their review [34] that hypoperfusion starts from the PCu, well before the onset of dementia, and spreads to the rest of the parietal cortex and the cingulate gyrus along with progression of AD. Although MCI does not always progress to dementia, it seems reasonable that weaker functional engagement of the PCu could be causal to lower MoCA score (higher MCI tendency), and lower working memory performance observed in our experimental task (beginning sub-period in Figs 3 and 6). It is also reasonable that a relationship with MoCA score is not observed in the end sub-period because this sub-period involves immediate memory, which is functionally distinct from working memory (end sub-period in Figs 3 and 6).

Interestingly, the alpha-rhythm amplitude in the PCu was not correlated with accuracy in and of itself in any sub-period. That may mean that, although functional weakening of the PCu may be causal to MCI, it may not always affect memory performance directly but rather indirectly via general cognitive engagement. The participants in this work probably had no awareness of a decline of any cognitive functions. Our results suggest that MEG could be used to detect subconscious age-based declines in cognitive function noninvasively by monitoring PCu activity. Non-invasive monitoring of the PCu would be highly beneficial for aged people, particularly considering that hitherto metabolic reduction or hypoperfusion studies implicating the PCu have either been investigated by positron emission tomography (PET), or pathophysiological methods by using post-mortem tissue [32–34]. More importantly is the fact that MCI does not always progress to dementia or AD. Given that PCu dysfunction is a neuropathological indicator in the early stages of AD, it may be possible to predict disease progression at the time of MCI symptom emergence using electrophysiological methods such as MEG or EEG.

The present study has some limitations. First, because working memory function may differ between sexes, only males were targeted. Separate research targeting females is needed to ascertain the extent to which present findings are generalizable across sexes. Additionally, only the alpha-rhythm was targeted. In general, brain rhythms are thought to play different functional roles. Therefore, future studies analyzing other rhythms, such as theta-rhythm for instance, should permit insight into other aspects underlying the neural mechanism of MCI.

Conclusion

Our research aim was to identify brain regions related to MCI. We recorded MEG during a sequential memory task and observed that the functional responsiveness of PCu alpha-rhythm attenuated in correspondence with low MoCA score, which is associated with MCI. Specifically, low MoCA scorers exhibited a functional decline in PCu alpha-rhythm desynchronization response particularly during the beginning parts of the sequence in which working memory plays a role. The participants of this study, who were recruited from an employment service center for elderly people, were physically healthy and active, and probably not cognizant of any cognitive functional decline. Our results suggest that MEG could be used to detect age-based declines in cognitive function noninvasively by monitoring PCu alpha activity. This could be beneficial to societies concerned with age-based cognitive decline, because hitherto metabolic reduction or hypoperfusion studies have been investigated invasively either by PET or pathophysiological methods.

Supporting information

S1 Data

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S2 Data

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S3 Data

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Acknowledgments

We thank Dr. Yuki Takakura, ST, and Dr. Mika Otsuki, MD, of Hokkaido University for their instruction in the implementation of the MoCA test. We deeply thank Dr. Ryusaku Hashimoto of Health Sciences University of Hokkaido and Prof. Shinya Kuriki of Hokkaido University for their useful discussion and suggestions. We also thank the graduate and undergraduate students of Hokkaido University and Hokkaido Bunkyo University, especially Mr. Nagito Suzuki, Mr. Kohji Ogino, Mr. Yuya Takeshita, and Mr. Hayate Onishi, for their help in the experiment and analysis.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

KY has been supported by Japan Society for the Promotion of Science (JSPS): https://www.jsps.go.jp/ under KAKENHI JP16K01345 and JP20H04496. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Decision Letter 0

Stephen D Ginsberg

3 Aug 2020

PONE-D-20-15784

Functional decline of the precuneus associated with mild cognitive impairment: magnetoencephalographic observations

PLOS ONE

Dear Dr. Yokosawa,

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==============================

Comments to the Author

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Reviewer #1: Partly

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2. Has the statistical analysis been performed appropriately and rigorously? 

Reviewer #1: No

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Reviewer #1: This work is an attempt to correlate cognitive decline with deactivation of PCu for which MEG signal is analysed for MOCA test that is attributed to working memory. The following comments should be considered.

"A score of 26 or 90 above is considered normal. Participants were placed into two groups: those that scored normally (High group, n = 7), and those with below normal scores (Low group, n = 13)."

What are individual MOCA scores of 20 subjects? How high is considered high and how low is considered low. Is there any step-jump in MOCA score of these two groups? If there is no step-jump, do subjects close to the separation boundary of two groups show similarity which affects the conclusion drawn? Should there be distinct separation of two groups which might requiring discarding some subjects who are close to the boundary? Will it create further imbalance in the dataset and lower the number of subjects to draw any definitive conclusions?

The n=7 and n=13 suffers from class imbalance that can affect the study. As such, The study has a very low n-size (though effects are observed) which makes it difficult to generalize. All the participants are Male. This analysis and results can be different if authors include female participants, as working memory functions differently in female. Even MCI age range may be different in case of female.

Though this study repeatedly mentioned about early detection about MCI, the participants age range appears to be too high. It is mentioned that “there was no significant difference in age between groups ” - however, neither significant p-value is reported, nor group-wise mean(+/- a.d.) age is mentioned.

Though there are a large number of references given to support why alpha rhythm is analysed. However in this domain of working memory (WM) related to cognitive control it is noted in literature that theta band is also deeply engaged with WM in literature. The authors are encouraged to investigate all the frequency bands; at least theta, beta and lower gamma oscillations along with the alpha band. This detail analysis will give a clearer depiction on an MEG study of functional decline associated with MCI.

As the time series are averaged across all epochs, a band of depicting the deviation (say, one standard deviation) about the average would bring more clarity on how MEG signal is behaving. Is the 'standardised amplitude' related to ERF for MEG (similar to ERP in EEG)?

How is the repeated measures linear regression done? How many regression coefficients are used? Why is this method chosen to measure correlation? In Fig.3, the straight line fit of middle and end has nearly same slope which discards the recency effect? What is the purpose of linear regression then?

“The surface of the template brain was decomposed to 15002 vertices and current dipoles were estimated on each vertex without orientation constraints.”—the authors should mention the atlas references from which template is obtained in this work and how this is arrived at from the acquired MEG signal. In the head template, please mention the motor regions, as according to the study, the participants pressed the button which accounts motor activity.

Please justify that the results are not biased by any motor activity. As the work suggests that “the PCu is associated with early MCI”; however in literature it was found that the precuneus generally involved in motor imagery and shifting attention between motor targets. Figure 5 represents that Time courses of bilateral standardized alpha-rhythm amplitude, where in the beginning, middle and end time course shows alpha desynchronization. Then, how can the authors justify the conclusion that PCu alpha desynchronization was

particularly absent during the beginning parts of the sequence in which working memory plays a role.

**********

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Reviewer #1: No

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

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PLoS One. 2020 Sep 28;15(9):e0239577. doi: 10.1371/journal.pone.0239577.r002

Author response to Decision Letter 0

2 Sep 2020

<Editors' comments and our responses>

<Comment >

The Academic Editor is also requesting that a statistical collaborator be included to address the statistical critiques.

<Response>

Thank you for the suggestion. Although we have not added a statistical collaborator, the statis-tical methods and reporting have been rigorously revised. Specifically, the description of all statistical tests has been organized into a new section in the methods section entitled, ‘Statistical analyses.’ The multiple regression analyses have been described in detail, and the full results of the regression models are clearly reported in the results. See <Response 6> below.

<Reviewers' comments and our responses>

<Comment 1>

What are individual MOCA scores of 20 subjects? How high is considered high and how low is con-sidered low. Is there any step-jump in MOCA score of these two groups? If there is no step-jump, do subjects close to the separation boundary of two groups show similarity which affects the conclusion drawn? Should there be distinct separation of two groups which might requiring discarding some subjects who are close to the boundary? Will it create further imbalance in the dataset and lower the number of subjects to draw any definitive conclusions?

<Response 1>

Thank you for your comments.

The individual MoCA scores are shown below; Score (Number of participants):

Low group: 19 (1), 20 (2), 22 (1), 23 (3), 24 (3), 25(3)

High group: 26 (3), 27 (3), 30 (1)

In the article, the score distribution can be seen in the horizontal axis in Fig. 3 or Fig. 6.

A score of 26 or above is considered normal in accordance with MoCA scoring instruction:

https://www.parkinsons.va.gov/resources/MoCA-Instructions-English.pdf

To clarify the criteria, the description has been revised as shown below. No step jump was observed between groups. Discarding some subjects who are close to the boundary may clarify the result of Fig. 4, but result in a detrimental loss in sample size smaller, as suggested by the reviewer. We would like to avoid arbitrary discarding, so we have not discarded any subjects in the revised manuscript. Moreover, the main results of the study are based on linear regression, and thus consider the rela-tionship of MoCA score irrespective of group.

(Page 3, lines 88-90): “In accordance with MoCA scoring instructions, one point was added for par-ticipants who had 12 or fewer years of formal education and a score of 26 or above is considered normal.”

<Comment 2>

The n=7 and n=13 suffers from class imbalance that can affect the study. As such, the study has a very low n-size (though effects are observed) which makes it difficult to generalize. All the partici-pants are Male. This analysis and results can be different if authors include female participants, as working memory functions differently in female. Even MCI age range may be different in case of female.

<Response 2>

Thank you for the comments. Indeed scores ranged wider than we expected, and consequently the sample size was imbalanced between groups. True, a class imbalance can limit generalizability of research findings. However, the purpose of grouping in the present study was to assist in determining a target cortical area. Once this area was determined, the subsequent main statistical analyses were based on linear regression, and thus the issue of class imbalance does not apply to the main findings of the present study.

As suggested by the reviewer, working memory functions are likely different between male and fe-male. It is precisely because of this that we restricted our analyses to one sex only. Indeed, care was taken to keep subject characteristics as homogeneous as possible. The study of sex differences is also important topic, but is left as a next step. This point has been added as a limitation at the end of the discussion (Page 10, lines 327-329), as shown below.

“…working memory function may differ between sexes, only males were targeted. Separate research targeting females is needed to ascertain the extent to which present findings are generalizable across sexes.”

<Comment 3>

Though this study repeatedly mentioned about early detection about MCI, the participants age range appears to be too high. It is mentioned that “there was no significant difference in age between groups ” - however, neither significant p-value is reported, nor group-wise mean(+/- a.d.) age is men-tioned.

<Response 3>

Thank you for your comments. The mean age of the groups and the p value for two-tailed independ-ent t test have been added to the text. Please see Page 6, lines 197-198.

“High and Low groups (65.4 ± 2.9 years old (mean ± SD) vs. 68.6 ± 4.4 years old (mean ± SD), respectively; p = .104).”

The fact that the age range is younger than that which is perhaps normally associated with MCI is why we mention “early” detection. Normally, when testing is conducted for MCI, it is because a patient has come in for medical consultation after noticing some decline in cognitive function. The point we are emphasizing in the present study is that the subjects tested were actively working and independently living. They were likely not cognizant of any functional decline. Nevertheless, we detected a functional decline in PCu alpha activity that was related to MoCA score. Thus, non-invasive testing with MEG could potentially be used for early detection of MCI, before people are even cognizant of any impairment.

<Comment 4>

Though there are a large number of references given to support why alpha rhythm is analysed. How-ever in this domain of working memory (WM) related to cognitive control it is noted in literature that theta band is also deeply engaged with WM in literature. The authors are encouraged to investigate all the frequency bands; at least theta, beta and lower gamma oscillations along with the alpha band. This detail analysis will give a clearer depiction on an MEG study of functional decline associated with MCI.

<Response 4>

Thank you for the useful comments. Actually, we have already analyzed the beta- and the-ta-rhythms; analysis of the gamma-rhythm is rather difficult for us because its frequency band is overlapped with that of the power line. As a preliminary result, the theta-rhythm analysis suggests that MCI delays the visual input of memory items (related results can be seen in Takase et al., Conf Proc IEEE Eng Med Biol Soc., pp. 1713-1716, 2019).

In general, brain rhythms are thought to play different functional roles. While we agree with the reviewer that presenting results from multiple frequency bands in one study can pro-vide more comprehensive insight, it can also dilute the focus of the scientific narrative. In the present study, we contend that presentation of alpha-rhythm only permits a more concise and comprehensible narrative, and that the presentation of other frequency bands, such as theta rhythm for instance, would be better done as a separate study. To clarify our position on this, the following lines have been added in the limitations paragraph at the end of the discussion in the revised manuscript (Page 10, lines 330-332).

“In general, brain rhythms are thought to play different functional roles. Therefore, future studies analyzing other rhythms, such as theta-rhythm for instance, should permit insight into other aspects underlying the neural mechanism of MCI.”

<Comment 5>

As the time series are averaged across all epochs, a band of depicting the deviation (say, one standard deviation) about the average would bring more clarity on how MEG signal is behaving. Is the 'stand-ardised amplitude' related to ERF for MEG (similar to ERP in EEG)?

<Response 5>

Thank you for the comment. We have revised Fig. 5 (see the attachment) to show the standard error (SE) of the mean time courses across subjects in each group. SE was chosen over standard deviation as SE makes the figure easier to visualize, and is more suitable for demonstrating statistically rele-vant differences of the standardized amplitudes between groups. The figure caption has also been revised accordingly.

Standardized amplitude is not related to ERF per se. In the present study, it simply means that the amplitude of the activity envelope has been converted from a measure of signal strength into a per-cent deviation from a baseline level. However, brain activity envelopes can contain information about transient brain responses, and do in the present study. The initial manuscript mentions this in the discussion, stating that the brief synchronization events which are visible in the time-courses are thought to represent transient responses to memory items. In the revised manuscript, this text can be found on Page 9, lines 292-293.

(Caption of Fig. 5 on page 8):

Fig. 5. Time courses of standardized alpha-rhythm amplitude originating from the left and right PCu. Signals are expressed as a percent deviation from baseline (-1–0 s). Time courses of the mean values and standard errors across subjects are shown. Alpha-rhythm amplitude desynchronized markedly during encoding in the High group, but not in the Low group. Laterality (Left/Right) was not mark-edly different.

<Comment 6>

How is the repeated measures linear regression done? How many regression coefficients are used? Why is this method chosen to measure correlation? In Fig.3, the straight line fit of middle and end has nearly same slope which discards the recency effect? What is the purpose of linear regression then?

<Response 6>

Thank you for your comment. We have realized that the statistical analyses were poorly described in the initial manuscript. Moreover, the use of the term correlation was misleading. In the revised man-uscript, we have reorganized the methods section, and placed a description of all statistical analyses in a section entitled, “Statistical analyses” (Page 6, lines 179-194).

Three different multiple linear regression analyses were performed. One used MoCA score as the regressor against recall accuracy according to each memory item presentation (MIP) sub-period (3 repeated measures: beginning, middle, and ending sub-period accuracy). A second multiple linear regression analysis regressed MoCA score against standardized alpha-rhythm amplitude in each MIP sub-period (3 repeated measures: beginning, middle, and ending sub-period alpha amplitude). A third multiple linear regression analysis regressed overall recall accuracy against standardized al-pha-rhythm amplitude in each MIP sub-period (3 repeated measures: beginning, middle, and ending sub-period alpha amplitude). Note that regression is used to assess linear relationships, and is com-pletely unrelated to the High and Low groupings which were used to identify target cortical areas for testing with regression.

The results section has been revised to properly report the F statistics and p values for the main fac-tors and interactions in these multiple regression models.

Regarding Fig. 3, the straight line represents the slope of the relationship of MoCA score to recall accuracy in accordance with each MIP sub-period. Note that the r-squared values have been replaced by beta statistics, as is appropriate when reporting relationships based on linear regression. The pri-macy/recency effect of the memory task can be clearly discerned by comparing the Y-axis values across the three sub-periods.

We hope that these revisions will clear up any remaining confusion regarding the statistical analyses. Please let us know if you have any further concerns.

<Comment 7>

“The surface of the template brain was decomposed to 15002 vertices and current dipoles were esti-mated on each vertex without orientation constraints.”—the authors should mention the atlas refer-ences from which template is obtained in this work and how this is arrived at from the acquired MEG signal. In the head template, please mention the motor regions, as according to the study, the partici-pants pressed the button which accounts motor activity.

<Response 7>

Thank you for the comment. MEG signals were processed by using Brainstorm software. The tem-plate brain was ICBM152, which is the default template used by Brainstorm. Alignment to the tem-plate, minimum-norm estimation were performed as per the default Brainstorm processing functions. The Desikan-Killiany cortical atlas is also part of Brainstorm software, and was applied as per stand-ard procedures in Brainstorm. A mention of Brainstorm has been added in the methods section (Page 5, line 152: “MEG signals were processed by using Brainstorm software.”).

The Desikan-Killiany atlas bilaterally includes motor cortex relevant regions such as the pre and post-central gyri. Selecting these regions and extracting their brain activity is a simple matter in Brainstorm. However, we do not think there is just cause for emphasizing brain activity in motor regions. First, the primary region exhibiting differential activity between High and Low groups was clearly the precuneus. Secondly, the analyses performed in the present study were during the memory item presentation period and the holding period. These are time windows where motor activity, even preparatory activity, should not have been present. The only period that should have had any motor related activity would have been the period from response cue presentation until the time of the re-sponse (approximately 1.5 s after recall cue presentation. See Fig. 1). However, this period was not considered in our analyses. Finally, response times (from onset of the recall cue until the moment of button press) were not significantly different between High and Low groups. Therefore, motor-related neural activities should not have been a factor in our results.

Note, a description of response times has been added to the text as per below:

(Page 4, lines 114-116) The pressed button, response accuracy, and response time (which is dura-tion between the onset of the recall cue and the moment of button press) were recorded auto-matically.

(Page 6, lines 180-181) The difference in age, and response time, between the High and Low MoCA score groups was separately compared via two-tailed independent t test with SPSS (IBM).

(Page 6, lines 201-203) However, no significant difference was obtained in the response times between those of the High and Low groups (1.57 ± 0.09 s (mean ± SD) vs. 1.49 ± 0.36 s (mean ± SD), respectively; p = .588).

<Comment 8>

Please justify that the results are not biased by any motor activity. As the work suggests that “the PCu is associated with early MCI”; however in literature it was found that the precuneus generally involved in motor imagery and shifting attention between motor targets. Figure 5 represents that Time courses of bilateral standardized alpha-rhythm amplitude, where in the beginning, middle and end time course shows alpha desynchronization. Then, how can the authors justify the conclusion that PCu alpha desynchronization was particularly absent during the beginning parts of the sequence in which working memory plays a role.

<Response 8>

Indeed, the PCu has been functional related to numerous aspects of visual processing, including that which is related to imagery and visual targeting. Importantly, as described in <Response 7>, we do not think motor activity is functionally relevant to the analyses of the present study.

The lack of PCu alpha desynchronization is a phenomenon that was exhibited by the Low MoCA scoring group in comparison to the High MoCA scoring group, and can be discerned clearly in Fig. 5. Multiple linear regression analysis confirmed that the alpha desynchronization response abates in correspondence with lower MoCA score for all MIP sub-periods, and significantly so for the begin-ning and middle sub-periods (see Fig. 6). In other words, PCu alpha desynchronization during memory item presentation is a functional response particularly exhibited by high MoCA scorers, and not exhibited by low MoCA scorers. Hence, we conclude that poor PCu alpha desynchronization response, particularly during the beginning sub-period may be related to MCI.

The Conclusion section has been reworded to improve clarity. (Page 10, lines 335-340)

“We recorded MEG during a sequential memory task and observed that the functional respon-siveness of PCu alpha-rhythm attenuated in correspondence with low MoCA score, which is associated with MCI. Specifically, low MoCA scorers exhibited a functional decline in PCu alpha-rhythm desynchronization response particularly during the beginning parts of the se-quence in which working memory plays a role.”

Decision Letter 1

Stephen D Ginsberg

10 Sep 2020

Functional decline of the precuneus associated with mild cognitive impairment: magnetoencephalographic observations

PONE-D-20-15784R1

Dear Dr. Yokosawa,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Stephen D. Ginsberg, Ph.D.

Section Editor

PLOS ONE

Acceptance letter

Stephen D Ginsberg

18 Sep 2020

PONE-D-20-15784R1

Functional decline of the precuneus associated with mild cognitive impairment: Magnetoencephalographic observations

Dear Dr. Yokosawa:

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