Effects of Stochastic Facilitation in Healthy Subjects with Different Auditory Working Memory Capacity
1,2Othman, E. a., 2mOhamad, m. 3abdul manan, h. and2,*YusOff, a. n.
ABSTRACT
This study investigated the effects of stochastic facilitation in healthy subjects with normal and low auditory working memory capacity (AWMC). Forty healthy volunteers were recruited in this study. They performed a backward recall task (BRT) in quiet and under four white noise intensity levels: 45, 50, 55, and 60 dB. Brain activations during the task were measured using functional magnetic resonance imaging (fMRI). The behavioral performance in both groups increased significantly in 50 and 55 dB white noise. The normal AWMC group (mean score = 48.70) demonstrated higher activation in the superior temporal gyrus and prefrontal cortex than the low AWMC group (mean score = 30.85). However, comparisons in the brain activation between groups for all noise levels were not statistically different. The results support previous findings that stochastic facilitation enhances cognitive performance in healthy individuals. The results also proposed that brain activity among healthy subjects is more or less similar, at least in the context of auditory working memory. These findings indicated that there were no differential effects of stochastic facilitation in healthy subjects with different AWMC.
Keywords: Auditory working memory; fMRI; SPM12; stochastic facilitation; white noise
1Department of Medical Imaging, Faculty of Health Sciences, Universiti Sultan Zainal Abidin, 21300 Kuala Terengganu, Terengganu, Malaysia
2Centre for Diagnostic, Therapeutic and Investigative Studies, Faculty of Health Science, Universiti Kebangsaan Malaysia, Jalan Raja Muda Abdul Aziz, 50300 Kuala Lumpur, Malaysia
3Department of Radiology, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Centre Bandar Tun Razak, Cheras, 56000 Kuala Lumpur, Malaysia
*Corresponding Author: Yusoff, A. N.
Email: [email protected] Tel: 603-92897295
Fax: 603-26914304
Received: 30 November 2020
Accepted for publication: 25 January 2021
Publisher: Malaysian Association of Medical Physics (MAMP) http://www.mamp.org.my/
https://www.facebook.com/MedicalPhysicsMalaysia
INTRODUCTION
Research suggests that the level of intrinsic neural noise influences a person’s cognitive performance and that each person has a different neural noise level (Sikström and Söderlund 2007). As such, it was hypothesized that adding external auditory noise may compensate for the low intrinsic neural noise levels (Sejdić and Lipsitz 2013). The phenomenon whereby introducing external noise to enhance cognitive performance is called stochastic facilitation (McDonnell and Ward 2011). Previous studies showed differential effects of stochastic facilitation in the clinical and non-clinical populations (Helps et al. 2014; Söderlund et al. 2016;
Othman et al. 2019). However, the differential effects of stochastic facilitation in healthy subjects with different auditory working memory capacity (AWMC) remains unclear. Therefore, this study aimed to investigate the differential effects of stochastic facilitation in healthy subjects with normal and low AWMC. We hypothesized that the provision of moderate noise would enhance AWM performance in both AWMC groups via stochastic facilitation. Our secondary objective was to see the differences in brain activity between the normal and low AWMC groups using a whole-brain analysis.
EXPERIMENTAL METHODS
PARTICIPANTS
Forty native Malay-speaking, right-handed healthy male volunteers gave informed consent in writing prior to participation. They had normal hearing as
assessed using pure-tone audiometry test and claimed to be non-musicians. They also did not have any history of cognitive impairments. This project was approved by the Institutional Ethics Committee of Universiti Kebangsaan Malaysia (UKM PPI/111/8/
JEP-2017-117) and the Malaysia Medical Research and Ethics Committee (NMRR-17-56-33800). The subjects’ AWMC was evaluated using the Malay Version of Auditory Verbal Learning Test (MVAVLT) (Jamaluddin et al. 2009), which was performed inside a quiet soundproof room. The experimenter read aloud 15 words at an approximately one-second interval.
Subjects had to listen, memorized, and repeat as many words as possible. A more detailed explanation of the categorization process has been discussed elsewhere (Othman et al. 2020).
AWM PROCEDURE
The subjects’ AWM performance was evaluated using the BRT (Abdul Manan et al. 2012). The task required subjects to listen and recall words in the reverse order of presentation within a given time frame. The words were presented binaurally to the subjects via MRI-safe headphones at 60 dB. The BRT was performed in a quiet soundproof room and under four different background white noise levels inside a 3.0 T magnetic resonance imaging (MRI) machine (Siemens Magnetom Verio, Philadelphia, USA). We used noise levels ranging from 45 to 60 dB as this range corresponds to an average level of noise made during a normal conversation in a quiet background (Kurmann et al. 2011; Levey et al. 2012).
DATA ACQUISITION AND PROCESSING Each scanning session began with the acquisition of a structural brain image. The imaging protocols have been described in detail elsewhere (Othman et al. 2020).
Functional data were analyzed using version 12 of the Statistical Parametric Mapping software (SPM12) (Functional Imaging Laboratory, Wellcome Department of Imaging Neuroscience, Institute of Neurology, University College of London, UK). The first four volumes were removed to allow magnetization to reach a stable state (Yusoff et al. 2013). The first step was to correct for slice acquisition delay by performing slice-timing correction.
The acquisition time of each slice was matched to the timing of a reference slice. The second step was to account for head motion by realigning the time-corrected data to the first image of each session. Then, the functional data were spatially normalized to the standard Montreal Neurological Institute (MNI) space. Lastly, the normalized images were smoothed to compensate for inter-subject variation in anatomy during averaging and increase the signal-to-noise ratio (Yusoff et al. 2016).
DATA ANALYSES
Demographic data (age and MVAVLT score) were analyzed using the IBM Statistical Package for Social Science (IBM SPSS). The Shapiro-Wilk test, rather than the Kolmogorov-Smirnov test, was used to check whether the data were normally distributed since the number of participants was less than 50. An independent t-test (P < .05) was used to contrast the demographic data between normal and low AWMC groups. Individual functional data were analyzed using a fixed effect analysis (FFX). Then, individual contrast files were computed in the random effects analysis (RFX) to produce an activation map (PFWE < 0.05).
A two-sample t-test was performed using SPM12 to compare the brain activity between normal and low AWMC groups during the BRT in each background noise level. The statistical map was thresholded at PFWE < 0.05.
RESULTS
This study compared brain activity between normal (mean age = 21.00 ± 1.52 years) and low (mean age = 21.55 ± 1.64 years) AWMC subjects during the BRT in quiet and in noise. Shapiro-Wilk test indicated that age in both groups was normally distributed. However, age was not significantly different between groups. The MVAVLT results showed that the normal AWMC group (mean score = 48.70, SD = ± 4.73) scored significantly higher (p < .001, two-tailed) as compared to the low AWMC group (mean score = 30.85, SD = ± 3.08).
This finding supports a previous study suggesting that individuals with low AWMC lack the capability to store and process information in mind (Unsworth and Engle 2007).
The Shapiro-Wilk test indicated that the BRT scores in both groups were normally distributed in all conditions. In the quiet condition, the normal AWMC group scored (mean score = 21.20 ± 1.54) significantly higher (p < .001) than the low AWMC group (mean score = 15.15 ± 1.50). In the 45 dB condition, the normal AWMC group scored (mean score = 21.90
± 1.25) significantly higher (p < .001) than the low AWMC group (mean score = 17.80 ± 3.09). In the 50 dB condition, the normal AWMC group scored (mean score = 24.20 ± 1.64) significantly higher (p < .001) than the low AWMC group (mean score = 19.70 ± 4.17).
In the 55 dB condition, the normal AWMC group scored (mean score = 25.10 ± 1.41) significantly higher (p <
.001) than the low AWMC group (mean score = 20.15 ± 2.46). Lastly, in the 60 dB condition, the normal AWMC group scored (mean score = 18.55 ± 1.19) significantly higher (p < .001) than the low AWMC group (mean
score = 10.85 ± 1.81). The results also revealed that both groups’ performance was significantly higher (p < .005) in 50 and 55 dB conditions than baseline.
These results indicated that the moderate noise level to enhance AWM performance in healthy subjects is 5 to 10 dB below the intensity of the input signal.
Brain activity during the BRT in noise was compared between the two AWMC groups, and the results are tabulated in Table 1. In both groups, whole- brain analysis obtained from the second-level RFX statistical maps revealed significant brain activations (PFWE < 0.05) in the AWM-related brain areas. The two-sample t-test indicated that brain activity was not significantly different between groups.
The brain activity pattern during the BRT in noise was also observed for both groups, as illustrated in Figure 1. Through visual observation, it can be seen that the normal AWMC group demonstrated a more robust and widely-distributed activity pattern than the low AWMC group, suggesting that the normal AWMC subjects recruited more cognitive resources during the BRT in noise than the low AWMC subjects.
Additionally, as shown in Table 1, the normal AWMC group demonstrated a greater height extent of activation in most brain areas than the low AWMC group. These brain areas included the superior temporal gyrus (STG), Heschl’s gyrus (HG), superior frontal gyrus (SFG), middle frontal gyrus (MFG), inferior frontal gyrus (MFG), inferior frontal gyrus (IFG), and anterior cingulate cortex (ACC). Nonetheless, the two-sample t-test failed to show any significantly activated brain area at a statistical threshold of PFWE < 0.05, suggesting that brain activity between normal and low AWMC subjects was not significantly different.
TABLE 1 Height extent of brain activations
Brain
areas AWMC
group
Brain activity (t-statistics)
45 dB 50 dB 55 dB 60 dB
Left Right Left Right Left Right Left Right
STG Normal 18.89 13.49 17.04 14.64 15.62 12.27 9.41 8.11
Low 9.22 8.77 7.39 8.03 8.75 8.26 8.16 8.09
HG Normal 13.55 13.79 14.36 11.40 9.39 9.63 7.37 7.96
Low 9.38 8.95 8.07 8.30 8.42 9.30 8.26 8.72
SFG Normal 7.51 8.76 9.98 9.37 10.63 7.00 6.87 7.33
Low 6.72 6.77 7.61 7.63 7.28 7.02 7.86 7.32
MFG Normal 11.72 9.45 12.14 14.94 10.72 5.43 8.68 7.20
Low 7.79 9.11 6.60 7.35 7.61 7.18 7.16 7.06
IFG Normal 10.20 8.03 9.29 10.75 10.16 13.54 7.38 6.77
Low 7.76 7.68 6.76 8.21 7.63 7.57 7.96 7.85
ACC Normal 16.56 7.72 6.51 8.33 5.44 11.04 7.02 7.39
Low 7.42 7.50 5.52 6.98 6.71 4.27 7.89 6.93
FIGURE 1 Brain activation pattern during the task in noise for the normal and low AWMC groups using a one-sample t-test
DISCUSSION
Most studies on stochastic facilitation have focused on clinical populations (Helps et al. 2014; Söderlund and Jobs 2016). Healthy participants were also included in these studies, but they served as healthy controls.
A common observation reported in these studies is that noise is beneficial for the clinical group, but not for the healthy controls. Collectively, this points out one crucial finding – stochastic facilitation may vary across individuals. The differential effects of stochastic facilitation observed across individuals may be attributed to differences in their level of intrinsic neural noise, as proposed by the moderate brain arousal model (MBA) (Söderlund and Sikström 2008). This model suggests that sub-attentive children and those clinically-diagnosed with attention deficit hyperactivity disorder (ADHD) have a low level of intrinsic neural noise. A common characteristic shared among individuals with ADHD is a low level of dopamine, a neurotransmitter that transmits information across neuronal networks (Berke 2018). Within these networks, different brain areas continuously communicate with one another via action potentials. These fluctuations in action potentials generate noise, termed as ‘intrinsic neural noise’ (Bays 2014). Since dopamine is crucial in generating the action potentials, the dopamine level determines the intrinsic neural noise (Helps et al. 2014). Hence, a low level of dopamine has been attributed to the inferior performance shown by the sub-attentive and ADHD children (Helps et al. 2014; Söderlund and Jobs 2016).
Thus, this study takes another angle and attempts to understand the differential effects of stochastic facilitation in healthy subjects with different AWMC.
In reference to the MBA model, we hypothesized that the normal and low AWMC subjects would have different intrinsic neural noise levels. Our findings, however, do not support this hypothesis.
The behavioral results revealed that the same noise level was needed to improve the AWM performance in both AWMC groups. This finding suggests that there were no differential effects of stochastic facilitation in healthy subjects with different AWMC. A limitation of this study is that only the AWM was considered.
Future research may expand the current work by investigating the differential effects of stochastic facilitation in other cognitive domains. Future research may also consider investigating the effects of different background with different intensity levels on stochastic facilitation.
CONCLUSION
This study investigated the differential effects of stochastic facilitation in healthy subjects with different AWMC and compared their brain activity during task performance in four background noise conditions. In both groups, AWM performance improved significantly in 50 and 55 dB background noise, supporting previous findings on stochastic facilitation. Additionally, normal AWMC subjects showed greater brain activity in the AWM-related brain areas than low AWMC subjects across noise levels. However, the activation was not significantly different between the two AWMC groups.
In light of these findings, it is plausible that brain activity between groups was not statistically different because brain functions among healthy subjects are more or less similar, at least in the context of AWM.
More importantly, our findings propose that there are no differential effects of stochastic facilitation in healthy subjects with different AWMC.
ACKNOWLEDGMENT
The authors would like to thank Mohamad Nor Affendi Awang and Nooradila Zolkiflee for performing the MRI scans. This research was funded by the UKM Incentive Research Grant (GGP-2017-010).
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