scholarly journals Non-linear Functional Brain Co-activations in Short-Term Memory Distortion Tasks

2021 ◽  
Vol 15 ◽  
Author(s):  
Anna Ceglarek ◽  
Jeremi K. Ochab ◽  
Ignacio Cifre ◽  
Magdalena Fafrowicz ◽  
Barbara Sikora-Wachowicz ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Cognitive Performance ◽  
Linear Correlation ◽  
Short Term Memory ◽  
Time Of Day ◽  
Short Term ◽  
Term Memory ◽  
Non Linear ◽  
Linear Correlation Analysis ◽  
Linear Correlations

Recent works shed light on the neural correlates of true and false recognition and the influence of time of day on cognitive performance. The current study aimed to investigate the modulation of the false memory formation by the time of day using a non-linear correlation analysis originally designed for fMRI resting-state data. Fifty-four young and healthy participants (32 females, mean age: 24.17 ± 3.56 y.o.) performed in MR scanner the modified Deese-Roediger-McDermott paradigm in short-term memory during one session in the morning and another in the evening. Subjects’ responses were modeled with a general linear model, which includes as a predictor the non-linear correlations of regional BOLD activity with the stimuli, separately for encoding and retrieval phases. The results show the dependence of the non-linear correlations measures with the time of day and the type of the probe. In addition, the results indicate differences in the correlations measures with hippocampal regions between positive and lure probes. Besides confirming previous results on the influence of time-of-day on cognitive performance, the study demonstrates the effectiveness of the non-linear correlation analysis method for the characterization of fMRI task paradigms.

scholarly journals Non-linear functional brain co-activations in short-term memory distortion tasks

2021 ◽  
Author(s):  
Anna Ceglarek ◽  
Jeremi K. Ochab ◽  
Ignacio Cifre ◽  
Magdalena Fąfrowicz ◽  
Barbara Sikora-Wachowicz ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Cognitive Performance ◽  
Linear Correlation ◽  
Short Term Memory ◽  
Time Of Day ◽  
Short Term ◽  
Term Memory ◽  
Non Linear ◽  
Linear Correlation Analysis ◽  
Linear Correlations

AbstractRecent works shed light on the neural correlates of true and false recognition and the influence of time of day on cognitive performance. The current study aimed to investigate the modulation of the false memory formation by the time of day using a non-linear correlation analysis originally designed for fMRI resting-state data. Fifty-four young and healthy participants (32 females, mean age: 24.17 y.o., SD: 3.56 y.o.) performed in MR scanner the modified Deese-Roediger-McDermott paradigm in short-term memory during one session in the morning and another in the evening. Subjects’ responses were modeled with a general linear model, which includes as a predictor the non-linear correlations of regional BOLD activity with the stimuli, separately for encoding and retrieval phases. The results show the dependence of the non-linear correlations measures with the time of day and the type of the probe. In addition, the results indicate differences in the correlations measures with hippocampal regions between positive and lure probes. Besides confirming previous results on the influence of time-of-day on cognitive performance, the study demonstrates the effectiveness of the non-linear correlation analysis method for the characterization of fMRI task paradigms.

scholarly journals Higher Handgrip Strength Is Linked to Better Cognitive Performance in Chinese Adults with Hypertension

2021 ◽  
Vol 11 (8) ◽  
pp. 985
Author(s):  
Shenghua Lu ◽  
Fabian Herold ◽  
Yanjie Zhang ◽  
Yuruo Lei ◽  
Arthur F. Kramer ◽  
...  
Keyword(s):  
Cognitive Performance ◽  
Short Term Memory ◽  
Handgrip Strength ◽  
Adult Health ◽  
Linear Regression Models ◽  
Chinese Adults ◽  
Short Term ◽  
Term Memory ◽  
Using Data ◽  
The Relationship

Objective: There is growing evidence that in adults, higher levels of handgrip strength (HGS) are linked to better cognitive performance. However, the relationship between HGS and cognitive performance has not been sufficiently investigated in special cohorts, such as individuals with hypertension who have an intrinsically higher risk of cognitive decline. Thus, the purpose of this study was to examine the relationship between HGS and cognitive performance in adults with hypertension using data from the Global Ageing and Adult Health Survey (SAGE). Methods: A total of 4486 Chinese adults with hypertension from the SAGE were included in this study. Absolute handgrip strength (aHGS in kilograms) was measured using a handheld electronic dynamometer, and cognitive performance was assessed in the domains of short-term memory, delayed memory, and language ability. Multiple linear regression models were fitted to examine the association between relative handgrip strength (rHGS; aHGS divided by body mass index) and measures of cognitive performance. Results: Overall, higher levels of rHGS were associated with higher scores in short-term memory (β = 0.20) and language (β = 0.63) compared with the lowest tertiles of rHGS. In male participants, higher HGS was associated with higher scores in short-term memory (β = 0.31), language (β = 0.64), and delayed memory (β = 0.22). There were no associations between rHGS and cognitive performance measures in females. Conclusion: We observed that a higher level of rHGS was associated with better cognitive performance among hypertensive male individuals. Further studies are needed to investigate the neurobiological mechanisms, including sex-specific differences driving the relationship between measures of HGS and cognitive performance in individuals with hypertension.

scholarly journals Effects of Dehydration and Rehydration on Cognitive Performance and Mood among Male College Students in Cangzhou, China: A Self-Controlled Trial

2019 ◽  
Vol 16 (11) ◽  
pp. 1891 ◽  
Author(s):  
Na Zhang ◽  
Song M. Du ◽  
Jian F. Zhang ◽  
Guan S. Ma
Keyword(s):  
Cognitive Performance ◽  
Test Scores ◽  
Short Term Memory ◽  
Digit Span ◽  
Error Rates ◽  
Digit Symbol ◽  
Short Term ◽  
Mental Work ◽  
Term Memory ◽  
Baseline Test

Water accounts for 75% of brain mass. Associations may exist between hydration and cognitive performance. The objective of this study was to investigate the effects of dehydration and rehydration on cognitive performance and mood. In this self-control trial, 12 men were recruited from a medical college in Cangzhou, China. After 12 h of overnight fasting, the participants took baseline tests at 8:00 AM on day 2. First morning urine and blood osmolality were analyzed to determine hydration state. Height, weight, and blood pressure were measured following standardized procedures. A visual analog scale for the subjective sensation of thirst was applied, and a profile of mood states questionnaire was applied. Tests were conducted for cognitive performance, including a test of digit span forward and backward, digit-symbol substitutions, dose-work, and stroop effects. Participants were required not to drink water for 36 h but were given three meals on day 3. On day 4, the same indexes were tested as a baseline test. At 8:30 AM, participants drank 1500 mL of purified water over 15 min. After a 1 h interval, the same measurements were performed. Compared with baseline test results, during the dehydration test, participants had lower scores of vigor (11.9 vs. 8.8, %, p = 0.007) and esteem-related affect (8.2 vs. 5.7, %, p = 0.006), lower total scores of digit span (14.3 vs. 13.3, %, p = 0.004), and higher error rates for dose-work (0.01 vs. 0.16, %, p = 0.005). Compared with the dehydration test scores, rehydration test scores showed that fatigue (4.3 vs. 2.1, %, p = 0.005) and total mood disturbance (TMD) (99.0 vs. 90.2, %, p = 0.008) improved, and scores of forward, backward, and total digit span increased (7.7 vs. 8.6, p = 0.014; 5.7 vs. 1.2, p = 0.019; 13.3 vs. 15.4, p = 0.001). Increases were also noted in correct number of digit symbol substitutions, reading speed, and mental work ability (70.8 vs. 75.4, p < 0.001; 339.3 vs. 486.4, n/min, p < 0.001; 356.1 vs. 450.2, p < 0.001), and reaction time decreased (30.2 vs. 28.7, s, p = 0.002). As a conclusion, dehydration had negative effects on vigor, esteem-related affect, short-term memory, and attention. Rehydration after water supplementation alleviated fatigue and improved TMD, short-term memory, attention, and reaction.

scholarly journals Electroconvulsive therapy hasn’t negative effects on short-term memory function, as assessed using a bedside hand-held device

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Helge H.O. Müller ◽  
Mareen Reike ◽  
Simon Grosse-Holz ◽  
Mareike Röther ◽  
Caroline Lücke ◽  
...  
Keyword(s):  
Cognitive Performance ◽  
Electroconvulsive Therapy ◽  
Short Term Memory ◽  
Memory Function ◽  
Memory Deficits ◽  
Treatment Resistant ◽  
Negative Effects ◽  
Short Term ◽  
Term Memory ◽  
Digital Measurements

Electroconvulsive therapy (ECT) is effective in the treatment of treatment-resistant major depression. The fear of cognitive impairment after ECT often deters patients from choosing this treatment option. There is little reliable information regarding the effects of ECT on overall cognitive performance, while short-term memory deficits are well known but not easy to measure within clinical routines. In this pilot study, we examined ECT recipients’ pre- and posttreatment performances on a digital ascending number tapping test. We found that cognitive performance measures exhibited good reproducibility in individual patients and that ECT did not significantly alter cognitive performance up to 2 hours after this therapy was applied. Our results can help patients and physicians make decisions regarding the administration of ECT. Digital measurements are recommended, especially when screening for the most common side effects on cognitive performance and short-term memory.

scholarly journals Using long short-term memory networks for river flow prediction

2020 ◽  
Vol 51 (6) ◽  
pp. 1358-1376
Author(s):  
Wei Xu ◽  
Yanan Jiang ◽  
Xiaoli Zhang ◽  
Yi Li ◽  
Run Zhang ◽  
...  
Keyword(s):  
River Flow ◽  
Short Term Memory ◽  
River Basins ◽  
Short Term ◽  
Term Memory ◽  
Learning Efficiency ◽  
Flow Prediction ◽  
Non Linear ◽  
Long Short Term Memory ◽  
The Impact

Abstract Deep learning has made significant advances in methodologies and practical applications in recent years. However, there is a lack of understanding on how the long short-term memory (LSTM) networks perform in river flow prediction. This paper assesses the performance of LSTM networks to understand the impact of network structures and parameters on river flow predictions. Two river basins with different characteristics, i.e., Hun river and Upper Yangtze river basins, are used as case studies for the 10-day average flow predictions and the daily flow predictions, respectively. The use of the fully connected layer with the activation function before the LSTM cell layer can substantially reduce learning efficiency. On the contrary, non-linear transformation following the LSTM cells is required to improve learning efficiency due to the different magnitudes of precipitation and flow. The batch size and the number of LSTM cells are sensitive parameters and should be carefully tuned to achieve a balance between learning efficiency and stability. Compared with several hydrological models, the LSTM network achieves good performance in terms of three evaluation criteria, i.e., coefficient of determination, Nash–Sutcliffe Efficiency and relative error, which demonstrates its powerful capacity in learning non-linear and complex processes in hydrological modelling.

scholarly journals Hippocampal 2-Arachidonoyl Glycerol Signaling Regulates Time-of-Day- and Stress-Dependent Effects on Rat Short-Term Memory

2020 ◽  
Vol 21 (19) ◽  
pp. 7316
Author(s):  
Alessia Santori ◽  
Maria Morena ◽  
Matthew N. Hill ◽  
Patrizia Campolongo
Keyword(s):  
Recognition Memory ◽  
Short Term Memory ◽  
Memory Performance ◽  
High Stress ◽  
Time Of Day ◽  
Stress Conditions ◽  
Short Term ◽  
Term Memory ◽  
Stress Dependent ◽  
Arachidonoyl Glycerol

Background: Cannabinoids induce biphasic effects on memory depending on stress levels. We previously demonstrated that different stress intensities, experienced soon after encoding, impaired rat short-term recognition memory in a time-of-day-dependent manner, and that boosting endocannabinoid anandamide (AEA) levels restored memory performance. Here, we examined if two different stress intensities and time-of-day alter hippocampal endocannabinoid tone, and whether these changes modulate short-term memory. Methods: Male Sprague-Dawley rats were subjected to an object recognition task and exposed, at two different times of the day (i.e., morning or afternoon), to low or high stress conditions, immediately after encoding. Memory retention was assessed 1 hr later. Hippocampal AEA and 2-arachidonoyl glycerol (2-AG) content and the activity of their primary degrading enzymes, fatty acid amide hydrolase (FAAH) and monoacylglycerol lipase (MAGL), were measured soon after testing. Results: Consistent with our previous findings, low stress impaired 1-hr memory performance only in the morning, whereas exposure to high stress impaired memory independently of testing time. Stress exposure decreased AEA levels independently of memory alterations. Interestingly, exposure to high stress decreased 2-AG content and, accordingly, increased MAGL activity, selectively in the afternoon. Thus, to further evaluate 2-AG’s role in the modulation of short-term recognition memory, rats were given bilateral intra-hippocampal injections of the 2-AG hydrolysis inhibitor KML29 immediately after training, then subjected to low or high stress conditions and tested 1 hr later. Conclusions: KML29 abolished the time-of-day-dependent impairing effects of stress on short-term memory, ameliorating short-term recognition memory performance.

scholarly journals Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX)

2021 ◽  
Vol 25 (3) ◽  
pp. 1671-1687
Author(s):  
Andreas Wunsch ◽  
Tanja Liesch ◽  
Stefan Broda
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Convolutional Neural Networks ◽  
Groundwater Level ◽  
Short Term Memory ◽  
State Of The Art ◽  
Short Term ◽  
Term Memory ◽  
Non Linear ◽  
Long Short Term Memory

Abstract. It is now well established to use shallow artificial neural networks (ANNs) to obtain accurate and reliable groundwater level forecasts, which are an important tool for sustainable groundwater management. However, we observe an increasing shift from conventional shallow ANNs to state-of-the-art deep-learning (DL) techniques, but a direct comparison of the performance is often lacking. Although they have already clearly proven their suitability, shallow recurrent networks frequently seem to be excluded from the study design due to the euphoria about new DL techniques and its successes in various disciplines. Therefore, we aim to provide an overview on the predictive ability in terms of groundwater levels of shallow conventional recurrent ANNs, namely non-linear autoregressive networks with exogenous input (NARX) and popular state-of-the-art DL techniques such as long short-term memory (LSTM) and convolutional neural networks (CNNs). We compare the performance on both sequence-to-value (seq2val) and sequence-to-sequence (seq2seq) forecasting on a 4-year period while using only few, widely available and easy to measure meteorological input parameters, which makes our approach widely applicable. Further, we also investigate the data dependency in terms of time series length of the different ANN architectures. For seq2val forecasts, NARX models on average perform best; however, CNNs are much faster and only slightly worse in terms of accuracy. For seq2seq forecasts, mostly NARX outperform both DL models and even almost reach the speed of CNNs. However, NARX are the least robust against initialization effects, which nevertheless can be handled easily using ensemble forecasting. We showed that shallow neural networks, such as NARX, should not be neglected in comparison to DL techniques especially when only small amounts of training data are available, where they can clearly outperform LSTMs and CNNs; however, LSTMs and CNNs might perform substantially better with a larger dataset, where DL really can demonstrate its strengths, which is rarely available in the groundwater domain though.

scholarly journals Dynamics of serial position change in probe-recognition task

Psihologija ◽  
2002 ◽  
Vol 35 (3-4) ◽  
pp. 261-285
Author(s):  
Mario Fific
Keyword(s):  
Serial Position ◽  
Short Term Memory ◽  
Serial Position Effect ◽  
Position Effect ◽  
Recognition Task ◽  
Short Term ◽  
Position Change ◽  
Term Memory ◽  
Non Linear ◽  
Probe Recognition

Relationship between practice and serial position effects was investigated, in order to obtain more evidence for underlying short-term memory processes. The investigated relationship is termed the dynamics of serial position change. To address this issue, the present study investigated mean latency, errors, and performed Ex-Gaussian convolution analysis. In six-block trials the probe-recognition task was used in the so-called fast experimental procedure. The serial position effect was significant in all six blocks. Both primacy and recency effects were detected, with primacy located in the first two blocks, producing a non-linear serial position effect. Although the serial position function became linear from the third block on, the convolution analysis revealed a non-linear change of the normal distribution parameter, suggesting special status of the last two serial positions. Further, separation of convolution parameters for serial position and practice was observed, suggesting different underlying mechanisms. In order to account for these findings, a strategy shift mechanism is suggested, rather then a mechanism based on changing the manner of memory scanning. Its influence is primarily located at the very beginning of the experimental session. The pattern of results of errors regarding the dynamics of serial position change closely paralleled those on reaction times. Several models of short-term memory were evaluated in order to account for these findings.

Toward Downscaling Storage-Discharge Dynamics: Training Long Short-Term Memory (LSTM) model for Simulating Nonlinear Storage-Discharge Relations at The Rum River Watershed, MN

2021 ◽  
Author(s):  
Pai-Feng Teng ◽  
John Nieber
Keyword(s):  
Short Term Memory ◽  
Water Storage ◽  
Rainfall Runoff ◽  
Total Water ◽  
Short Term ◽  
Term Memory ◽  
Non Linear ◽  
Long Short Term Memory ◽  
The Relationship ◽  
Hydrological Variables

&lt;p&gt;Flooding is one of the most financially devastating natural hazards in the world. Studying storage-discharge relations can have the potential to improve existing flood forecasting systems, which are based on rainfall-runoff models. This presentation will assess the non-linear relation between daily water storage (&amp;#916;S) and discharge (Q) simulated by physical-based hydrological models at the Rum River Watershed, a HUC8 watershed in Minnesota, between 1995-2015, by training Long Short-Term Memory (LSTM) networks and other machine learning (ML) algorithms. Currently, linear regression models do not adequately represent the relationship between the simulated total &amp;#916;S and total Q at the HUC-8 watershed (R&lt;sup&gt;2&lt;/sup&gt; = 0.3667). Since ML algorithms have been used for predicting the outputs that represent arbitrary non-linear functions between predictors and predictands, they will be used for improving the accuracy of the non-linear relation of the storage-discharge dynamics. This research will mainly use LSTM networks, the time-series deep learning neural network that has already been used for predicting rainfall-runoff relations. The LSTM network will be trained to evaluate the storage-discharge relationship by comparing two sets of non-linear hydrological variables simulated by the semi-distributed Hydrological Simulated Program-Fortran (HSPF): the relationship between the simulated discharges and input hydrological variables at selected HUC-8 watersheds, including air temperatures, cloud covers, dew points, potential evapotranspiration, precipitations, solar radiations, wind speeds, and total water storage, and the dynamics between simulated discharge and input variables that do not include the total water storage. The result of this research will lay the foundation for assessing the accuracy of downscaled storage-discharge dynamics by applying similar methods to evaluate the storage-discharge dynamics at small-scaled, HUC-12 watersheds. Furthermore, its results have the potentials for us to evaluate whether downscaling of storage-discharge dynamics at the HUC-12 watershed can improve the accuracy of predicting discharge by comparing the result from the HUC-8 and the HUC-12 watersheds.&lt;/p&gt;

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