scholarly journals Temporally coherent perturbation of neural dynamics during retention alters human multi-item working memory

2019 ◽  
Author(s):  
Jiaqi Li ◽  
Qiaoli Huang ◽  
Qiming Han ◽  
Yuanyuan Mi ◽  
Huan Luo
Keyword(s):  
Neural Network ◽  
Working Memory ◽  
Temporal Dynamics ◽  
Human Subjects ◽  
Neural Dynamics ◽  
Perturbation Approach ◽  
Invasive Approach ◽  
Non Invasive ◽  
Model Combining ◽  
Dynamic Perturbation

SummaryTemporarily storing a list of items in working memory (WM), a fundamental ability in cognition, has been posited to rely on the temporal dynamics of multi-item neural representations during retention. Here, we develop a “dynamic perturbation” approach to manipulate the relative memory strength of a list of WM items, by interfering with their neural dynamics during the delay period in a temporally correlated way. Six experiments on human subjects confirm the effectiveness of this WM manipulation method. A computational model combining continuous attractor neural network (CANN) and short-term synaptic plasticity (STP) principles further reproduces all the empirical findings. The model shows that the “dynamic perturbation” modifies the synaptic efficacies of WM items through STP principles, eventually leading to changes in their relative memory strengths. Our results support the causal role of temporal dynamics of neural network in mediating multi-item WM and offer a promising, non-invasive approach to manipulate WM.

scholarly journals Reconstruction of input functions from a dynamic PET image with sequential administration of 15O2 and H215O for noninvasive and ultra-rapid measurement of CBF, OEF, and CMRO2

2017 ◽  
Vol 38 (5) ◽  
pp. 780-792 ◽  
Author(s):  
Nobuyuki Kudomi ◽  
Yukito Maeda ◽  
Hiroyuki Yamamoto ◽  
Yuka Yamamoto ◽  
Tetsuhiro Hatakeyama ◽  
...  
Keyword(s):  
Rate Constants ◽  
Human Subjects ◽  
Invasive Procedure ◽  
Invasive Technique ◽  
Invasive Approach ◽  
Non Invasive ◽  
Sequential Administration ◽  
Invasive Method ◽  
The Difference ◽  
Arterial Input Functions

CBF, OEF, and CMRO2 images can be quantitatively assessed using PET. Their image calculation requires arterial input functions, which require invasive procedure. The aim of the present study was to develop a non-invasive approach with image-derived input functions (IDIFs) using an image from an ultra-rapid O2 and C15O2 protocol. Our technique consists of using a formula to express the input using tissue curve with rate constants. For multiple tissue curves, the rate constants were estimated so as to minimize the differences of the inputs using the multiple tissue curves. The estimated rates were used to express the inputs and the mean of the estimated inputs was used as an IDIF. The method was tested in human subjects ( n = 24). The estimated IDIFs were well-reproduced against the measured ones. The difference in the calculated CBF, OEF, and CMRO2 values by the two methods was small (<10%) against the invasive method, and the values showed tight correlations ( r = 0.97). The simulation showed errors associated with the assumed parameters were less than ∼10%. Our results demonstrate that IDIFs can be reconstructed from tissue curves, suggesting the possibility of using a non-invasive technique to assess CBF, OEF, and CMRO2.

scholarly journals Stable population coding for working memory coexists with heterogeneous neural dynamics in prefrontal cortex

2016 ◽  
Vol 114 (2) ◽  
pp. 394-399 ◽  
Author(s):  
John D. Murray ◽  
Alberto Bernacchia ◽  
Nicholas A. Roy ◽  
Christos Constantinidis ◽  
Ranulfo Romo ◽  
...  
Keyword(s):  
Working Memory ◽  
Prefrontal Cortex ◽  
Neural Circuit ◽  
Temporal Dynamics ◽  
Population Level ◽  
Population Coding ◽  
Neural Dynamics ◽  
Stable Population ◽  
Neuron Activity ◽  
Delayed Response

Working memory (WM) is a cognitive function for temporary maintenance and manipulation of information, which requires conversion of stimulus-driven signals into internal representations that are maintained across seconds-long mnemonic delays. Within primate prefrontal cortex (PFC), a critical node of the brain’s WM network, neurons show stimulus-selective persistent activity during WM, but many of them exhibit strong temporal dynamics and heterogeneity, raising the questions of whether, and how, neuronal populations in PFC maintain stable mnemonic representations of stimuli during WM. Here we show that despite complex and heterogeneous temporal dynamics in single-neuron activity, PFC activity is endowed with a population-level coding of the mnemonic stimulus that is stable and robust throughout WM maintenance. We applied population-level analyses to hundreds of recorded single neurons from lateral PFC of monkeys performing two seminal tasks that demand parametric WM: oculomotor delayed response and vibrotactile delayed discrimination. We found that the high-dimensional state space of PFC population activity contains a low-dimensional subspace in which stimulus representations are stable across time during the cue and delay epochs, enabling robust and generalizable decoding compared with time-optimized subspaces. To explore potential mechanisms, we applied these same population-level analyses to theoretical neural circuit models of WM activity. Three previously proposed models failed to capture the key population-level features observed empirically. We propose network connectivity properties, implemented in a linear network model, which can underlie these features. This work uncovers stable population-level WM representations in PFC, despite strong temporal neural dynamics, thereby providing insights into neural circuit mechanisms supporting WM.

scholarly journals Revealing the Temporal Dynamics in Non-invasive Electrophysiological recordings with Topography-based Analyses

2019 ◽  
Author(s):  
Xuefei Wang ◽  
Hao Zhu ◽  
Xing Tian
Keyword(s):  
Processing Speed ◽  
Temporal Dynamics ◽  
Analytical Framework ◽  
Neural Dynamics ◽  
Time Duration ◽  
Topographic Analysis ◽  
Non Invasive ◽  
Temporal Variance ◽  
Related Potentials ◽  
Temporal Indices

AbstractThe fine temporal resolution of electroencephalography (EEG) makes it one of the most widely used non-invasive electrophysiological recording methods in cognitive neuroscience research. One of the common ways to explore the neural dynamics is to create event-related potentials (ERPs) by averaging trials, followed by the examination of the response magnitude at peak latencies. However, a complete profile of neural dynamics, including temporal indices of onset time, offset time, duration, and processing speed, is needed to investigate cognitive neural mechanisms. Based on the multivariate topographic analysis, we developed an analytical framework that included two methods to explore neural dynamics in ERPs. The first method separates continuous ERP waveforms into distinct components based on their topographic patterns. Crucial temporal indices such as the peak latency, onset and offset times can be automatically identified and indices about processing speed such as duration, rise, and fall speed can be derived. The second method scrutinizes the temporal dynamics of identified components by reducing the temporal variance among trials. The response peaks of signal trials are identified based on a target topographic template, and temporal-variance-free ERPs are obtained after aligning individual trials. This method quantifies the temporal variance as a new measure of cognitive noise, as well as increases both the accuracy of temporal dynamics estimation and the signal-to-noise ratio (SNR) of the ERP responses. The validity and reliability of these methods were tested with simulation as well as empirical datasets from an attention study and a semantic priming (N400) study. Together, we offer an analytical framework in a data-driven, bias-free manner to investigate neural dynamics in non-invasive scalp recordings. These methods are implemented in the Python-based open-source package TTT (Topography-based Temporal-analysis Toolbox).

NON-INVASIVE APPROACH TO PREDICT THE CHOLESTEROL LEVEL IN BLOOD USING BIOIMPEDANCE AND NEURAL NETWORK TECHNIQUES

2013 ◽  
Vol 25 (06) ◽  
pp. 1350046 ◽  
Author(s):  
M. S. Mohktar ◽  
F. Ibrahim ◽  
N. A. Ismail
Keyword(s):  
Neural Network ◽  
Body Mass ◽  
Measurement Data ◽  
Area Under The Curve ◽  
Bioelectrical Impedance ◽  
Impedance Analysis ◽  
Ann Model ◽  
Invasive Approach ◽  
Non Invasive ◽  
Artificial Neural Network Ann

This paper presents a new non-invasive approach to predict the status of high total cholesterol (TC) level in blood using bioimpedance and the artificial neural network (ANN) techniques. The input parameters for the ANN model are acquired from a non-invasive bioelectrical impedance analysis (BIA) measurement technique. The measurement data were obtained from 260 volunteered participants. A total of 190 subject's data were used for the ANN training purpose and the remaining 70 subject's data were used for model testing. Six parameters from the BIA parameters were found to be significant predictors for TC level in blood using logistic regression analysis. The six input predictors for the ANN modeling are age, body mass index (BMI), body capacitance, basal metabolic rate, extracellular mass and lean body mass. Four ANN techniques such as the gradient descent with momentum, the resilient, the scaled conjugate gradient and the Levenberg–Marquardt were used and compared for predicting the high TC level in the blood. The finding showed that the resilient method was the best model with prediction accuracy, sensitivity, specificity and area under the curve value obtained from the test data were 82.9%, 85.4%, 79.3% and 0.83%, respectively.

scholarly journals Salivary cortisol as a non-invasive approach to assess stress in dystocic dairy calves

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Levente Kovács ◽  
Fruzsina Luca Kézér ◽  
Szilárd Bodó ◽  
Ferenc Ruff ◽  
Rupert Palme ◽  
...  
Keyword(s):  
Birth Weight ◽  
Body Condition ◽  
Salivary Cortisol ◽  
Sampling Period ◽  
Dairy Calves ◽  
Birth Process ◽  
Invasive Approach ◽  
Non Invasive ◽  
Saliva Cortisol ◽  
Cortisol Levels

AbstractThe intensity and the magnitude of saliva cortisol responses were investigated during the first 48 h following birth in newborn dairy calves which underwent normal (eutocic, EUT, n = 88) and difficult (dystocic, DYS, n = 70) calvings. The effects of parity and body condition of the dam, the duration of parturition, the time spent licking the calf, the sex and birth weight of the calf were also analyzed. Neonatal salivary cortisol concentrations were influenced neither by factors related to the dam (parity, body condition) nor the calf (sex, birth weight). The duration of parturition and the time spent licking the calf also had no effect on salivary cortisol levels. Salivary cortisol concentrations increased rapidly after delivery in both groups to reach their peak levels at 45 and 60 min after delivery in EUT and DYS calves, respectively supporting that the birth process means considerable stress for calves and the immediate postnatal period also appears to be stressful for newborn calves. DYS calves exhibited higher salivary cortisol concentrations compared to EUT ones for 0 (P = 0.022), 15 (P = 0.016), 30 (P = 0.007), 45 (P = 0.003), 60 (P = 0.001) and 120 min (P = 0.001), and for 24 h (P = 0.040), respectively. Peak levels of salivary cortisol and the cortisol release into saliva calculated as AUC were higher in DYS than in EUT calves for the 48-h of the sampling period (P = 0.009 and P = 0.003, respectively). The greater magnitude of saliva cortisol levels in DYS calves compared to EUT ones suggest that difficult parturition means severe stress for bovine neonates and salivary cortisol could be an opportunity for non-invasive assessment of stress during the early neonatal period in cattle.

scholarly journals Resting‐State EEG Microstates Parallel Age‐Related Differences in Allocentric Spatial Working Memory Performance

2021 ◽  
Author(s):  
Adeline Jabès ◽  
Giuliana Klencklen ◽  
Paolo Ruggeri ◽  
Christoph M. Michel ◽  
Pamela Banta Lavenex ◽  
...  
Keyword(s):  
Older Adults ◽  
Working Memory ◽  
Resting State ◽  
Cognitive Aging ◽  
Temporal Dynamics ◽  
Spatial Working Memory ◽  
Brain Activity ◽  
Memory Performance ◽  
Brain Regions ◽  
Age Related

AbstractAlterations of resting-state EEG microstates have been associated with various neurological disorders and behavioral states. Interestingly, age-related differences in EEG microstate organization have also been reported, and it has been suggested that resting-state EEG activity may predict cognitive capacities in healthy individuals across the lifespan. In this exploratory study, we performed a microstate analysis of resting-state brain activity and tested allocentric spatial working memory performance in healthy adult individuals: twenty 25–30-year-olds and twenty-five 64–75-year-olds. We found a lower spatial working memory performance in older adults, as well as age-related differences in the five EEG microstate maps A, B, C, C′ and D, but especially in microstate maps C and C′. These two maps have been linked to neuronal activity in the frontal and parietal brain regions which are associated with working memory and attention, cognitive functions that have been shown to be sensitive to aging. Older adults exhibited lower global explained variance and occurrence of maps C and C′. Moreover, although there was a higher probability to transition from any map towards maps C, C′ and D in young and older adults, this probability was lower in older adults. Finally, although age-related differences in resting-state EEG microstates paralleled differences in allocentric spatial working memory performance, we found no evidence that any individual or combination of resting-state EEG microstate parameter(s) could reliably predict individual spatial working memory performance. Whether the temporal dynamics of EEG microstates may be used to assess healthy cognitive aging from resting-state brain activity requires further investigation.

Label-free non-invasive classification of rice seeds using optical coherence tomography assisted with deep neural network

2021 ◽  
Vol 137 ◽  
pp. 106861
Author(s):  
Deepa Joshi ◽  
Ankit Butola ◽  
Sheetal Raosaheb Kanade ◽  
Dilip K. Prasad ◽  
S.V. Amitha Mithra ◽  
...  
Keyword(s):  
Neural Network ◽  
Optical Coherence Tomography ◽  
Deep Neural Network ◽  
Optical Coherence ◽  
Label Free ◽  
Non Invasive ◽  
Rice Seeds

Reliable detection of atrial fibrillation with a medical wearable under inpatient conditions (CoMMoD-A-fib)

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
M Jacobsen ◽  
T.A Dembek ◽  
A.P Ziakos ◽  
G Kobbe ◽  
M Kollmann ◽  
...  
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Deep Neural Network ◽  
Sampling Rate ◽  
Public Institution ◽  
Funding Source ◽  
Upper Arm ◽  
Recording Time ◽  
Reduced Ejection Fraction ◽  
Non Invasive

Abstract Background Atrial fibrillation (A-fib) is the most common arrhythmia; however, detection of A-fib is a challenge due to irregular occurrence. Purpose Evaluating feasibility and performance of a non-invasive medical wearable for detection of A-fib. Methods In the CoMMoD-A-fib trial admitted patients with a high risk for A-fib carried the wearable and an ECG Holter (control) in parallel over a period of 24 hours under not physically restricted conditions. The wearable with a tight-fit upper arm band employs a photoplethysmography (PPG) technology enabling a high sampling rate. Different algorithms (including a deep neural network) were applied to 5 min PPG datasets for detection of A-fib. Proportion of monitoring time automatically interpretable by algorithms (= interpretable time) was analyzed for influencing factors. Results In 102 inpatients (age 71.0±11.9 years; 52% male) 2306 hours of parallel recording time could be obtained; 1781 hours (77.2%) of these were automatically interpretable by an algorithm analyzing PPG derived intervals. Detection of A-Fib was possible with a sensitivity of 92.7% and specificity of 92.4% (AUC 0.96). Also during physical activity, detection of A-fib was sufficiently possible (sensitivity 90.1% and specificity 91.2%). Usage of the deep neural network improved detection of A-fib further (sensitivity 95.4% and specificity 96.2%). A higher prevalence of heart failure with reduced ejection fraction was observed in patients with a low interpretable time (p=0.080). Conclusion Detection of A-fib by means of an upper arm non-invasive medical wearable with a high resolution is reliably possible under inpatient conditions. Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): Internal grant program (PhD and Dr. rer. nat. Program Biomedicine) of the Faculty of Health at Witten/Herdecke University, Germany. HELIOS Kliniken GmbH (Grant-ID 047476), Germany

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