Time-frequency time-space long short-term memory networks for image classification of histopathological tissue.
Ontology highlight
ABSTRACT:
SUBMITTER: Pham TD
PROVIDER: S-EPMC8249635 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The frontal lobes control wide-ranging cognitive functions; however, functional subdivisions of human frontal cortex are only coarsely mapped. Here, functional magnetic resonance imaging reveals two distinct visual-biased attention regions in lateral frontal cortex, superior precentral sulcus (sPCS) and inferior precentral sulcus (iPCS), anatomically interdigitated with two auditory-biased attention regions, transverse gyrus intersecting precentral sulcus (tgPCS) and caudal inferior frontal sulcus (cIFS). Intrinsic functional connectivity analysis demonstrates that sPCS and iPCS fall within a broad visual-attention network, while tgPCS and cIFS fall within a broad auditory-attention network. Interestingly, we observe that spatial and temporal short-term memory (STM), respectively, recruit visual and auditory attention networks in the frontal lobe, independent of sensory modality. These findings not only demonstrate that both sensory modality and information domain influence frontal lobe functional organization, they also demonstrate that spatial processing co-localizes with visual processing and that temporal processing co-localizes with auditory processing in lateral frontal cortex.
| S-EPMC4545499 | biostudies-literature
Project description:Automated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time-frequency and time-space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.
| S-EPMC7994826 | biostudies-literature
Project description:Noninvasive neuroimaging studies have revealed a network of brain regions that activate during human memory encoding; however, the relative timing of such activations remains unknown. Here we used intracranially recorded high-frequency activity (HFA) to first identify regions that activate during successful encoding. Then, we leveraged the high-temporal precision of HFA to investigate the timing of such activations. We found that memory encoding invokes two spatiotemporally distinct activations: early increases in HFA that involve the ventral visual pathway as well as the medial temporal lobe and late increases in HFA that involve the left inferior frontal gyrus, left posterior parietal cortex, and left ventrolateral temporal cortex. We speculate that these activations reflect higher-order visual processing and top-down modulation of attention/semantic information, respectively.
| S-EPMC4289670 | biostudies-literature
Project description:Are the information processing steps that support short-term sensory memory common to all the senses? Systematic, psychophysical comparison requires identical experimental paradigms and comparable stimuli, which can be challenging to obtain across modalities. Participants performed a recognition memory task with auditory and visual stimuli that were comparable in complexity and in their neural representations at early stages of cortical processing. The visual stimuli were static and moving Gaussian-windowed, oriented, sinusoidal gratings (Gabor patches); the auditory stimuli were broadband sounds whose frequency content varied sinusoidally over time (moving ripples). Parallel effects on recognition memory were seen for number of items to be remembered, retention interval, and serial position. Further, regardless of modality, predicting an item's recognizability requires taking account of (1) the probe's similarity to the remembered list items (summed similarity), and (2) the similarity between the items in memory (inter-item homogeneity). A model incorporating both these factors gives a good fit to recognition memory data for auditory as well as visual stimuli. In addition, we present the first demonstration of the orthogonality of summed similarity and inter-item homogeneity effects. These data imply that auditory and visual representations undergo very similar transformations while they are encoded and retrieved from memory.
| S-EPMC1800308 | biostudies-literature
Project description:Brain-computer interface (BCI) systems having the ability to classify brain waves with greater accuracy are highly desirable. To this end, a number of techniques have been proposed aiming to be able to classify brain waves with high accuracy. However, the ability to classify brain waves and its implementation in real-time is still limited. In this study, we introduce a novel scheme for classifying motor imagery (MI) tasks using electroencephalography (EEG) signal that can be implemented in real-time having high classification accuracy between different MI tasks. We propose a new predictor, OPTICAL, that uses a combination of common spatial pattern (CSP) and long short-term memory (LSTM) network for obtaining improved MI EEG signal classification. A sliding window approach is proposed to obtain the time-series input from the spatially filtered data, which becomes input to the LSTM network. Moreover, instead of using LSTM directly for classification, we use regression based output of the LSTM network as one of the features for classification. On the other hand, linear discriminant analysis (LDA) is used to reduce the dimensionality of the CSP variance based features. The features in the reduced dimensional plane after performing LDA are used as input to the support vector machine (SVM) classifier together with the regression based feature obtained from the LSTM network. The regression based feature further boosts the performance of the proposed OPTICAL predictor. OPTICAL showed significant improvement in the ability to accurately classify left and right-hand MI tasks on two publically available datasets. The improvements in the average misclassification rates are 3.09% and 2.07% for BCI Competition IV Dataset I and GigaDB dataset, respectively. The Matlab code is available at https://github.com/ShiuKumar/OPTICAL .
| S-EPMC6591300 | biostudies-literature
Project description:Cell assemblies have long been thought to be associated with brain rhythms, notably the gamma rhythm. Here, we use a computational model to show that the beta1 frequency band, as found in rat association cortex, has properties complementary to the gamma band for the creation and manipulation of cell assemblies. We focus on the ability of the beta1 rhythm to respond differently to familiar and novel stimuli, and to provide a framework for combining the two. Simulations predict that assemblies of superficial layer pyramidal cells can be maintained in the absence of continuing input or synaptic plasticity. Instead, the formation of these assemblies relies on the nesting of activity within a beta1 rhythm. In addition, cells receiving further input after assembly formation produce coexistent spiking activity, unlike the competitive spiking activity characteristic of assembly formation with gamma rhythms.
| S-EPMC3048142 | biostudies-literature
Project description:The functional relationship between correct response probability and response time is investigated in data sets from Rubin, Hinton and Wenzel, J Exp Psychol Learn Mem Cogn 25:1161-1176, 1999 and Anderson, J Exp Psychol [Hum Learn] 7:326-343, 1981. The two measures are linearly related through stimulus presentation lags from 0 to 594 s in the former experiment and for repeated learning of words in the latter. The Tagging/Retagging interpretation of short term memory is introduced to explain this linear relationship. At stimulus presentation the words are tagged. This tagging level drops slowly with time. When a probe word is reintroduced the tagging level has to increase for the word to be properly identified leading to a delay in response time. The tagging time is related to the meaningfulness of the words used-the more meaningful the word the longer the tagging time. After stimulus presentation the tagging level drops in a logarithmic fashion to 50% after 10 s and to 20% after 240 s. The incorrect recall and recognition times saturate in the Rubin et al. data set (they are not linear for large time lags), suggesting a limited time to search the short term memory structure: the search time for recall of unusual words is 1.7 s. For recognition of nonsense words the corresponding time is about 0.4 s, similar to the 0.243 s found in Cavanagh (1972).
| S-EPMC2585621 | biostudies-other
Project description:Cognitive deficits are a recognised component of Parkinson's disease (PD). However, particularly within the domain of short-term memory, it is unclear whether these impairments are masked, or caused, by patients' dopaminergic medication. The effect of medication on pure maintenance in PD patients has rarely been explored, with most assessments examining maintenance intercalated between other executive tasks. Moreover, few studies have utilised methods that can measure the quality of mental representations, which can enable the decomposition of recall errors into their underlying neurocognitive components. Here, we fill this gap by examining pure maintenance in PD patients in high and low dopaminergic states. Participants had to encode the orientation of two stimuli and reproduce these orientations after a short (2 s) or long (8 s) delay. In addition, we also examined the performance of healthy, age-matched older adults to contextualise these effects and determine whether PD represents an exacerbation of the normal ageing process. Patients showed improved recall OFF compared to ON their dopaminergic medication, but only for long-duration trials. Moreover, PD patients OFF their medication actually performed at a level superior to age-matched controls, indicative of a paradoxical enhancement of memory in the low dopaminergic state. The application of a probabilistic model of response selection suggested that PD patients made fewer misbinding errors in the low, compared with high, dopaminergic state for longer-delay trials. Thus, unexpectedly, the mechanisms that prevent memoranda from being corrupted by misbinding over time appear to be enhanced in PD patients OFF dopaminergic medication. Possible explanations for this paradoxical effect are discussed.
| S-EPMC6683156 | biostudies-literature
Project description:Network functioning during cognitive tasks is of major interest in Alzheimer's disease (AD). Cognitive functioning in AD includes variable performance in short-term memory (STM). In most studies, the verbal STM functioning in AD patients has been interpreted within the phonological loop subsystem of Baddeley's working memory model. An alternative account considers that domain-general attentional processes explain the involvement of frontoparietal networks in verbal STM beside the functioning of modality-specific subsystems. In this study, we assessed the functional integrity of the dorsal attention network (involved in task-related attention) and the ventral attention network (involved in stimulus-driven attention) by varying attentional control demands in a STM task. Thirty-five AD patients and twenty controls in the seventies performed an fMRI STM task. Variation in load (five versus two items) allowed the dorsal (DAN) and ventral attention networks (VAN) to be studied. ANOVA revealed that performance decreased with increased load in both groups. AD patients performed slightly worse than controls, but accuracy remained above 70% in all patients. Statistical analysis of fMRI brain images revealed DAN activation for high load in both groups. There was no between-group difference or common activation for low compared to high load conditions. Psychophysiological interaction showed a negative relationship between the DAN and the VAN for high versus low load conditions in patients. In conclusion, the DAN remained activated and connected to the VAN in mild AD patients who succeeded in performing an fMRI verbal STM task. DAN was necessary for the task, but not sufficient to reach normal performance. Slightly lower performance in early AD patients compared to controls might be related to maintained bottom-up attention to distractors, to decrease in executive functions, to impaired phonological processing or to reduced capacity in serial order processing.
| S-EPMC6580312 | biostudies-literature
Project description:Time series are an important data class that includes recordings ranging from radio emissions, seismic activity, global positioning data, and stock prices to EEG measurements, vital signs, and voice recordings. Rapid growth in sensor and recording technologies is increasing the production of time series data and the importance of rapid, accurate analyses. Time series data are commonly analyzed using time-varying spectral methods to characterize their nonstationary and often oscillatory structure. Current methods provide local estimates of data features. However, they do not offer a statistical inference framework that applies to the entire time series. The important advances that we report are state-space multitaper (SS-MT) methods, which provide a statistical inference framework for time-varying spectral analysis of nonstationary time series. We model nonstationary time series as a sequence of second-order stationary Gaussian processes defined on nonoverlapping intervals. We use a frequency-domain random-walk model to relate the spectral representations of the Gaussian processes across intervals. The SS-MT algorithm efficiently computes spectral updates using parallel 1D complex Kalman filters. An expectation-maximization algorithm computes static and dynamic model parameter estimates. We test the framework in time-varying spectral analyses of simulated time series and EEG recordings from patients receiving general anesthesia. Relative to standard multitaper (MT), SS-MT gave enhanced spectral resolution and noise reduction ([Formula: see text]10 dB) and allowed statistical comparisons of spectral properties among arbitrary time series segments. SS-MT also extracts time-domain estimates of signal components. The SS-MT paradigm is a broadly applicable, empirical Bayes' framework for statistical inference that can help ensure accurate, reproducible findings from nonstationary time series analyses.
| S-EPMC5776784 | biostudies-literature