MEASURING SYNCHRONIZATION IN THE EPILEPTIC BRAIN: A COMPARISON OF DIFFERENT APPROACHES

2007 ◽  
Vol 17 (10) ◽  
pp. 3539-3544 ◽  
Author(s):  
HANNES OSTERHAGE ◽  
FLORIAN MORMANN ◽  
MATTHÄUS STANIEK ◽  
KLAUS LEHNERTZ
Keyword(s):  
Prospective Studies ◽  
Temporal Dynamics ◽  
Brain Regions ◽  
Relative Merit ◽  
Seizure Prediction ◽  
Free Interval ◽  
Linear And Nonlinear ◽  
Spatio Temporal

We investigate the relative merit of different linear and nonlinear synchronization measures for a characterization of the spatio-temporal dynamics of the epileptic process. Analyzing long-lasting multichannel electroencephalographic recordings from more than 20 epilepsy patients we show that all measures are able to identify brain regions of pathological synchronization associated with epilepsy, even during the seizure-free interval, and are able to detect a long-lasting transitional preseizure state. These findings render synchronization measures attractive for future prospective studies on seizure prediction.

Real-time characterization of the spatio-temporal dynamics of deformable mirrors

2016 ◽  
Author(s):  
James Kilpatrick ◽  
Adela Apostol ◽  
Anatoliy Khizhnya ◽  
Vladimir Markov ◽  
Leonid Beresnev
Keyword(s):  
Real Time ◽  
Temporal Dynamics ◽  
Deformable Mirrors ◽  
Spatio Temporal

scholarly journals Uncovering the spatio-temporal dynamics of value-based decision-making in the human brain: a combined fMRI–EEG study

2014 ◽  
Vol 369 (1655) ◽  
pp. 20130473 ◽  
Author(s):  
Tobias Larsen ◽  
John P. O'Doherty
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Temporal Dynamics ◽  
Brain Regions ◽  
Choice Task ◽  
Binary Choice ◽  
Intraparietal Sulcus ◽  
Trial Onset ◽  
Eeg Data ◽  
Spatio Temporal

While there is a growing body of functional magnetic resonance imaging (fMRI) evidence implicating a corpus of brain regions in value-based decision-making in humans, the limited temporal resolution of fMRI cannot address the relative temporal precedence of different brain regions in decision-making. To address this question, we adopted a computational model-based approach to electroencephalography (EEG) data acquired during a simple binary choice task. fMRI data were also acquired from the same participants for source localization. Post-decision value signals emerged 200 ms post-stimulus in a predominantly posterior source in the vicinity of the intraparietal sulcus and posterior temporal lobe cortex, alongside a weaker anterior locus. The signal then shifted to a predominantly anterior locus 850 ms following the trial onset, localized to the ventromedial prefrontal cortex and lateral prefrontal cortex. Comparison signals between unchosen and chosen options emerged late in the trial at 1050 ms in dorsomedial prefrontal cortex, suggesting that such comparison signals may not be directly associated with the decision itself but rather may play a role in post-decision action selection. Taken together, these results provide us new insights into the temporal dynamics of decision-making in the brain, suggesting that for a simple binary choice task, decisions may be encoded predominantly in posterior areas such as intraparietal sulcus, before shifting anteriorly.

scholarly journals STAB: a spatio-temporal cell atlas of the human brain

2020 ◽  
Vol 49 (D1) ◽  
pp. D1029-D1037
Author(s):  
Liting Song ◽  
Shaojun Pan ◽  
Zichao Zhang ◽  
Longhao Jia ◽  
Wei-Hua Chen ◽  
...  
Keyword(s):  
Human Brain ◽  
Single Cell ◽  
Temporal Dynamics ◽  
Cell Types ◽  
Brain Regions ◽  
Molecular Characteristics ◽  
Great Effort ◽  
Brain Functions ◽  
Spatio Temporal ◽  
The Brain

Abstract The human brain is the most complex organ consisting of billions of neuronal and non-neuronal cells that are organized into distinct anatomical and functional regions. Elucidating the cellular and transcriptome architecture underlying the brain is crucial for understanding brain functions and brain disorders. Thanks to the single-cell RNA sequencing technologies, it is becoming possible to dissect the cellular compositions of the brain. Although great effort has been made to explore the transcriptome architecture of the human brain, a comprehensive database with dynamic cellular compositions and molecular characteristics of the human brain during the lifespan is still not available. Here, we present STAB (a Spatio-Temporal cell Atlas of the human Brain), a database consists of single-cell transcriptomes across multiple brain regions and developmental periods. Right now, STAB contains single-cell gene expression profiling of 42 cell subtypes across 20 brain regions and 11 developmental periods. With STAB, the landscape of cell types and their regional heterogeneity and temporal dynamics across the human brain can be clearly seen, which can help to understand both the development of the normal human brain and the etiology of neuropsychiatric disorders. STAB is available at http://stab.comp-sysbio.org.

scholarly journals Large scale spatio-temporal patterns of road development in the Amazon rainforest

2013 ◽  
Vol 41 (3) ◽  
pp. 253-264 ◽  
Author(s):  
SADIA E. AHMED ◽  
ROBERT M. EWERS ◽  
MATTHEW J. SMITH
Keyword(s):  
Large Scale ◽  
Temporal Dynamics ◽  
Temporal Patterns ◽  
Biodiversity Loss ◽  
Road Density ◽  
Road Development ◽  
Spatio Temporal ◽  
Dominant Direction ◽  
Dynamics Of Populations

SUMMARYThere is burgeoning interest in predicting road development because of the wide ranging important socioeconomic and environmental issues that roads present, including the close links between road development, deforestation and biodiversity loss. This is especially the case in developing nations, which are high in natural resources, where road development is rapid and often not centrally managed. Characterization of large scale spatio-temporal patterns in road network development has been greatly overlooked to date. This paper examines the spatio-temporal dynamics of road density across the Brazilian Amazon and assesses the relative contributions of local versus neighbourhood effects for temporal changes in road density at regional scales. To achieve this, a combination of statistical analyses and model-data fusion techniques inspired by studies of spatio-temporal dynamics of populations in ecology and epidemiology were used. The emergent development may be approximated by local growth that is logistic through time and directional dispersal. The current rates and dominant direction of development may be inferred, by assuming that roads develop at a rate of 55 km per year. Large areas of the Amazon will be subject to extensive anthropogenic change should the observed patterns of road development continue.

scholarly journals Similarity-based fusion of MEG and fMRI reveals spatio-temporal dynamics in human cortex during visual object recognition

2015 ◽  
Author(s):  
Radoslaw Cichy ◽  
Dimitrios Pantazis ◽  
Aude Oliva
Keyword(s):  
Object Recognition ◽  
Temporal Dynamics ◽  
Imaging Techniques ◽  
Visual Object ◽  
Data Sets ◽  
Visual Object Recognition ◽  
Occipital Pole ◽  
Spatio Temporal ◽  
The Brain

Every human cognitive function, such as visual object recognition, is realized in a complex spatio-temporal activity pattern in the brain. Current brain imaging techniques in isolation cannot resolve the brain's spatio-temporal dynamics because they provide either high spatial or temporal resolution but not both. To overcome this limitation, we developed a new integration approach that uses representational similarities to combine measurements from different imaging modalities - magnetoencephalography (MEG) and functional MRI (fMRI) - to yield a spatially and temporally integrated characterization of neuronal activation. Applying this approach to two independent MEG-fMRI data sets, we observed that neural activity first emerged in the occipital pole at 50-80ms, before spreading rapidly and progressively in the anterior direction along the ventral and dorsal visual streams. These results provide a novel and comprehensive, spatio-temporally resolved view of the rapid neural dynamics during the first few hundred milliseconds of object vision. They further demonstrate the feasibility of spatially unbiased representational similarity based fusion of MEG and fMRI, promising new insights into how the brain computes complex cognitive functions.

scholarly journals Epileptic Seizure Prediction Through Machine Learning and Spatio-Temporal Features Based Time Series Analysis of Intracranial Electroencephalogram Data

2019 ◽  
Vol 9 (1) ◽  
pp. 4763-4770
Keyword(s):  
Machine Learning ◽  
Temporal Dynamics ◽  
Class Imbalance ◽  
Training Data ◽  
Learning Approaches ◽  
Seizure Prediction ◽  
Buffer Time ◽  
Temporal Features ◽  
Emergency Medication ◽  
Spatio Temporal

Epilepsy is a group of neurological disorders identifiable by infrequent but recurrent seizures. Seizure prediction is widely recognized as a significant problem in the neuroscience domain. Developing a Brain-Computer Interface (BCI) for seizure prediction can provide an alert to the patient, providing a buffer time to get the necessary emergency medication or at least be able to call for help, thus improving the quality of life of the patients. A considerable number of clinical studies presented evidence of symptoms (patterns) before seizure episodes and thus, there is large research on seizure prediction, however, there is very little existing literature that illustrates the use of structured processes in machine learning for predicting seizures. Limited training data and class imbalance (EEG segments corresponding to preictal phase, the duration just before the seizure, to about an hour prior to the episode, are usually in a tiny minority) are a few challenges that need to be addressed when employing machine learning for this task. In this paper we present a comparative study of various machine learning approaches that can be used for classification of EEG signals into preictal and interictal (Interictal is the time between seizures) using the features extracted from the intracranial EEG. Publicly available data has been used for this purpose for both human and canine subjects. After data pre-processing and extensive feature extraction, different models are trained and are effectively used to analyze the temporal dynamics of the brain (interictal and preictal) in affected subjects. We present the improved results for various classification algorithms, with AUROC values of best classification models at 0.99.

scholarly journals Characterization and spatio-temporal dynamics of Cylindrospermopsis raciborski in an Amazonian Lagoon, Ecuador.

Enfoque UTE ◽  
2018 ◽  
Vol 9 (2) ◽  
pp. 117-124
Author(s):  
Judith Venegas ◽  
Pablo Castillejo Pons ◽  
Susana Chamorro ◽  
Ivonne Carrillo ◽  
Eduardo Lobo
Keyword(s):  
Fresh Water ◽  
Environmental Conditions ◽  
Scientific Literature ◽  
Temporal Dynamics ◽  
Cylindrospermopsis Raciborskii ◽  
Human Consumption ◽  
Distribution Maps ◽  
Spatio Temporal ◽  
Recreational Use

The cyanobacteria Cylindrospermopsis raciborskii, is a fresh water ubiquitous species from tropical to temperate weather. It is potentially capable of producing toxins.  Thus it is necessary to monitor its presence in fresh waters associated to recreational use activities and human consumption. There are official reports and one thesis reporting the presence of C. raciborskii in Ecuador. Nevertheless, this country does not appear in the latest distribution maps of this species in the scientific literature. In this article, we report the presence of C. raciborskii in Ecuador, together with the characterization of the environmental conditions of one of the habitats where this species is present: the Limoncocha lagoon, province of Sucumbíos.

scholarly journals Spatio-temporal dynamics of face perception

2019 ◽  
Author(s):  
I. Muukkonen ◽  
K. Ölander ◽  
J. Numminen ◽  
V.R. Salmela
Keyword(s):  
Temporal Dynamics ◽  
Theoretical Models ◽  
Brain Regions ◽  
Support Vector ◽  
Functional Roles ◽  
Emotional Distance ◽  
Early Visual Cortex ◽  
Spatio Temporal ◽  
Information Matrices ◽  
Combine Information

AbstractThe temporal and spatial neural processing of faces have been studied rigorously, but few studies have unified these dimensions to reveal the spatio-temporal dynamics postulated by the models of face processing. We used support vector machine decoding and representational similarity analysis to combine information from different locations (fMRI), timepoints (EEG), and theoretical models. By correlating information matrices derived from pair-wise decodings of neural responses to different facial expressions (neutral, happy, fearful, angry), we found early EEG timepoints (110-150 ms) to match fMRI data from early visual cortex (EVC), and later timepoints (170 – 250 ms) to match data from occipital and fusiform face areas (OFA/FFA) and posterior superior temporal sulcus (pSTS). The earliest correlations were driven by information from happy faces, and the later by more accurate decoding of fearful and angry faces. Model comparisons revealed systematic changes along the processing hierarchy, from emotional distance and visual feature coding in EVC to coding of intensity of expressions in right pSTS. The results highlight the importance of multimodal approach for understanding functional roles of different brain regions.

Characterization of spatio-temporal dynamics of deformable mirrors

2018 ◽  
Author(s):  
Peter Morrison ◽  
Glenn Osborne ◽  
John Siegenthaler ◽  
Joni Pentony ◽  
Vladimir . Markov ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Deformable Mirrors ◽  
Spatio Temporal

scholarly journals The Dual-Task Cost Is Due to Neural Interferences Disrupting the Optimal Spatio-Temporal Dynamics of the Competing Tasks

2021 ◽  
Vol 15 ◽  
Author(s):  
Diego Mac-Auliffe ◽  
Benoit Chatard ◽  
Mathilde Petton ◽  
Anne-Claire Croizé ◽  
Florian Sipp ◽  
...  
Keyword(s):  
Dual Task ◽  
Temporal Dynamics ◽  
Memory Task ◽  
Brain Regions ◽  
Anterior Cingulate ◽  
Long Term Memory ◽  
Dorsal Anterior Cingulate Cortex ◽  
Ample Evidence ◽  
Neural Origin ◽  
Spatio Temporal

Dual-tasking is extremely prominent nowadays, despite ample evidence that it comes with a performance cost: the Dual-Task (DT) cost. Neuroimaging studies have established that tasks are more likely to interfere if they rely on common brain regions, but the precise neural origin of the DT cost has proven elusive so far, mostly because fMRI does not record neural activity directly and cannot reveal the key effect of timing, and how the spatio-temporal neural dynamics of the tasks coincide. Recently, DT electrophysiological studies in monkeys have recorded neural populations shared by the two tasks with millisecond precision to provide a much finer understanding of the origin of the DT cost. We used a similar approach in humans, with intracranial EEG, to assess the neural origin of the DT cost in a particularly challenging naturalistic paradigm which required accurate motor responses to frequent visual stimuli (task T1) and the retrieval of information from long-term memory (task T2), as when answering passengers’ questions while driving. We found that T2 elicited neuroelectric interferences in the gamma-band (>40 Hz), in key regions of the T1 network including the Multiple Demand Network. They reproduced the effect of disruptive electrocortical stimulations to create a situation of dynamical incompatibility, which might explain the DT cost. Yet, participants were able to flexibly adapt their strategy to minimize interference, and most surprisingly, reduce the reliance of T1 on key regions of the executive control network-the anterior insula and the dorsal anterior cingulate cortex-with no performance decrement.

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