scholarly journals Lingering representations of stimulus history at encoding influence recall organization

2016 ◽  
Author(s):  
Stephanie C.Y. Chan ◽  
Marissa C. Applegate ◽  
Neal W Morton ◽  
Sean M. Polyn ◽  
Kenneth A. Norman
Keyword(s):  
Pattern Analysis ◽  
Fmri Data ◽  
Temporal Context ◽  
The Brain

Several prominent theories posit that information about recent experiences lingers in the brain and organizes memories for current experiences, by forming a temporal context that is linked to those memories at encoding. According to these theories, if the thoughts preceding an experience X resemble the thoughts preceding an experience Y, then X and Y should show an elevated probability of being recalled together. We tested this prediction by using multi-voxel pattern analysis (MVPA) of fMRI data to measure neural evidence for lingering processing of preceding stimuli. As predicted, memories encoded with similar lingering thoughts (about the category of preceding stimuli) were more likely to be recalled together, thereby showing that the "fading embers" of previous stimuli help to organize recall.

scholarly journals Resting-state brain activity can predict target-independent aptitude in fMRI-neurofeedback training

2021 ◽  
Author(s):  
Takashi Nakano ◽  
Masahiro Takamura ◽  
Haruki Nishimura ◽  
Maro Machizawa ◽  
Naho Ichikawa ◽  
...  
Keyword(s):  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Fmri Data ◽  
Independent Test ◽  
The Individual ◽  
The Brain

AbstractNeurofeedback (NF) aptitude, which refers to an individual’s ability to change its brain activity through NF training, has been reported to vary significantly from person to person. The prediction of individual NF aptitudes is critical in clinical NF applications. In the present study, we extracted the resting-state functional brain connectivity (FC) markers of NF aptitude independent of NF-targeting brain regions. We combined the data in fMRI-NF studies targeting four different brain regions at two independent sites (obtained from 59 healthy adults and six patients with major depressive disorder) to collect the resting-state fMRI data associated with aptitude scores in subsequent fMRI-NF training. We then trained the regression models to predict the individual NF aptitude scores from the resting-state fMRI data using a discovery dataset from one site and identified six resting-state FCs that predicted NF aptitude. Next we validated the prediction model using independent test data from another site. The result showed that the posterior cingulate cortex was the functional hub among the brain regions and formed predictive resting-state FCs, suggesting NF aptitude may be involved in the attentional mode-orientation modulation system’s characteristics in task-free resting-state brain activity.

scholarly journals Assessing hippocampal functional reserve in temporal lobe epilepsy: A multi-voxel pattern analysis of fMRI data

2013 ◽  
Vol 105 (1-2) ◽  
pp. 140-149 ◽  
Author(s):  
Heidi M. Bonnici ◽  
Meneka Sidhu ◽  
Martin J. Chadwick ◽  
John S. Duncan ◽  
Eleanor A. Maguire
Keyword(s):  
Temporal Lobe Epilepsy ◽  
Temporal Lobe ◽  
Pattern Analysis ◽  
Fmri Data ◽  
Functional Reserve

An Improved RV Based Fuzzy Cluster Analysis (IRVFCA) Method for Task-Related Functional MRI Data Analysis

2013 ◽  
Vol 427-429 ◽  
pp. 1570-1573
Author(s):  
Han Wang ◽  
Wei Ming Zeng
Keyword(s):  
Time Course ◽  
Experimental Validation ◽  
Brain Volume ◽  
Pearson Correlation ◽  
Fuzzy Cluster ◽  
Fmri Data ◽  
Data Sets ◽  
Fuzzy Cluster Analysis ◽  
Fuzzy Clustering Method ◽  
The Brain

Functional magnetic resonance imaging (fMRI) has become one of the important tools of functional connectivity studies of the human brain. Fuzzy clustering method (FCM) is a common method for analysis of FMRI data. Traditional FCA methods measure the similarity between the BOLD time course of a centroid and the ones of all other voxels in the brain on the basis of Pearson correlation coefficient. This article puts forward a multi-voxel-based similarity measure, an improved RV (IRV) measure, which takes the hypothesis into account that the function homogeneous voxels of brain volume are spatially clustered within a local region. Experimental validation is presented through four visual fMRI data sets which shows that the IRVFCA method not only has improved the speed of FCA, but has comparatively raised the accuracy of the method.

Methods of Predicting the Brain Activity Based on Noun

2013 ◽  
Vol 347-350 ◽  
pp. 2516-2520
Author(s):  
Jian Hua Jiang ◽  
Xu Yu ◽  
Zhi Xing Huang
Keyword(s):  
Brain Activity ◽  
Past Research ◽  
Neural Representation ◽  
Fmri Data ◽  
Semantic Features ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
The Past ◽  
Machine Learning Methods ◽  
The Brain

Over the last decade, functional magnetic resonance imaging (fMRI) has become a primary tool to predict the brain activity.During the past research, researchers transfer the focus from the picture to the word.The results of these researches are relatively successful. In this paper, several typical methods which are machine learning methods are introduced. And most of the methods are by using fMRI data associated with words features. The semantic features (properties or factors) support words neural representation, and have a certain commonality in the people.The purpose of the application of these methods is used for prediction or classification.

scholarly journals Multi-voxel pattern analysis of fMRI data predicts clinical symptom severity

NeuroImage ◽  
2011 ◽  
Vol 57 (1) ◽  
pp. 113-123 ◽  
Author(s):  
Marc N. Coutanche ◽  
Sharon L. Thompson-Schill ◽  
Robert T. Schultz
Keyword(s):  
Clinical Symptom ◽  
Symptom Severity ◽  
Pattern Analysis ◽  
Fmri Data

scholarly journals Predictability changes what we remember in familiar temporal contexts

2018 ◽  
Author(s):  
Hyojeong Kim ◽  
Margaret L. Schlichting ◽  
Alison R. Preston ◽  
Jarrod A. Lewis-Peacock
Keyword(s):  
Human Brain ◽  
Pattern Classification ◽  
Prediction Error ◽  
Fmri Data ◽  
The Past ◽  
The Future ◽  
Abstract Level ◽  
Episodic Memories ◽  
The Brain

AbstractThe human brain constantly anticipates the future based on memories of the past. Encountering a familiar situation reactivates memory of previous encounters which can trigger a prediction of what comes next to facilitate responsiveness. However, a prediction error can lead to pruning of the offending memory, a process that weakens its representation in the brain and leads to forgetting. Our goal in this study was to evaluate whether memories are spared from pruning in situations that allow for more abstract yet reliable predictions. We hypothesized that when the category, but not the identity, of a new stimulus can be anticipated, this will reduce pruning of existing memories and also reduce encoding of the specifics of new memories. Participants viewed a sequence of objects, some of which reappeared multiple times (“cues”), followed always by novel items. Half of the cues were followed by new items from different (unpredictable) categories, while others were followed by new items from a single (predictable) category. Pattern classification of fMRI data was used to identify category-specific predictions after each cue. Pruning was observed only in unpredictable contexts, while encoding of new items suffered more in predictable contexts. These findings demonstrate that how episodic memories are updated is influenced by the reliability of abstract-level predictions in familiar contexts.

scholarly journals Local Discriminant Hyperalignment for multi-subject fMRI data alignment

2016 ◽  
Author(s):  
Muhammad Yousefnezhad ◽  
Daoqiang Zhang
Keyword(s):  
Discriminant Analysis ◽  
Canonical Correlation ◽  
State Of The Art ◽  
Experimental Studies ◽  
Superior Performance ◽  
Fmri Data ◽  
Data Alignment ◽  
Cognitive States ◽  
Multivariate Pattern ◽  
The Brain

AbstractMultivariate Pattern (MVP) classification can map different cognitive states to the brain tasks. One of the main challenges in MVP analysis is validating the generated results across subjects. However, analyzing multi-subject fMRI data requires accurate functional alignments between neuronal activities of different subjects, which can rapidly increase the performance and robustness of the final results. Hyperalignment (HA) is one of the most effective functional alignment methods, which can be mathematically formulated by the Canonical Correlation Analysis (CCA) methods. Since HA mostly uses the unsupervised CCA techniques, its solution may not be optimized for MVP analysis. By incorporating the idea of Local Discriminant Analysis (LDA) into CCA, this paper proposes Local Discriminant Hyperalignment (LDHA) as a novel supervised HA method, which can provide better functional alignment for MVP analysis. Indeed, the locality is defined based on the stimuli categories in the train-set, where the correlation between all stimuli in the same category will be maximized and the correlation between distinct categories of stimuli approaches to near zero. Experimental studies on multi-subject MVP analysis confirm that the LDHA method achieves superior performance to other state-of-the-art HA algorithms.

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