NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data

Lu, Zitong; Ku, Yixuan

doi:10.3389/fninf.2020.563669

Cited by 32 publications

(38 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, we calculated RDM with codes from Neurora (Z. Lu&Ku, 2020b), and then extracted the corresponding neural pattern dissimilarity value from it.(Z. Lu & Ku, 2020b).…”

Section: Figure * Eeg -Tpds and Stpdsmentioning

confidence: 99%

“…We do statistics on neural pattern dissimilarity(NPDS), which calculate pair t-test at each time point and draws a heat map, and the area of the picture framed means significant difference(Z. Lu&Ku, 2020b).(Z. Lu & Ku, 2020b)。 It can be found that in the case of the whole brain, in the middle and late stage, there are significant differences around 0.4s and 0.6s.. This is consistent with the extraction of semantic memory in time, which shows that there are differences in the extraction of this part: STPDS(spaced -once) > STPDS(massed -once).…”

Section: Figure Neural Pattern Dissimilaritymentioning

confidence: 99%

“…Specifically, we calculated RDM with codes from Neurora (Z. Lu & Ku, 2020) and then extracted the corresponding neural pattern dissimilarity value from it. Then every subject got two lists of values of dissimilarity, which can be drawn like figure17.…”

Section: Tpds and Stpdsmentioning

confidence: 99%

“…Firstly, we do statistics on neural pattern dissimilarity (NPDS), which calculate a pair ttest at each time point and draw a heat map, and the area of the picture framed means significant difference (Z. Lu & Ku, 2020). Here we label consecutive time points (≥5), which include p less than 0.01, as significant cluster figure19.…”

Section: Tpds and Stpdsmentioning

confidence: 99%

“…On the other hand, we draw lessons from the previous neural pattern similarity (NPS) (Cavanagh et al, 2018;Haxby, 2001;Z. Lu & Ku, 2020) and Spatiotemporal pattern similarity (STPS) (Y.…”

Section: Selection and Introduction Of Eeg Indicatorsmentioning

confidence: 99%

See 4 more Smart Citations

Greater Neural Pattern Dissimilarity and Higher Meaningfulness in the Retrieval Stage are Enhanced by Spaced Learning

Liang

2021

Preprint

View full text Add to dashboard Cite

Spaced learning research aims to find the best learning interval to improve people's learning efficiency. Although it has been studied for many years, people do not know the physiological mechanism behind it clearly. Some neural representation studies have found that under the condition of spaced learning, the representation similarity of two encoding increases, and believe that this conflicts with the classical encoding variability theory. In this experiment, we used a new experimental paradigm and a longer lag to let the Chinese university students to learn English-Chinese word pairs, and we introduced the idea of meaningful learning into the learning stage to improve the traditional keyword method experiment task. Finally, by comparing the difference (spaced learning - one-time learning) vs. difference (massed learning - one-time learning) in the final test (retrieval) stage, we get different results: there is no significant difference in ERP, At the same time, the neural spatial pattern dissimilarity (STPDS) had significant results in the parietal lobe of 400ms and the right frontal lobe of 600ms. However, we believe that there is no direct contradiction between the two experimental evidence. On the contrary, they reflect different aspects of the process of spaced learning. Based on the evidence of different neural representation evidence of encoding and retrieval, this paper presents an idea of reintegrating three classical theories, eliminates the opposition between encoding variability and defective processing, and summarizes and classifies the experimental paradigm of spaced learning.

show abstract

“…Specifically, we calculated RDM with codes from Neurora (Z. Lu&Ku, 2020b), and then extracted the corresponding neural pattern dissimilarity value from it.(Z. Lu & Ku, 2020b).…”

Section: Figure * Eeg -Tpds and Stpdsmentioning

confidence: 99%

Section: Figure Neural Pattern Dissimilaritymentioning

confidence: 99%

Section: Tpds and Stpdsmentioning

confidence: 99%

Section: Tpds and Stpdsmentioning

confidence: 99%

“…On the other hand, we draw lessons from the previous neural pattern similarity (NPS) (Cavanagh et al, 2018;Haxby, 2001;Z. Lu & Ku, 2020) and Spatiotemporal pattern similarity (STPS) (Y.…”

Section: Selection and Introduction Of Eeg Indicatorsmentioning

confidence: 99%

See 3 more Smart Citations

Greater Neural Pattern Dissimilarity and Higher Meaningfulness in the Retrieval Stage are Enhanced by Spaced Learning

Liang

2021

Preprint

View full text Add to dashboard Cite

show abstract

An easy‐to‐follow handbook for electroencephalogram data analysis with Python

Lu,

Li,

Nie

et al. 2024

Brain-X

Self Cite

View full text Add to dashboard Cite

This easy‐to‐follow handbook offers a straightforward guide to electroencephalogram (EEG) analysis using Python, aimed at all EEG researchers in cognitive neuroscience and related fields. It spans from single‐subject data preprocessing to advanced multisubject analyses. This handbook contains four chapters: Preprocessing Single‐Subject Data, Basic Python Data Operations, Multiple‐Subject Analysis, and Advanced EEG Analysis. The Preprocessing Single‐Subject Data chapter provides a standardized procedure for single‐subject EEG data preprocessing, primarily using the MNE‐Python package. The Basic Python Data Operations chapter introduces essential Python operations for EEG data handling, including data reading, storage, and statistical analysis. The Multiple‐Subject Analysis chapter guides readers on performing event‐related potential and time‐frequency analyses and visualizing outcomes through examples from a face perception task dataset. The Advanced EEG Analysis chapter explores three advanced analysis methodologies, Classification‐based decoding, Representational Similarity Analysis, and Inverted Encoding Model, through practical examples from a visual working memory task dataset using NeuroRA and other powerful packages. We designed our handbook for easy comprehension to be an essential tool for anyone delving into EEG data analysis with Python (GitHub website: https://github.com/ZitongLu1996/Python‐EEG‐Handbook; For Chinese version: https://github.com/ZitongLu1996/Python‐EEG‐Handbook‐CN).

show abstract

Every individual makes a difference: A trinity derived from linking individual brain morphometry, connectivity and mentalising ability

Dong

et al. 2023

Human Brain Mapping

View full text Add to dashboard Cite

Mentalising ability, indexed as the ability to understand others' beliefs, feelings, intentions, thoughts and traits, is a pivotal and fundamental component of human social cognition. However, considering the multifaceted nature of mentalising ability, little research has focused on characterising individual differences in different mentalising components. And even less research has been devoted to investigating how the variance in the structural and functional patterns of the amygdala and hippocampus, two vital subcortical regions of the “social brain”, are related to inter‐individual variability in mentalising ability. Here, as a first step toward filling these gaps, we exploited inter‐subject representational similarity analysis (IS‐RSA) to assess relationships between amygdala and hippocampal morphometry (surface‐based multivariate morphometry statistics, MMS), connectivity (resting‐state functional connectivity, rs‐FC) and mentalising ability (interactive mentalisation questionnaire [IMQ] scores) across the participants ( ). In IS‐RSA, we proposed a novel pipeline, that is, computing patching and pooling operations‐based surface distance (CPP‐SD), to obtain a decent representation for high‐dimensional MMS data. On this basis, we found significant correlations (i.e., second‐order isomorphisms) between these three distinct modalities, indicating that a trinity existed in idiosyncratic patterns of brain morphometry, connectivity and mentalising ability. Notably, a region‐related mentalising specificity emerged from these associations: self‐self and self‐other mentalisation are more related to the hippocampus, while other‐self mentalisation shows a closer link with the amygdala. Furthermore, by utilising the dyadic regression analysis, we observed significant interactions such that subject pairs with similar morphometry had even greater mentalising similarity if they were also similar in rs‐FC. Altogether, we demonstrated the feasibility and illustrated the promise of using IS‐RSA to study individual differences, deepening our understanding of how individual brains give rise to their mentalising abilities.

show abstract

NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data

Cited by 32 publications

References 53 publications

Greater Neural Pattern Dissimilarity and Higher Meaningfulness in the Retrieval Stage are Enhanced by Spaced Learning

Greater Neural Pattern Dissimilarity and Higher Meaningfulness in the Retrieval Stage are Enhanced by Spaced Learning

An easy‐to‐follow handbook for electroencephalogram data analysis with Python

Every individual makes a difference: A trinity derived from linking individual brain morphometry, connectivity and mentalising ability

Contact Info

Product

Resources

About