Submission guidelines for abstract submission to the Neuroscience 2025:
Submission Portal: Submit your abstract through the designated online submission portal provided on the conference website.
Abstract Format: Prepare your abstract according to the specified format guidelines, including word count, font size, and formatting style.
Title: Provide a concise and descriptive title that accurately reflects the content of your research.
Authors: List all authors' names, affiliations, and contact information. Indicate the presenting author if different from the primary author.
Abstract Content: Clearly state the objectives, methods, results, and conclusions of your research within the abstract. Avoid abbreviations and undefined acronyms.
Keywords: Include a list of relevant keywords or phrases to facilitate indexing and searchability.
Clarity and Conciseness: Write your abstract in clear, concise language, avoiding unnecessary jargon or technical terms that may be unclear to readers.
Relevance: Ensure that your abstract aligns with the conference theme and focuses on topics relevant to Neuroscience 2025.
Graphics and Tables: If applicable, include relevant graphics, tables, or images to enhance understanding of your research findings. Ensure they are high quality and clearly labelled.
References: If citing previous research or literature, include appropriate references following the specified citation style.
Submission Deadline: Submit your abstract by the specified deadline to be considered for presentation at the conference. Late submissions may not be accepted.
Confirmation: Upon successful submission, you should receive a confirmation email or notification acknowledging your abstract.
Review Process: Abstracts will undergo a peer review process by the conference scientific committee. Notification of acceptance or rejection will be communicated within the specified timeframe.
Presentation Format: If your abstract is accepted, follow the guidelines provided for oral or poster presentation, including presentation length, formatting requirements, and session scheduling.
NOTE: Download the abstract template from the above for submission reference.
Adhering to these submission guidelines will help ensure that your abstract is properly formatted, relevant, and competitive for consideration at the Neuroscience 2025.
How brain connectivity and machine learning enhance understanding of human cognition
2024-12-16 - 2024-12
A recent study explores the relationship between brain connectivity and intelligence, highlighting the value of interpretability in predictive modeling for deeper insights into human cognition.
Machine learning in neuroscience
Neuroscientific research on human cognition has evolved from focusing on single-variable explanatory studies to employing machine learning-based predictive modeling. This shift enables the analysis of relationships between behavior and multiple neurobiological variables to forecast behavior across diverse samples.
Intelligence, a key predictor of life outcomes such as health and academic achievement, has been extensively studied, with theories dividing it into fluid and crystallized components. Recent machine learning approaches have enhanced intelligence prediction using brain connectivity data. However, limited conceptual insights, reliance on specific intelligence measures, and methodological constraints highlight the need for further research to systematically explore predictive brain features.
The present study adhered to a rigorous methodology, with all analyses, sample sizes, and variables preregistered on the Open Science Framework. The primary analyses followed preregistered protocols, with additional post hoc analyses conducted to further explore brain connections most relevant for intelligence prediction.
Study participants were drawn from the Human Connectome Project (HCP) Young Adult Sample S1200, consisting of 1,200 individuals between 22 and 37 years of age. Informed consent was obtained in accordance with the Declaration of Helsinki and all procedures were approved by the Washington University Institutional Review Board.
After exclusions for missing data, cognitive impairment based on Mini-Mental State Examination (MMSE) scores of 26 and less, or excessive head motion, the final sample included 806 participants, 418 of whom were female and 733 right-handed. Measures of intelligence including general intelligence (gg), crystallized intelligence (gCgC), and fluid intelligence (gFgF) were estimated using bi-factor and exploratory factor analyses from cognitive test scores.
Functional magnetic resonance imaging (fMRI) data were collected during resting state and seven cognitive tasks to construct subject-specific functional connectivity (FC) matrices. Minimally pre-processed fMRI data underwent additional preprocessing steps, including nuisance regression, global signal correction, and removal of task-evoked activation, to improve connectivity estimates. Predictive modeling utilized feedforward neural networks, which incorporated five-fold cross-validation, hyperparameter optimization, and an out-of-sample deconfounding approach to control for covariates such as age, sex, and head motion.
Model interpretability was enhanced using layer-wise relevance propagation (LRP) to identify functional brain connections most critical for predictions. External replication was performed using two independent d
