Neuroscience
Terms and conditions
Oct 09-10, 2025 Tokyo, Japan

World Congress on Neuroscience

Early Bird Registration End Date: Feb 05, 2025
Abstract Submission Opens: Dec 23, 2024

Terms and Conditions

Thank you for choosing Sciconx Conferences. Please read the following terms and conditions carefully as they govern your reservation and participation in our events. Your reservation implies acceptance of these terms.

  • Submission of your online registration indicates your acceptance of Sciconx Conferences terms and conditions. Confirmation is subject to complete payment receipt, and a confirmation email with a receipt will be sent upon successful registration.
  • Full payment of registration fees is required before the event. Access to the conference will only be granted upon confirmation of complete payment.
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General:

  • Cancellation and refund requests must be emailed to support@sciconx.com
  • Refunds will be issued after the conference concludes in the second week.
  • As stated in our registration terms and conditions, unfortunately, we are unable to process refunds once the official invitation letter has been completed.

Cancellation Charges

Registration fees:

  • 60 days or more before the event: Eligible for a full refund minus a 20% administration fee for each registration.
  • Within 45-60 days before the event: Eligible for a 50% refund.
  • Within 45 days of the conference: Not entitled to any refund.
  • E-Poster/virtual participation fees are non-refundable.
  • All discounted registrations are non-refundable.

Accommodation Cancellation

  • No refunds will be provided for accommodation fees.
  • Substitutions can be made at any time with written notification to Sciconx at least 30 days before the event.
  • Transfer of fully paid registrations to others within the same organization is allowed with substitute details provided.

Sciconx reserves the right to modify the program, venue, and timing. Attendees are responsible for any associated expenses or losses in case of postponement or date changes.

  • Views expressed by speakers, sponsors, and exhibitors are their own. Sciconx is not liable for advice or opinions expressed at the conference.
  • Sciconx is not responsible for direct or indirect loss or damage resulting from event services or data.

Attendees must arrange suitable insurance coverage for conference participation. Sciconx is not responsible for personal property loss or damage.

Photographs and video recordings may be taken for advertising purposes. Attendees who wish not to be filmed should inform organizers in writing before the conference.

  • Participants are responsible for fulfilling their visa requirements, and Sciconx cannot be held responsible for any visa-related issues.
  • In consideration of increased security measures, we urge all attendees to promptly commence their visa application process.
  • Sciconx Conferences will not engage in direct communication with embassies or consulates on behalf of visa applicants. It is crucial that all delegates or invitees apply specifically for a Business Visa. Your cooperation in this regard is highly valued.

Registrants are responsible for parking and transportation.

Press permission must be obtained from Sciconx before the conference. Media should not conduct interviews without written approval.

All event attendees, including speakers, sponsors, and exhibitors, are subject to these terms and conditions. Sciconx reserves the right to change conditions without prior notice, reviewing them periodically for regulatory compliance and conference improvements.

Latest News

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


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