Neuroscience
About
Oct 09-10, 2025 Tokyo, Japan

World Congress on Neuroscience

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

Welcome to Sciconx Conferences, a leading organizer of premier scientific conferences dedicated to advancing knowledge, fostering collaboration, and driving innovation across various disciplines.

At Sciconx Conferences, we believe in the power of bringing together experts, researchers, practitioners, and industry professionals from around the globe to exchange ideas, share insights, and explore new frontiers in science and technology. With a commitment to excellence and a passion for creating impactful events, we strive to provide unparalleled opportunities for learning, networking, and professional development.

Our Mission

Our mission is to facilitate the dissemination of cutting-edge research, promote interdisciplinary collaboration, and inspire the next generation of leaders in science and technology. Through our conferences, we aim to address the most pressing challenges facing society today and drive positive change through innovation and discovery.

Our Vision

We envision a world where scientific knowledge knows no boundaries and where collaboration knows no borders. By fostering a culture of inclusivity, diversity, and excellence, we aim to create a global community of researchers, scholars, and practitioners united in their pursuit of advancing human knowledge and improving the world we live in.

Our Values

Excellence: We strive for excellence in everything we do, from the quality of our conferences to the professionalism of our staff and the satisfaction of our attendees.

Innovation: We embrace innovation as the driving force behind progress and are committed to pushing the boundaries of scientific discovery through creativity and ingenuity.

Integrity: We conduct ourselves with the highest standards of integrity, ethics, and transparency, earning the trust and respect of our stakeholders and partners.

Collaboration: We believe in the power of collaboration and actively seek opportunities to foster interdisciplinary partnerships and exchange ideas across diverse fields of study.

Impact: We measure our success not only by the number of attendees at our conferences but also by the real-world impact of the research and collaborations that emerge from our events.

Our Conferences

Sciconx Conferences hosts a diverse portfolio of conferences spanning a wide range of scientific disciplines. One of our flagship events is the International Conference on Neuroscience.

Welcome to the International Conference on Neuroscience hosted by Sciconx Conferences, taking place in Tokyo, Japan on October 09-10, 2025. Join us as we bring together global leaders, esteemed researchers, healthcare professionals, and industry pioneers for a transformative experience dedicated to shaping the future with the use of Neuroscience.

Under the theme "Neurotech: Bridging Minds & Discovery" this conference will encapsulate the limitless potential of Neuroscience to push boundaries, revolutionize industries, and create transformative solutions.

Each conference is meticulously curated to provide a dynamic platform for researchers, practitioners, policymakers, and industry leaders to come together, share their expertise, and explore new opportunities for collaboration and innovation.

Connect with Us

We welcome inquiries, feedback, and partnership opportunities. Please feel free to reach out to us at support@sciconx.com or visit our website (https://www.sciconx.com/neuroscience) to learn more about our upcoming conferences, sponsorship opportunities, and how you can get involved.

Join us in shaping the future of science. Reserve your spot today!

Thank you for your interest in Sciconx Conferences. We look forward to connecting with you and shaping the future of science together.

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

Useful Links

Subscribe Now