Medical Design and Outsourcing

  • Home
  • Medical Device Business
    • Mergers & Acquisitions
    • Financial
    • Regulatory
  • Applications
    • Cardiovascular
    • Devices
    • Imaging
    • Implantables
    • Medical Equipment
    • Orthopedic
    • Surgical
  • Technologies
    • Supplies and Components Index
    • Contract Manufacturing
    • Components
    • Electronics
    • Extrusions
    • Materials
    • Motion Control
    • Prototyping
    • Pumps
    • Tubing
  • MedTech Resources
    • Medtech Events in 2025
    • The 2024 Medtech Big 100
    • Medical Device Handbook
    • MedTech 100 Index
    • Subscribe to Print Magazine
    • DeviceTalks
    • Digital Editions
    • eBooks
    • Educational Assets
    • Manufacturer Search
    • Podcasts
    • Print Subscription
    • Webinars / Digital Events
    • Whitepapers
    • Voices
    • Views
    • Video
  • 2025 Leadership
    • 2024 Winners
    • 2023 Winners
    • 2022 Winners
    • 2021 Winners
  • Women in Medtech
  • Advertise
  • Subscribe

Dead Salmon, Bugs And Brain Scans: Can We Ever Reach A Consensus In Neuroscience Research?

October 6, 2016 By Brunel University

Brain scans involving functional magnetic resonance imaging (fMRI) have been a media darling for two decades. Images of brains lighting up to external stimuli have proved irresistible and have helped neuroscience achieve popular renown.

But fMRI has also received a battering. First, most famously, when used on a dead salmon, the technique appeared to show brain and spinal cord activity when subject to stimuli (in this case, images of people in social situations).

More recently, a PNAS (Proceedings of the National Academy of Sciences) paper reported a bug in one of the most widely used pieces of software for analyzing data from fMRI scans. Again, this was widely reported.

The reason that fMRI works is due to the fact that increased neuronal activity leads to increased flow of blood in the brain. Of course, fMRI scans do produce a lot of data.

Consequently, trying to identify those areas where a section of the brain is activated more compared with other areas requires processing huge amounts of data. Rapidly, the multiple comparisons problem rears its head.

A possible consequence of this is that researchers may, and indeed do, opt for a limited or small sample size. There are then the limitations of small sample sizes. A recent study in Nature Reviews Neuroscience reports, “Here, we show that the average statistical power of studies in the neurosciences is very low. The consequences of this include overestimates of effect size and low reproducibility of results.”

The broader question of how to analyze fMRI neuroimaging data has gradually attracted more attention over the question of how to collect the data.

Methods aiming to extract the interesting information directly from the data, contrary to the traditional approach based on testing the hypothesis proposed by researchers beforehand, have been introduced to the neuroimaging field.

These emerging data-driven methods provide more possibilities and perspectives of investigating and understanding the neuroimaging data that convey some crucial information for discovering brain mechanisms underlying cognitive behaviors.

Typically, an fMRI study on a certain stimulation paradigm would adopt a single method of analysis and statistical thresholding. This raises the question of the generalizability of the results from a single method of analysis. Yet, if a second study on the same stimulation paradigm would utilize another method of analysis, two different sets of divergent results would manifest. Considering that in this field methods of analysis and statistics have proliferated, it is inevitable that a somewhat confusing picture of the scientific progress gained by fMRI research would emerge.

We report on a collaborative study between Brunel and Aarhus Universities, with the data collected at the Advanced Magnetic Imaging center of Aalto university, Finland. What we have done, for the first time is to analyze fMRI data with the consensus clustering paradigm called binarisation of consensus partition matrices (Bi-CoPaM). This paradigm is capable of merging results from many analysis methods in order to obtain robust and reproducible clusters from various datasets. As a result, this can finally lead to a consensual landscape of neuroimaging results.

To validate the paradigm, we applied it to a complex fMRI experiment involving affective processing of hundreds of music clips. We found that brain structures related to visual, reward, and auditory processing have intrinsic spatial patterns of coherent neuroactivity.

The comparisons between the results obtained from our method and those from each individual clustering algorithm demonstrate that our paradigm has notable advantages over traditional single clustering algorithms in being able to evidence robust connectivity patterns even with complex neuroimaging data, involving a variety of stimuli and affective evaluations of them.

Our innovative use of the Bi-CoPaM paradigm allows us to find clusters including functionally and anatomically related neural networks consistently responding to emotional music, i.e. the basal ganglia, thalamus, insula, and other areas involved with processing of auditory features such as the Heschl’s gyrus, the Rolandic operculum, and the superior temporal gyrus.

One of the pioneering aspects of this study is the employment of the Bi-CoPaM paradigm that explores fMRI data without any predetermined model, which is needed in classical model-based approaches.

The most important finding of this study is that our proposed approach was able to discover a single cluster, including the anatomically connected subcortical and cortical structures of the reward circuit, responding selectively to liked, enjoyed music. This is one of the few studies obtaining such finding with a data-driven approach.

Oh and that dead salmon reacting? Once the data underwent correction for multiple comparisons the false positives were eliminated—it really was a deceased fish!

(Source: AlphaGalileo)

Related Articles Read More >

An illustration of Embolization Inc.'s Nitinol Enhanced Device (NED).
This nitinol vascular embolization device has another shape memory material up its sleeve
A photo of nitinol, a nickel-titanium alloy used for medical devices such as stents, heart valves, catheters and orthopedics.
What is nitinol and where is it used?
July 2025 edition: The Surgical Robotics issue, featuring Capstan Medical, J&J and Zimmer Biomet
A photo of Capstan Medical's mitral valve implant, which uses nitinol.
Capstan Medical’s R&D head discusses the heart valve and robotics startup’s tech, engineering challenges and solutions, advice for others in medtech and how to join his team
“mdo
EXPAND YOUR KNOWLEDGE AND STAY CONNECTED
Get the latest medical device business news, application and technology trends.

DeviceTalks Weekly

See More >

MDO Digital Edition

Digital Edition

Subscribe to Medical Design & Outsourcing. Bookmark, share and interact with the leading medical design engineering magazine today.

MEDTECH 100 INDEX

Medtech 100 logo
Market Summary > Current Price
The MedTech 100 is a financial index calculated using the BIG100 companies covered in Medical Design and Outsourcing.
DeviceTalks

DeviceTalks is a conversation among medical technology leaders. It's events, podcasts, webinars and one-on-one exchanges of ideas & insights.

DeviceTalks

New MedTech Resource

Medical Tubing

MassDevice

Mass Device

The Medical Device Business Journal. MassDevice is the leading medical device news business journal telling the stories of the devices that save lives.

Visit Website
MDO ad
Medical Design and Outsourcing
  • MassDevice
  • DeviceTalks
  • MedTech100 Index
  • Medical Tubing + Extrusion
  • Medical Design Sourcing
  • Drug Delivery Business News
  • Drug Discovery & Development
  • Pharmaceutical Processing World
  • R&D World
  • About Us/Contact
  • Advertise With Us
  • Subscribe to Print Magazine
  • Subscribe to our E-Newsletter
  • Listen to our Weekly Podcasts
  • Join our DeviceTalks Tuesdays Discussion

Copyright © 2025 WTWH Media, LLC. All Rights Reserved. The material on this site may not be reproduced, distributed, transmitted, cached or otherwise used, except with the prior written permission of WTWH Media LLC. Site Map | Privacy Policy | RSS

Search Medical Design & Outsourcing

  • Home
  • Medical Device Business
    • Mergers & Acquisitions
    • Financial
    • Regulatory
  • Applications
    • Cardiovascular
    • Devices
    • Imaging
    • Implantables
    • Medical Equipment
    • Orthopedic
    • Surgical
  • Technologies
    • Supplies and Components Index
    • Contract Manufacturing
    • Components
    • Electronics
    • Extrusions
    • Materials
    • Motion Control
    • Prototyping
    • Pumps
    • Tubing
  • MedTech Resources
    • Medtech Events in 2025
    • The 2024 Medtech Big 100
    • Medical Device Handbook
    • MedTech 100 Index
    • Subscribe to Print Magazine
    • DeviceTalks
    • Digital Editions
    • eBooks
    • Educational Assets
    • Manufacturer Search
    • Podcasts
    • Print Subscription
    • Webinars / Digital Events
    • Whitepapers
    • Voices
    • Views
    • Video
  • 2025 Leadership
    • 2024 Winners
    • 2023 Winners
    • 2022 Winners
    • 2021 Winners
  • Women in Medtech
  • Advertise
  • Subscribe