Detecting low-frequency functional connectivity in fMRI using a self-organizing map (SOM) algorithm.

نویسندگان

  • Scott J Peltier
  • Thad A Polk
  • Douglas C Noll
چکیده

Low-frequency oscillations (<0.08 Hz) have been detected in functional MRI studies, and appear to be synchronized between functionally related areas. A current challenge is to detect these patterns without using an external reference. Self-organizing maps (SOMs) offer a way to automatically group data without requiring a user-biased reference function or region of interest. Resting state functional MRI data was classified using a self-organizing map (SOM). Functional connectivity between the left and right motor cortices was detected in five subjects, and was comparable to results from a reference-based approach. SOMs are shown to be an attractive option in detecting functional connectivity using a model-free approach. Hum. Brain Mapping 20:220-226, 2003.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

A Non-Parametric Approach for the Activation Detection of Block Design fMRI Simulated Data Using Self-Organizing Maps and Support Vector Machine

Functional magnetic resonance imaging (fMRI) is a popular method to probe the functional organization of the brain using hemodynamic responses. In this method, volume images of the entire brain are obtained with a very good spatial resolution and low temporal resolution. However, they always suffer from high dimensionality in the face of classification algorithms. In this work, we combine a sup...

متن کامل

The Time Adaptive Self Organizing Map for Distribution Estimation

The feature map represented by the set of weight vectors of the basic SOM (Self-Organizing Map) provides a good approximation to the input space from which the sample vectors come. But the timedecreasing learning rate and neighborhood function of the basic SOM algorithm reduce its capability to adapt weights for a varied environment. In dealing with non-stationary input distributions and changi...

متن کامل

fMRI data analysis techniques and the self-organizing maps approach

Functional Magnetic Resonance Imaging (fMRI) is a widely used technique to know more about how the brain function supports mental activities. Although fMRI is a powerful tool to detect functional activation within the brain, the obtained data from fMRI experiences cannot be easily directed analyzed because of a number of factors: weakness of the signal, abundant noise in the data and the diffic...

متن کامل

Landforms identification using neural network-self organizing map and SRTM data

During an 11 days mission in February 2000 the Shuttle Radar Topography Mission (SRTM) collected data over 80% of the Earth's land surface, for all areas between 60 degrees N and 56 degrees S latitude. Since SRTM data became available, many studies utilized them for application in topography and morphometric landscape analysis. Exploiting SRTM data for recognition and extraction of topographic ...

متن کامل

Analysis of functional magnetic resonance imaging data using self-organizing mapping with spatial connectivity.

Commonly used methods in analyzing functional magnetic resonance imaging (fMRI) data, such as the Student's t-test and cross-correlation analysis, are model-based approaches. Although these methods are easy to implement and are effective in analyzing data obtained with simple paradigms, they are not applicable in situations in which patterns of neuronal response are complicated and when fMRI re...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Human brain mapping

دوره 20 4  شماره 

صفحات  -

تاریخ انتشار 2003