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Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...

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Classifying schizophrenia patients and healthy individuals: Whole brain SPECT functional connectivity using support

Amritha Harikumar1, Joanne Wardell1, David Keator2,3,4

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Summary

This study shows that random forest and logistic regression classifiers are more effective than linear SVM for diagnosing schizophrenia using single photon emission computed tomography (SPECT) brain scans. These findings suggest improved diagnostic accuracy for functional brain networks in schizophrenia.

Keywords:
ClassificationICASPECTSchizophreniaSupport vector machine

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Area of Science:

  • Neuroimaging
  • Psychiatric Disorders
  • Machine Learning

Background:

  • Functional magnetic resonance imaging (fMRI) is widely used for brain network analysis in disorders like schizophrenia.
  • Single photon emission computed tomography (SPECT) also measures neural activity via blood flow and tracers.
  • Limited research exists on using SPECT data for individual subject classification in schizophrenia.

Purpose of the Study:

  • To evaluate the accuracy of individual diagnostic prediction for schizophrenia using SPECT data.
  • To compare the performance of various machine learning classifiers on SPECT-derived functional brain networks.

Main Methods:

  • Utilized spatially constrained independent component analysis (sc-ICA) on SPECT data from 213 subjects (137 schizophrenia patients, 76 controls).
  • Network priors were derived from fMRI data using the NeuroMark fMRI 1.0 template.
  • Initially employed a support vector machine (SVM) classifier, followed by post hoc evaluation of random forest, logistic regression, voting, and multilayer perceptron.

Main Results:

  • Linear SVM performed poorly, with more false negatives compared to other methods.
  • Random forest achieved 88% sensitivity and 61% specificity.
  • Logistic regression achieved 87% sensitivity and 68% specificity.

Conclusions:

  • Random forest and logistic regression demonstrate superior performance for schizophrenia classification using SPECT data.
  • These findings highlight the potential of sc-ICA combined with advanced classifiers for understanding aberrant functional networks in schizophrenia.
  • Further investigation into these methods is recommended for future SPECT studies.