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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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Updated: Apr 5, 2026

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

Published on: August 12, 2019

Translational Perspectives for Computational Neuroimaging.

Klaas E Stephan¹, Sandra Iglesias², Jakob Heinzle²

¹Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich and ETH Zurich, 8032 Zurich, Switzerland; Wellcome Trust Centre for Neuroimaging, University College London, London WC1N 3BG, UK; Max Planck Institute for Metabolism Research, 50931 Cologne, Germany.

|August 21, 2015

Summary

This summary is machine-generated.

Computational neuroimaging offers mechanistic explanations for brain function, moving beyond statistics. This approach, applied to schizophrenia, aids psychiatric diagnostics by linking brain states to measurements.

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Published on: May 12, 2019

Area of Science:

Neuroscience
Computational Psychiatry
Psychiatric Diagnostics

Background:

Functional neuroimaging advances our understanding of brain function.
Translating neuroimaging findings into psychiatric diagnostic tools remains a significant challenge.

Purpose of the Study:

To review contemporary computational neuroimaging frameworks.
To explore applications of these models in psychiatry, focusing on schizophrenia.
To identify challenges and trends in computational neuroimaging for clinical translation.

Main Methods:

Focus on forward models linking unobservable brain states to neuroimaging measurements.
Review of biophysical network models, generative models, and model-based fMRI analyses.
Application of computational models to psychiatric questions, using schizophrenia as a case study.

Main Results:

Computational neuroimaging provides mechanistic explanations beyond statistical characterizations.
Models are being applied to psychiatric questions, with schizophrenia as a key example.
Convergence trends among different computational neuroimaging approaches are identified.

Conclusions:

Computational neuroimaging offers promising avenues for psychiatric diagnostics.
A translational neuromodeling strategy is proposed, emphasizing open datasets.
Evaluating clinical utility requires prospective patient studies and openly available data.