Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Sensitivity, Specificity, and Predicted Value

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...

Dose-Response Relationship: Selectivity and Specificity

Dose-Response Relationship: Selectivity and Specificity

Drugs exert their therapeutic effects by interacting with receptors, enzymes, or ion channels that are present throughout the human body. The strength and duration of the interaction between a drug and its target receptor are characterized by the selectivity and specificity of the drug. Selectivity refers to a drug's strong preference for its intended target over other targets. For instance, isoprenaline, a non-selective β-adrenergic agonist, interacts with both β1- and...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Complex affect dynamics offer limited incremental value for cross-sectional prediction of psychological and behavioural variables in cross-validated models.

BMC medicine·2026

Same author

Competing programs shape cortical sensorimotor-association axis development.

Nature·2026

Same author

Cumulative timing-dependent changes in corticospinal excitability during suprathreshold paired-pulse transcranial magnetic stimulation.

Scientific reports·2026

Same author

Geometric constraints on the architecture of mammalian cortical connectomes.

Cell·2026

Same author

Multiscale heterogeneity of atypical functional connectivity in autism.

Nature. Mental health·2026

Same author

Geometric brain signatures of Alzheimer's disease progression and subtypes.

medRxiv : the preprint server for health sciences·2026

Same journal

Brain-Inspired Large Model Mindreading.

NeuroImage·2026

Same journal

Light on Broken Networks: Resting-State fNIRS as a Tool for Connectivity Mapping.

NeuroImage·2026

Same journal

Criticism-Evoked Rumination Is Linked to Dynamic adjustments of the Left Superficial Amygdala in Adolescents.

NeuroImage·2026

Same journal

GeNED.ar cohort: Neuroimaging Resource for Aging Studies in an Admixed Population from Argentina.

NeuroImage·2026

Same journal

DTI-ALPS index correlates with poor neuromodulation outcomes of bilateral STN-DBS in Parkinson's disease patients: a prospective cohort study.

NeuroImage·2026

Same journal

Decoding neuronal criticality firing patterns for large brain based EEG models.

NeuroImage·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 18, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Connectome sensitivity or specificity: which is more important?

Andrew Zalesky¹, Alex Fornito², Luca Cocchi³

¹Melbourne Neuropsychiatry Centre and Melbourne School of Engineering, The University of Melbourne, Australia.

|July 2, 2016

Summary

This summary is machine-generated.

Specificity is crucial for accurate brain network mapping. False positives significantly distort network properties, making high specificity essential for reliable connectome reconstruction.

Keywords:

Clustering coefficient Complex networks Connectome False negatives False positives Modularity Network efficiency Sensitivity Specificity Tractography

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

Related Experiment Videos

Last Updated: Mar 18, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

Area of Science:

Neuroimaging
Computational Neuroscience
Network Science

Background:

Current axonal fiber reconstruction methods struggle to achieve both high sensitivity and specificity in macro-scale connectomes.
Deterministic tractography produces sparse connectomes with false negatives, while probabilistic methods yield dense but less specific connectomes due to false positives.

Purpose of the Study:

To determine the optimal tradeoff between sensitivity and specificity in connectome reconstruction.
To evaluate the impact of false positives and false negatives on brain network properties.

Main Methods:

Empirical evaluation of false positive and false negative impacts on connectome properties.
Theoretical asymptotic analysis of small-world networks to quantify the importance of specificity versus sensitivity.

Main Results:

Specificity is at least twice as important as sensitivity for estimating key brain network topological measures (clustering, efficiency, modularity).
The importance of specificity increases with network size, particularly for estimating network efficiency.
False positives disproportionately occur between network modules, significantly impacting network topology.

Conclusions:

Prioritizing specificity in connectome reconstruction is essential for accurate brain network mapping.
Current efforts should realign to maximize specificity, rather than solely focusing on sensitivity.