mathematics

Martin A. Lindquist

Martin A. Lindquist is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Martin A. Lindquist rather than just read about it. In short: Martin A. Lindquist is a statistician and biostatistician whose work focuses on functional magnetic resonance imaging (fMRI), neuroimaging methodology, brain connectivity, causal inference, and pain neuroscience.

Key takeaways

Martin A. Lindquist belongs to mathematics; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Martin A. Lindquist to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Martin A. Lindquist from memory before moving on to harder problems.

Reference excerpt

Martin A. Lindquist is a statistician and biostatistician whose work focuses on functional magnetic resonance imaging (fMRI), neuroimaging methodology, brain connectivity, causal inference, and pain neuroscience. He is a professor in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health. His research has included statistical modeling of the hemodynamic response in fMRI, dynamic functional connectivity, mediation analysis in neuroimaging, and methodological work on reliability, preprocessing, and biomarkers in brain imaging.

Early life and education Lindquist was born in Stockholm, Sweden. He completed an MSc in engineering physics at the Royal Institute of Technology (KTH) in Stockholm in 1997 and earned a PhD in statistics from Rutgers University in 2001. His doctoral dissertation was titled Fast Functional MRI Using Two-Dimensional Prolate Spheroidal Wavefunctions. His thesis advisors were Lawrence Shepp and Cun-Hui Zhang.

Career After completing his doctorate, Lindquist was a postdoctoral associate at the Center for Magnetic Resonance Research at the University of Minnesota from 2001 to 2002. He joined Columbia University in 2002 as an assistant professor of statistics and became an associate professor in 2008. In 2012 he moved to Johns Hopkins University as an associate professor of biostatistics, and in 2015 he became a full professor.

Research Lindquist's published work spans several areas of neuroimaging statistics. His early research included methods for rapid fMRI acquisition and the statistical analysis of high-temporal-resolution imaging data. He later published on hemodynamic response modeling, dynamic functional connectivity, and reliability in neuroimaging research. He has also published on causal inference and mediation analysis for neuroimaging data, including functional causal mediation and high-dimensional mediation models. His more recent work has addressed pain neuroimaging and neuroimaging-based biomarkers. His co-authored publications include work on neurologic signatures of physical pain. He is a principal investigator on the Acute to Chronic Pain Signatures (A2CPS) project, which seeks to identify biomarkers and advance the study of pain. Lindquist also developed online courses, including Principles of fMRI I, Principles of fMRI II, and The Statistical Analysis of fMRI Data, offered through Coursera, which have reached 100,000 students world-wide. Together with Tor Wager, he co-authored Principles of fMRI, a low-cost book on fMRI data analysis.

Honors and awards Lindquist was elected a Fellow of the American Statistical Association in 2016. In 2018 he received the Organization for Human Brain Mapping Education in Neuroimaging Award.

Selected works Lindquist, Martin (2008). "The Statistical Analysis of fMRI Data". Statistical Science. Lindquist, Martin (2012). "Functional Causal Mediation Analysis with an Application to Brain Connectivity". Journal of the American Statistical Association. Wager, Tor; Atlas, Lauren; Lindquist, Martin; Roy, Mathieu; Woo, Choong-Wan; Kross, Ethan (2013). "An fMRI-based Neurologic Signature of Physical Pain". New England Journal of Medicine. Lindquist, Martin; Xu, Yuting; Nebel, Mary Beth; Caffo, Brian (2014). "Evaluating Dynamic Bivariate Correlations in Resting-state fMRI: A comparison study and a new approach". NeuroImage. Lindquist, Martin; Geuter, Stephan; Wager, Tor; Caffo, Brian (2019). "Modular Preprocessing Pipelines can Reintroduce Artifacts into fMRI Data". Human Brain Mapping.

References

External links Johns Hopkins University faculty page Professional webpage

Worked examples

Example 1 — a first encounter with Martin A. Lindquist

Start with the simplest possible case. Write down what Martin A. Lindquist claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Martin A. Lindquist before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Martin A. Lindquist ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Martin A. Lindquist

In research: Martin A. Lindquist appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Martin A. Lindquist in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Martin A. Lindquist is common in secondary-school and first-year university syllabi. It links to neighbouring topics Biostatisticians, Columbia University faculty, Johns Hopkins Bloomberg School of Public Health faculty, so understanding it makes those chapters shorter.
In everyday life: Look for Martin A. Lindquist outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Martin A. Lindquist in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Martin A. Lindquist means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Martin A. Lindquist out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Martin A. Lindquist in simple terms?

Why does Martin A. Lindquist matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Martin A. Lindquist?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Martin A. Lindquist.