87996 - Physics in Neuroscience and Medicine

Learning outcomes

At the end of the course the student will have the knowledge of important physical principles and experimental procedures applied to medical diagnosis and scientific research in medicine. Particular attention will be devoted to nuclear magnetic resonance (NMR) relaxometry, diffusometry, spectroscopy and imaging and their combination, along with more traditional techniques (CT, PET, EEG, MEG, NIRS). The student will also learn about advanced diagnostic techniques for neuroscience, based on morphological and functional images, which are the instruments for brain function and connectivity research. He/she will be able to use software for planning pulse sequences and simulate the results of NMR experiments and for data inversion in one and two dimensions (T1, T2, self-diffusion coefficient). Moreover magnetic resonance neuroimaging data will be analyzed in a practical tutorial, simulating a post processing session.

Course contents

Physical basis of Nuclear Magnetic Resonance (NMR). Time Domain NMR (TD-NMR) - Relaxometry and Diffusometry: characterization of relaxation times (T1 and T2) and self-diffusion coefficient (D). Bloembergen, Purcell, Pound theory (BPP Theory). Magnetic Resonance for fluids in Porous Media: surface effects, relaxation times in porous materials and in biological tissues. Inversion from data time domain to relaxation times - UPEN algorithm and UpenWin software. Basis of two-dimensional NMR: relaxation-relaxation, relaxation-exchange and diffusion-relaxation. Single-sided NMR: basic concepts of MR in the constant field gradient of a portable NMR device (MOUSE PM10), profiles and examples of in-situ application.

NMR Spectroscopy- Chemical shift and J-coupling. NMR imaging (MRI) - k-space and k-space mapping. Time diagrams for NMR and MRI experiments. Multi-dimensional experiments. NMR for fluids under confinement. Ex-situ experiments by means of portable NMR devices. Relaxation times in biological tissues. Apparent diffusion coefficients and diffusion tensor. In vivo Magnetic Resonance Spectroscopy- Post processing - relative and absolute quantification. Single voxel (SV) and Chemical shift Imaging. Heteronuclear spectroscopy. Diffusion weighted/Diffusion Tensor imaging. Tractography, whole brain tractography. Structural connectivity. Functional MRI - Block design - Resting state fMRI. Functional connectivity by fMRI, Magneto encephalography (MEG) and fNIRS. Practical tutorials on inversion from data time domain to relaxation times by means of UPEN algorithm and UpenWin software in one and two dimensions. Practical tutorials simulating post processing sessions. Examples of applications in medicine, neuroscience and in the field of fluids in porous media.

Readings/Bibliography

The Human Central Nervous System, Nieuwenhuys, Rudolf, Voogd, Jan, Huijzen, Christiaan van. 2008. Springer

Callaghan, Principles of MRMicroscopy

Introduction to FFC NMR theory and models for complex and confined fluid. Bortolotti V, Brizi L, Landi G, Testa C., Zama F.

In Vivo NMR Spectroscopy: Principles and Techniques, 2nd Edition Robin A. de Graaf

Diffusion MRI- Hedi Johansen-Berg-Timothy E.J. Berg Elsevier

Functional Magnetic Resonance Imaging. An Introduction to Methods Edited by Peter Jezzard, Paul M Matthews, and Stephen M Smith

MRI: The Basics- 2017, Ray H Hashemi, CJ Lisanti, WG Bradley. Wolters Kluwer

Scientific papers suggested during lessons.

Teaching methods

Frontal lessons. Supplementary exercises are proposed during the lessons to be solved in groups and discussed during class. Part of the course (module 2) consists in practical exercises. NMR data will be acquired and processed using dedicated softwares.

Students with DSA or temporary or permanent disabilities: it is recommended to contact the responsible University office in good time (https://site.unibo.it/studenti-con-disabilita-e-dsa/it): it will be their responsibility to propose any adaptations to the students concerned, which must however be submitted, 15 days in advance, to the approval of the teacher, who will evaluate the opportunity also in relation to the educational objectives of the course.

Assessment methods

Learning evaluation is through a final examination in oral form, which ascertains the acquisition of the expected knowledge and skills. For Module 1, the exam consists of 2 questions on two different topics covered in Module 1.


For Module 2, the exam starts with a discussion of papers given by the student on some of the topics covered in the lectures, which involve learning and applying dedicated algorithms and software for data analysis. After this discussion, the exam proceeds with a test of knowledge of the topics covered in the lectures. The total duration of the test averages 45 minutes.


Grading of final grade (taking into account both modules):


18-24: Preparation on a limited number of topics covered in the course and ability to analyze independently only on purely executive issues, expression in language not always rigorous;


25-29: Knowledge of topics covered in the course, ability to make independent choices of critical analysis, correct use of specific terminology;


30-30L: The student's achievement of critical knowledge of the topics covered and demonstration of the attainment of mastery of expression and scientific language will be evaluated with grades of excellence.

Teaching tools

Pc, video projector, softwares and laboratory instruments.

Office hours

See the website of Claudia Testa

See the website of Leonardo Brizi