--- # System prepended metadata title: Introduction to Neuroscience (INS) v2 tags: [Talk] --- --- title: Introduction to Neuroscience (INS) v2 tags: Talk description: View the slide with "Slide Mode". --- # WEAR YOUR MASK ## Thank you! --- # Introduction to Functional Magnetic Resonance Imaging (fMRI) ###### Will Strawson, ws231@sussex.ac.uk ---- ### By the end of this class you should... ###### - Understand the difference between structural and functional MRI ###### - Understand the biological basis of the fMRI signal ###### - Understand the data structure of fMRI ###### - Understand what 'activation blobs' do and don't represent ###### - Have an appreciation for preprocessing fMRI data ###### - Be able to compare fMRI to other neuroimaging techniques --- # Magnetic Resonance Imaging ![](https://i.imgur.com/BZBrbc9.jpg =500x) ---- ![](https://i.imgur.com/yFGn2zh.png) ##### Image of human finger from Mansfield and Maudsley (1977) obtained at 0.35 Tesla in 23 minutes. ---- ![](https://i.imgur.com/l8ORaWK.png) ##### First whole body image , obtained July, 1977. It required nearly 5 hours to produce. ---- ![](https://i.imgur.com/s6wayRL.png) #### T1 weighted structural MRI image, obtained ~2018. Took approximately 7 minutes to produce! ##### CSF signal repressed --- # Structural MRI #### Structural images provide information about the anatomy of what's being imaged by showing differences between different kinds of tissues (Grey Matter vs White Matter vs CSF) #### Structural MRI provides high spatial resolution (0.8mm x 0.8mm x 0.8mm) images. --- # Voxels ![](https://i.imgur.com/qH5ykJi.png =400x) ---- ## Over to you (Viewing a structural image) 1. Open FSLeyes 2. File -> Add from file -> fsl_course_data/ExBox11/structural.nii.gz 3. Increase brightness + contrast ---- ![](https://i.imgur.com/qH5ykJi.png =150x) ##### _Voxels_: Volumetric Pixel - the basic unit of measurement. Each voxel has a coordinate in the volume ##### _Slice_: all voxels in 2D plane. ##### _Volume_: The entire 3-D grid covering the space imaged in the MRI scanner. Volume is composed of voxels ##### _TR_: One volume scanned every TR - basic time measurement in MRI --- # What about the 'f' in fMRI? ### We want to map _function_ to _structure_. ### But how do we quantify neuronal activation using non-invasive MRI? --- ## BOLD fMRI! ### _BOLD_ = Blood Oxygenation Level Dependent #### We measure the level of blood oxygenation throughout different cortical regions, and infer neuronal activation from that. ##### Based on the premise that increased blood oxygenation = increased neuronal activation. --- # Time ![](https://i.imgur.com/x506oIm.png) ### Each voxel has an associated time-series of BOLD activation. --- ![](https://i.imgur.com/qH5ykJi.png =200x) ![](https://i.imgur.com/jdPAXVB.png =400x) ###### We have measurment of BOLD for each voxel across time ---- ## Over to you (viewing a function MRI image) 1. Delete any previous images 2. File -> Add from file -> fsl_course_data/ExBox11/fmri.nii.gz 3. Expore volumes (movie view) 4. View time series' (View -> Time series) --- # What do those blobs mean? ### BOLD -> Parameter Estimate -> Test-statistic -> p-value ![](https://i.imgur.com/fvxRDZj.png) ---- ## Over to you (Overlaying statistical map) 1. File -> Open from file -> fsl_course_data/ExBox11/fmri.feat/thresh_zstat5.nii 2. Change colour grading from grey 3. Notice the scores on the bottom right --- # Preprocessing ### fMRI signal is _very_ noisy ### Many _preprocessing_ techniques are available to remove physiological and scanner noise e.g. temporal filtering ; Motion correction; spatial smoothing etc --- ## Coregistration #### Co-Registration (or simply registration) refers to the alignment and overlay of fMRI data from a single subject with that subject's own but separately acquired anatomic image. ## Normalization #### Process that aligns and warps fMRI data into a generic anatomic template (e.g. Talairach and MNI atlases). Normalization is usually performed on data from multiple subjects. --- # Multiple Comparisons ### Question: with p-value = 0.05 for a single voxel and 100,000 voxels, how many false positives? ---- ### 5000 of the 100,000 total voxels in a study (100,000 x 0.05) would potentially appear falsely activated. ### This issue is known in statistics as the multiple comparisons problem. ---- ![](https://i.imgur.com/Ot99BsD.png =1000x) ###### (Craig Bennet et al) ---- ## Bonferoni Correction ### Bonferroni is super simple —just divide your original acceptance threshold (P≤0.05) by the number of tests you are analyzing. ###### e.g. 0.05 / 100,000(voxels) = p < 0.000001 #### However, this will equal high false negative results, ##### Other methods: use 'False Discovery Rate (FDR)'; Cluster Correction. --- ## Temporal and Spatial Resolution ![](https://i.imgur.com/87TcqFc.png) ###### - In comparison to other methods, fMRI has very good spatial resolution (~3mm) but average temporal resolution (~3s). ###### - One axis is missing from this diagram: Invasivness. --- ## One Caveat... #### The fMRI methods of analysis I've presented, and the language i've used, may implicitly assume a modular view of the brian. ###### e.g. Different cognitive processes require different computational solutions which are implemented in anatomically/functionally distinct regions that operate independently – that is, in a modular fashion - brain area _x_ is responsible for cognitive process _y_ ---- #### This is a BIG assumption that is still up for debate. #### There is evidence that cognitive processes relying on highly distrubuted networks of coordinated activity, in specific frequency bands (see EEG/MEG evidence). ---- #### What do you think? --- ## Summary ###### - Structural-MRI = hi-res, no time; functional-MRI = has time componant ###### - Biological basis of fMRI = BOLD ###### - Data structure composed of voxels; slices; volumes. (4 dimensional for fMRI) ###### - 'blobs' represent statistical scores not 'activation' or even BOLD ###### - fMRI data needs preprocessing ###### - fMRI has good spatial resolution, but poor temporal resolution in comparison to other neuroimaging techniques (e.g. MEG ) --- #### A good, chilled out resource: ![](https://i.imgur.com/qtS5oSg.png =500x)