Neuroimaging Processing : Overview, Limitations, pitfalls, etc. etc.

Neuroimaging Neuroimaging includes the use of various techniques to either directly or indirectly image the structure or function of the brain. Structural neuroimaging deals with the structure of the brain (e.g. shows contrast between different tissues: cerebrospinal fluid, grey matter, white matter). Functional neuroimaging is used to indirectly measure brain function (e.g. neural activity) Molecular neuroimaging measures biological processes in the brain at the molecular and cellular level.

Malhi et al. 2007

MRI acquisition

MRI Basics Water = H2O Each Hydrogen = one proton Protons Spin Generates detectable signal in externally applied magnetic field: that is, it causes protons to precess at a frequency proportional to the strength of the magnetic field – the ‘resonant’ frequency Water Content of GM 70% WM 85% Blood 93% Hydrogen Atom PROTON

Magnetic Resonance Imaging (MRI)

Magnetic Resonance Imaging (MRI) Excitation Radio frequency (RF) pulse is applied at the precession frequency (Lamour Frequency) Sending an RF pulse at Lamour freq, particular amplitude and length of time – possible to flip the net magnetism 90° - perpendicular to Magnetic Field (B0) Relaxation T1-weighted is the time it takes for the protons to relax to B0 Not all protons bound by their molecules in same way, dependant on tissue type

Preprocessing: Structural MRI Volume/Thickness/Surface Area/Curvature ….

Structural MRI Region of Interest (ROI) Voxel based morphometry (SPM/FSL) Surface based morphometry (FreeSurfer)

Structural MRI Region of Interest (ROI) Voxel based morphometry (SPM/FSL) Surface based morphometry (FreeSurfer) Volume

Structural MRI Region of Interest (ROI) Voxel based morphometry (SPM/FSL) Surface based morphometry (FreeSurfer) Left Right Thickness Surface Area Curvature Gyrification

Region of Interest What can we measure in a Region of Interest (ROI)? Total volume Shape Average diffusion Average blood flow Average level of Glutamate Average Dopamine levels

Region of Interest Manual v Automated Caudate Manual v FS ICC 0.95 Hippocampus Manual v FS ICC 0.79 52% Volume Difference

What’s the problem with ROI? FreeSurfer Manual

Region of Interest Temporal lobe epilepsy patients (TLE) v Healthy controls (HC) Manual FreeSurfer TLE HC Volume

Voxel-based Mophometry Statistical Parametric Mapping (SPM) FMRIB Software Library (FSL) No a priori hypothesis Volume Change Chronic Schizophrenia patients after Clozapine treatment for 6 months < Healthy Controls (FDR correction p<0.05)

Voxel-based Mophometry Segmentation Normalisation Modulation Smoothing Original MNI Brain

VBM - Limitations Accuracy of the spatial normalisation Regular SPM uses 1000 parameters – just fits overall shape of the brain - mis-registrations Deformation-based morphometry (e.g. DARTEL) – deformation field is analysed Grey matter matched with grey matter – doesn't’t indicate whether sulci/gyri are aligned

FreeSurfer The cortex Volume, thickness or surface area? Volume = surface area * thickness

Volume, thickness & surface area Related but don’t necessarily track each other .... Morphometry Differences between Young, Elderly and Mild Alzheimer’s in entorhinal cortex. *p<0.05 Dickerson et al.2007

Cortical Curvature Temporal Lobe Epilepsy (MR-negative) Cortical curvature abnormality in the ipsilateral temporal lobe - Not explained by volume or thickness Possible surrogate marker for malformations of cortical development Ronan et al. 2011

FreeSurfer Cortical Reconstruction Cortical Analysis - cortical thickness, surface are, volume, cortical folding and curvature Cortical and sub-cortical segmentation

Surfaces: White and Pial

Surface Model Mesh (“Finite Element”) Vertex = point of 6 triangles XYZ at each vertex Triangles/Surface Element ~ 150,000 Area, Curvature, Thickness, Volume at each vertex The cortical surface is represented by a finite element model using triangles to cover the cortical surface. The reconstruction is the (mostly automated) process of assigning an xyz to each corner of each triangle. Once the xyz of each corner of each triangle is known, then it is possible to characterize the entire cortical surface in terms of area, distance, curvature, and thickness.

Cortical Thickness pial surface Distance between white and pial surfaces One value per vertex mm white/gray surface

Curvature (Radial) Circle tangent to surface at each vertex Curvature measure is 1/radius of circle One value per vertex Signed (sulcus/gyrus)

Inter-subject registration Gyrus-to-Gyrus and Sulcus-to-Sulcus Some minor folding patterns won’t line up Atlas registration is probabilistic, most variable regions get less weight. Done automatically in recon-all Template

Query Design Estimate Contrast - QDEC Average brain

Advantages of FreeSurfer Analysis of separate components of volume – thickness and surface area Geometry is used for inter-subject registration (major sulcal and gyral patterns) 2-D surface smoothing versus 3-D volume smoothing – more biologically meaningful

Temporal Lobe Epilepsy (MTS) Regular VBM - Volume DBM - Volume/Shape FreeSurfer - Cortical Thinning

Temporal Lobe Epilepsy (MR-negative) Volume Deformation/ Shape Cortical Thinning

Use FreeSurfer Be Happy

Diffusion Tensor Imaging (DTI) Diffusion MRI White Matter Organisation

Diffusion Tensor Imaging (DTI)

Measuring Anisotropy λ1 λ3 λ2 Eigenvectors: the 3 directions Eigenvalues: the rate of diffusion, λ1, λ2 and λ3 Apparent diffusion Coefficient (Mean diffusivity) = average of λ1, λ2 and λ3 Direction of least resistance to water diffusion, λ1

Tractography

Tractography

Tractography Cortical Spinal Tract

Voxel-based Morphometry for dMRI Issues with regular VBM analysis Not-perfect alignment Smoothing - arbitrary Tract-based Spatial Statistics Smith et al. 2006 – FMRIB Fractional Anisotropy (FA) map DTI-TK with TBSS High level warping using all the tensor information for better alignment

DTI and Schizophrenia Widespread FA reduction in Schizophrenia versus controls

DeCC neuroimaging MDD = 153 HC = 153 Matched age and gender Gaussian Process Classifier LOOCV Accuracy = 59%