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

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

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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. - PowerPoint PPT Presentation

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

Page 1: Neuroimaging Processing : Overview ,  Limitations ,  pitfalls, etc . etc.

Neuroimaging Processing :

Overview, Limitations, pitfalls, etc. etc.

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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.

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Malhi et al. 2007

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MRI acquisition

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MRI BasicsWater = 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

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Magnetic Resonance Imaging (MRI)

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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

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Preprocessing: Structural MRI

Volume/Thickness/Surface Area/Curvature ….

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Structural MRIRegion of Interest (ROI)

Voxel based morphometry (SPM/FSL)Surface based morphometry (FreeSurfer)

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Structural MRIRegion of Interest (ROI)Voxel based morphometry (SPM/FSL)

Surface based morphometry (FreeSurfer)

Volume

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

Left

Right

Thickness

Surface Area

Curvature

Gyrification

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What can we measure in aRegion of Interest (ROI)?

Total volume

Shape

Average diffusion

Average blood flow

Average level of Glutamate

Average Dopamine levels

Region of Interest

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Region of Interest Manual v Automated Caudate

Hippocampus

Manual v FS

ICC 0.79

52% Volume Difference

Manual v FS

ICC 0.95

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What’s the problem with ROI?

FreeSurfer Manual

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

Manual FreeSurferTLEHC TLEHC

Volu

me

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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)

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Voxel-based Mophometry

MNI Brain

Original Segmentation Normalisation Modulation Smoothing

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VBM - Limitations

Accuracy of the spatial normalisationRegular 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

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FreeSurferThe cortex

Volume, thickness or surface area?Volume = surface area * thickness

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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

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Cortical CurvatureTemporal Lobe Epilepsy (MR-negative)Cortical curvature abnormality in the ipsilateral temporal lobe - Not explained by volume or thicknessPossible surrogate marker for malformations of cortical development

Ronan et al. 2011

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FreeSurfer

Cortical Reconstruction

Cortical Analysis - cortical thickness, surface are, volume, cortical folding and curvature

Cortical and sub-cortical segmentation

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Surfaces: White and Pial

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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

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Cortical Thickness

white/gray surface

pial surface• Distance between white and pial surfaces

• One value per vertex

mm

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Curvature (Radial)• Circle tangent to

surface at each vertex

• Curvature measure is 1/radius of circle

• One value per vertex

• Signed (sulcus/gyrus)

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Inter-subject registrationsubject 1 subject 2 subject 3 subject 4

Template

• 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

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Query Design Estimate Contrast - QDEC

Average brain

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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

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Temporal Lobe Epilepsy (MTS)

Regular VBM- Volume

DBM- Volume/Shape

FreeSurfer- Cortical Thinning

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Temporal Lobe Epilepsy (MR-negative)

Volume

Deformation/Shape

Cortical Thinning

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Use FreeSurfer

Be Happy

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Diffusion MRI

White Matter Organisation

Diffusion Tensor Imaging (DTI)

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Diffusion Tensor Imaging (DTI)

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Direction of least resistance to water diffusion, λ1

Eigenvectors: the 3 directions

Eigenvalues: the rate of diffusion, λ1, λ2 and λ3

Apparent diffusion Coefficient (Mean diffusivity) = average of λ1, λ2 and λ3

λ1

λ3

λ2

Measuring Anisotropy

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Tractography

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Tractography

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Tractography

Cortical Spinal Tract

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Voxel-based Morphometry for dMRI

Issues with regular VBM analysisNot-perfect alignment Smoothing - arbitrary

Fractional Anisotropy (FA) map

Tract-based Spatial StatisticsSmith et al. 2006 – FMRIB

DTI-TK with TBSSHigh level warping using all the tensor information for better alignment

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DTI and Schizophrenia

Widespread FA reduction in Schizophrenia versus controls

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DeCC neuroimaging

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