Advanced Magnetic Resonance Imaging techniques in the diagnosis of Alzheimer’s Disease and evaluation of AD pathogenesis in an

aging brain

Dr. Henry Mak (BSc, MBChB, MD, FRCR) - HKU Alzheimer's Disease Research Network

- Research Centre of Heart, Brain, Hormone & Healthy Aging

- State Key Laboratory of Brain & Cognitive Sciences- Diagnostic Radiology, LKS Faculty of Medicine, University of

Hong Kong

Content

• Traditional role of MRI in Dementia or AD diagnosis

• AD pathogenesis and imaging biomarkers• New roles of advanced MR techniques in AD

diagnosis and pathogenesis

3

A global problem…

Background• 36 million people suffering from dementia (40% in

China and India)• Cost for caring this population ~ US$600+ billion• Four-fold ↑ by 2050 to 106.8 million (59% in China

and India)• Alzheimer’s Disease: 2/3 Vascular (& Other) Dementias: 1/3

`

Alzheimer’s Disease International, 2011 Brookmeyer, Alzheimer’s & Dementia 2007Brayne et al. BJ Psych 1995

Histopathological evidence in AD

Hallmarks of disease: intraneuronal neurofibrillary tangles (NFT), neuropil threads (paired helical filaments containing abnormal phosphorylated tau proteins), extracellular β-amyloid plaques

Progression of neurofibrillary changes follows a specific pattern: entorhinal area→ hippocampal formation (CA1) (Braak I & II) → temporal neocortex → multimodal associative limbic areas (Braak III

& IV) → other isocortical areas (Braak V & VI)

Neuronal and cortical synapse loss

Braak & Braak, Neurobiol Aging 1995West et al, The Lancet, 1994

Neuroimaging in Dementia

Osborn et al. Alzheimer’s Disease in DIAGNOSTIC IMAGING BRAIN

Neuroimaging in Dementia

American Academy of Neurology recommends at least 1 structural scan (CT or MRI) in the routine evaluation of dementia

Knopman DS et al. Neurology 2001; 56: 1143-1153.

(↑Amyloid β-PET-CT or ↓Aβ CSF)

(↑tau PET-CT or ↑tau CSF)

(Genetic study- APP, PS1, PS2)

MR perfusion (or FDG-PET), 1H MR spectroscopy- NAA MRI volumetry - hippocampus /ectorhinal cortex

Biomarkers

1H MR spectroscopy- Glu, GABA, mI functional MRIMR perfusion (or FDG-PET) Diffusion tensor imaging

Proposed biomarkers (MRI-based highlighted in red) for detection of AD pathological changes in the amyloid cascade hypothesis.

Hardy & Selkoe 2002

Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria

Probable AD: A plus one or more supportive features B, C, D, or E

Core diagnostic criteria A. Presence of an early and significant episodic memory impairment that

includes the following features: …………………………………………….

Supportive features B. Presence of medial temporal lobe atrophy Volume loss of hippocampi, entorhinal cortex, amygdala evidenced on

MRI with qualitative ratings using visual scoring (referenced to well characterised population with age norms) or quantitative volumetry of regions of interest (referenced to well characterized population with age norms)

C. Abnormal cerebrospinal fluid biomarker • Low amyloid β1–42 concentrations, increased total tau concentrations,

or increased phospho-tau concentrations, or combinations of the three • Other well validated markers to be discovered in the futureD. Specific pattern on functional neuroimaging with PET • Reduced glucose metabolism in bilateral temporal parietal regions • Other well validated ligands, including those that foreseeably will

emerge such as Pittsburg compound B or FDDNP E. Proven AD autosomal dominant mutation within the immediate family

Dubois et al. Lancet Neurol 2007; 6: 734-746

Smith JAD 2010

Ideal neuroimaging marker

• i) detects early neurodegenerative pathology early and specific, preclinical stage, phenotypes and other dementias

• ii) reflects pathological stage across entire severity spectrum

• iii) predicts disease conversion • iv) monitors therapeutic intervention sensitivity to

therapeutic response, repetitive use

Who is AD?

Hippocampal Atrophy

Scheltens P et al. JNNP 1992; 55: 967-972

Score 0 Score 2 Score 4

Visual assessment of Medial Temporal Lobe atrophy

Scheltens 5-point scale

Hippocampal volumetry

control AD

Jack Jr CR et al. Neurology 1992; 42: 183-188.

hippocampal formation (HF)total intracranial volume (TIV)dementia of the Alzheimer type (DAT)

Manual volumetry

• Control group – how good? • Normalization with total intracranial volume• ‘Analyze’ software – inter-observer and intra-

observer variations• Probable AD – gold standard?• A-priori hypothesis

Ashburner et al, Lancet Neurology, 2003

25

Voxel-based Morphometry (VBM)

Template created in DARTEL toolbox with 39 subjects

Create the right and left hippocampi mask in Marina (Masks for Region Of Interest Analysis, version 0.61, B. Walter, Giessen, German, 2002) with MNI template.

Logistic regression analysis

Method

(with normalization)

Baseline model*

Sensitivity

(%)

73.7

Specificity

(%)

70.0

Prediction

Accuracy

(%)

71.8

Manual RH 89.5 85.0 87.2

Manual LH 78.9 90.0 84.6

Standard VBM RH-ROI 84.2 90.0 87.2

Standard VBM LH-ROI 73.7 80.0 76.9

DARTEL RH-ROI 84.2 90.0 87.2

DARTEL LH-ROI 89.5 85.0 87.2

*Baseline model only includes age, gender and education as explanatory variables in the logistic regression analysis

Mak KF et al., JAD, 2011

Regional Patterns - AD versus FTD

FTD - frontal atrophy, MTL asymmetrical atrophy most severe in anterior part

AD - more severe MTL/hippocampal atrophy, equally affected MTL on both sides

Mesial Temporal lobe/Hippocampal atrophy - not specific for AD, even greater in some cases of semantic dementia - EC volume loss similar in FTD and AD

Glodzik-Sobanska et al. Neuroimag Clin N Am 2005;15:803-26.

Frontotemporal Lobar degeneration (FTLD)

Clinical Subtypes:

Frontotemporal dementia (FTD)

Semantic dementia (SD)

Nonfluent aphasia (NFA)

Frontotemporal dementia (FTD) with extrapyramidal symptoms• Corticobasal degeneration• Progressive supranuclear palsy

Frontotemporal dementia (FTD) with motor neuron disease

Atrophy patterns for discrimination of FTLD subtypes

Classification based on:

1. Frontal versus Temporal

2. Right versus Left

FTD- bilateral frontotemporal, R>L

SD- predominantly temporal

NFA- bilateral frontotemporal, R<L

Progressive Supranuclear Palsy

Perfusion

DeKosky ST et al. Alzheimer Dis Assoc Disord 1990: 4, 14–23.

Assessing utility of single photon emission computed tomography (SPECT) scan in Alzheimer disease: Correlation with cognitive severity.

18F-FDG PET

Which one is AD?

Cerebral Amyloid Angiopathy

Binswanger’s Disease , Vascular Dementia

Bastos et al. Top Magn Reson Imaging 2004; 15: 369-89.

Mixed Dementia

Bastos et al. Top Magn Reson Imaging 2004; 15: 369-89.

Arterial Spin Labeling (or tagging)

• Intrinsic water as contrast, NON-INVASIVE • Absolute quantification of regional cerebral blood

flow (rCBF) • Other than CBF, probe into hemodynamic parameters

such as arterial transit time (aTT), arterial blood volume (aBV) etc.

Golay X, et al. Top Magn Reson Imag 2004; 15: 10-27.

Petersen ET. Non-invasive measurement of perfusion: a critical review of arterial spin labelling techniques. Br J Radiol; 2006: 688-701.

Johnson NA et al. Radiology 2005; 234: 851-59.

AD vs CN

Patterns of cerebral hypoperfusion in AD & MCI measured with ASL MR Imaging

Multiple post-labeling delay ASL

1. obtains hemodynamic parameters (other than CBF) such as arterial transit time (aTT) and arterial blood volume (aBV), as they also pertain to cerebrovascular integrity

2. offers the uniqueness to assess and separate quantitatively & independently the delay in arrival of the labeled bolus (↑aTT), as well as the ↓CBF secondary to a reduction in metabolism (typically seen) in AD patients.

AGE AND ALZHEIMER’S DISEASE RELATED EFFECTS ON CEREBRAL BLOOD FLOW ANDARTERIAL TRANSIT TIME OF POSTERIOR CINGULATE CORTEX WITH SERIAL ARTERIALSPIN LABELING MRI

Zhu X et al. Alzheimer's & Dementia 2007; 3 (3) Supplement, S176-S177.Schuff N et al. The British Journal of Radiology, 2007; 80: S109–S114.

In CN, CBF negatively correlated with age (R2 = 0.55, p= 0.002), declining

1.5% per year, ATT increases with age (R2 = 0.27, p= 0.04), 0.5% per year.

Independent of age, AD with significant CBF reduction (35% lower than

controls, p= 0.04); MCI CBF reduction intermediate between CN & AD

Independent of age, AD with marked increase of ATT (34% longer than

CN, p= 0.005). Increased ATT in MCI was a trend (18% longer, p= 0.06)

CBF alone correctly classified 82% of patients and 67% of MCI

Adding ATT improved classification to 100% for AD and 78% for MCI

Conclusions:

1. new finding is prolonged ATT in AD and MCI

2. ATT changes independent of CBF changes and improved the classification of AD and MCI.

Between group SPM analysis of ASL CBF maps of AD as compared to controls, with regional hypoperfusion (highlighted in red) overlaid onto the T1W map.

R LMak HKF et al. J Alzheimer’s Dis 2012

ROIs

Mean CBF (mL/100g/min) Mean aBV (%) Mean aTT (s)

HC AD p-value HC AD p-value HC AD p-value

Superior Frontal Gyrus

Left32.9

(12.7)36.5

(15.0)0.561

0.260(0.262)

0.265(0.172)

0.9640.556

(0.164)0.588

(0.160)0.667

Right34.6

(22.5)36.8

(22.6)0.834

0.201(0.202)

0.281(0.240)

0.4270.570

(0.293)0.594

(0.175)0.836

Superior Temporal

Gyrus

Left44.4

(14.8)36.5

(19.1)0.231

0.720(0.378)

0.402(0.200)

0.012*0.547

(0.205)0.599

(0.190)0.498

Right47.2

(15.8)36.7

(20.2)0.134

0.618 (0.400)

0.563(0.236)

0.6650.578

(0.240)0.574

(0.201)0.964

Inferior Frontal Gyrus

Left44.2

(12.0)36.7

(8.84)0.075

0.559(0.309)

0.524(0.243)

0.7460.310

(0.137)0.428

(0.134)0.031*

Right43.1

(10.6)35.1

(10.3)0.053

0.414(0.297)

0.450(0.200)

0.7190.342

(0.169)0.435

(0.202)0.198

Inferior Parietal Gyrus

Left49.5

(17.7)40.9

(11.3)0.144

0.540(0.411)

0.412(0.170)

0.3060.336

(0.180)0.447

(0.166)0.105

Right51.1

(25.3)42.0

(11.8)0.158

0.823(0.532)

0.522(0.212)

0.037*0.357

(0.147)0.456

(0.178)0.168

Cingulate Gyrus

Middle52.4

(11.3)42.0

(11.0)0.021*

0.849(0.439)

0.699(0.508)

0.4110.260

(0.134)0.370

(0.139)0.043*

Posterior48.6

(16.9)35.0

(13.4)0.028*

0.622(0.419)

0.354(0.220)

0.049*0.462

(0.163)0.576

(0.182)0.091

Mean CBF, aBV, and aTT values (with standard deviation) of healthy controls (HC) and AD patients at the various ROIs. * denotes a result that is statistically significant (p<0.05)

MMSE (p-value) ADAS_cog (p-value)

Left Superior Frontal Gyrus Control (N=11; N=9) .095 (0.781) -0.300 (0.433)

Case (N= 9; N=8) .049 (0.900) -0.405 (0.319)

Control + Case (N=20; N=17) -0.092 (0.698) -0.147 (0.573)

Right Superior

Frontal Gyrus

Control (N=11; N=9) -0.511 (0.108) 0.088 (0.822)

Case (N= 9; N=8) -0.061 (0.877) -0.294 (0.480)

Control + Case (N=20; N=17) -0.139 (0.559) -0.150 (0.566)

Left Superior Temporal Gyrus Control (N=15; N=13) 0.336 (0.220) -0.608 (0.027*)

Case (N= 13; N=12) 0.436 (0.137) -0.640 (0.025*)

Control + Case (N=28; N=25) 0.389 (0.041*) -0.537 (0.006*)

Right Superior

Temporal Gyrus

Control (N=15; N=13) -0.097 (0.732) -0.416 (0.157)

Case (N= 13; N=12) -0.120 (0.696) -0.144 (0.656)

Control + Case (N=25) 0.205 (0.295) -0.390 (0.054)

Left Inferior Frontal Gyrus Control (N=15; N=13) 0.290 (0.295) 0.027 (0.930)

Case (N= 13; N=12) 0.660 (0.014*) -0.756 (0.004*)

Control + Case (N=28; N=25) 0.497 (0.007*) -0.497 (0.011*)

Right Inferior Frontal Gyrus Control (N=15; N=13) 0.310 (0.261) 0.075 (0.809)

Case (N= 13; N=12) 0.505 (0.078) -0.673 (0.016*)

Control + Case (N=28; N=25) 0.504 (0.006*) -0.508 (0.009*)

Left Inferior Parietal Gyrus Control (N=15; N=13) 0.315 (0.252) 0.010 (0.973)

Case (N= 13; N=12) 0.500 (0.082) -0.634 (0.027*)

Control + Case (N=28; N=25) 0.407 (0.031*) -0.397 (0.050*)

Right Inferior Parietal Gyrus Control (N=15; N=13) 0.305 (0.269) -0.253 (0.405)

Case (N= 13; N=12) 0.595 (0.032*) -0.674 (0.016*)

Control + Case (N=28; N=25) 0.394 (0.038*) -0.451 (0.024*)

Middle Cingulate Gyrus Control (N=15; N=13) 0.430 (0.110) -0.294 (0.329)

Case (N= 13; N=12) 0.366 (0.218) -0.477 (0.117)

Control + Case (N=28; N=25) 0.536 (0.003*) -0.528 (0.007*)

Posterior Cingulate Gyrus Control (N=15; N=13) 0.202 (0.470) -0.332 (0.268)

Case (N= 13; N=12) 0.167 (0.585) -0.309 (0.328)

Control + Case (N=28; N=25) 0.435 (0.021*) -0.499 (0.011*)

Pearson Correlation between CBF and MMSE and ADAS cog scores in different brain regions

* denotes a result that is statistically significant (p<0.05)

MMSE (p-value) ADAS_cog (p-value)

Left Superior Frontal Gyrus Control (N=11; N=9) -0.519 (0.102) 0.238 (0.538)

Case (N= 9; N=8) 0.364 (0.335) 0.062 (0.885)

Control + Case (N=20; N=17) -0.065 (0.785) 0.182 (0.485)

Right Superior

Frontal Gyrus

Control (N=11; N=9) -0.542 (0.085) 0.139 (0.722)

Case (N= 9; N=8) 0.329 (0.387) 0.170 (0.687)

Control + Case (N=20; N=17) -0.073 (0.760) 0.156 (0.550)

Left Superior Temporal Gyrus Control (N=15; N=13) -0.161 (0.567) -0.197 (0.519)

Case (N= 13; N=12) 0.577 (0.039*) 0.078 (0.810)

Control + Case (N=28; N=25) 0.018 (0.926) 0.087 (0.678)

Right Superior

Temporal Gyrus

Control (N=15; N=13) -0.138 (0.625) -0.388 (0.190)

Case (N= 13; N=12) 0.411 (0.162) 0.222 (0.488)

Control + Case (N=25) 0.095 (0.631) -0.018 (0.932)

Left Inferior Frontal Gyrus Control (N=15; N=13) -0.100 (0.724) -0.020 (0.949)

Case (N= 13; N=12) -0.671 (0.012*) 0.519 (0.084)

Control + Case (N=28; N=25) -0.550 (0.002*) 0.495 (0.012*)

Right Inferior Frontal Gyrus Control (N=15; N=13) -0.332 (0.227) 0.003 (0.992)

Case (N= 13; N=12) -0.531 (0.062) 0.167 (0.603)

Control + Case (N=28; N=25) -0.429 (0.023*) 0.249 (0.231)

Left Inferior Parietal Gyrus Control (N=15; N=13) -0.005 (0.986) 0.116 (0.706)

Case (N= 13; N=12) -0.521 (0.068) 0.496 (0.101)

Control + Case (N=28; N=25) -0.415 (0.028*) 0.416 (0.039*)

Right Inferior Parietal Gyrus Control (N=15; N=13) -0.220 (0.431) -0.060 (0.847)

Case (N= 13; N=12) -0.317 (0.291) 0.583 (0.047*)

Control + Case (N=28; N=25) -0.363 (0.058) 0.432 (0.031*)

Middle Cingulate Gyrus Control (N=15; N=13) 0.130 (0.645) 0.118 (0.700)

Case (N= 13; N=12) -0.216 (0.477) 0.038 (0.906)

Control + Case (N=28; N=25) -0.380 (0.046*) 0.307 (0.136)

Posterior Cingulate Gyrus Control (N=15; N=13) 0.092 (0.745) 0.086 (0.781)

Case (N= 13; N=12) -0.110 (0.720) 0.084 (0.796)

Control + Case (N=28; N=25) -0.306 (0.113) 0.298 (0.148)

Pearson Correlation between aTT and MMSE and ADAS cog scores in different brain regions

Niwa K et al.Am J Physio Heart Circ Physio 2002; 283: H315-H323.

Cerebrovascular autoregulation is profoundly impaired in mice overexpressing amyloid precursor protein.

Niwa K et al.Am J Physio Heart Circ Physio 2002; 283: H315-H323.

Age matched, left: level of basal ganglia, right: level of centrum semiovale

Axial Tc-99m ECD SPECT Multi-infarct Dementia

Axial Tc-99m ECD SPECT in patient with clinical PSP presentation

Hippocampal Volumetry

Right Left146

147

148

149

150

151

152

153

154

155Mean normalised volume of hippocampi

in AD and HC

ADHC

Hippocampus

Nor

mal

ised

vol

ume

ratio

**

**

Error bars are standard error of the mean** indicates significance at p<0.001

Cerebral Perfusion

Error bars are standard error of the mean.* indicates significance at p<0.03

MC PC0

10

20

30

40

50

60

Mean CBF of Cingulate Gyri ROIs in AD and HC

ADHC

Cingulate Gyrus

CBF

(ml/

100g

/min

)

* *

ROC curves – Single parameter

AUC:MC – 0.749PC – 0.744RH – 0.928LH – 0.872

ROC curves – Two parameters

AUC:MR – 0.938ML – 0.887PR – 0.933PL – 0.923

ROC curves – 3-4 parameters

AUC:MRL – 0.944PRL – 0.938MPR – 0.933MPL – 0.908MPRL – 0.938

Severity index (SI) = 0.5 * (MMSE + 30 – 2 x ADAS-cog)

SUMMARY

• Usage of advanced MRI techniques in diagnosis of AD and its phenotypes:

• - hippocampal volume useful but not entirely specific (for discrimination from frontotemporal dementia, mixed dementia etc), need topographical distribution of cortical atrophy by computed-assisted neuroanatomy

• - perfusion patterns in different diseases

• - combination of structural MRI and MR perfusion achieved higher diagnostic accuracy

Summary

• Mechanism of AD pathogenesis:

• ASL MR perfusion• -CBF reduction in AD can be due to either neural

(metabolic less active) or vascular (cerebral microvasculature abnormality) dysfunction

• -↓aBV & ↑aTT suggest underlying hemodynamic disturbance in addition to/other than metabolic decline

Acknowledgements to collaborators in AD research

HKU Alzheimer’s Disease Research Network - Chu LW, Chang RCC, Lee TMC, Chan KH, Law ACK, Song Y, Weekes B, Lum T, Kwan J

HKU Diag. Rad. (past PhD students) - Chiu C, Qian W, Zhang L

HK Polytechnic University – Chan C

HK Sanatorium & Hospital- Ho GCL, Leung E

Philips Healthcare, HK- Chan Q

Aarhus University Hospital, Denmark - Petersen ET

ETH, Zurich - Henning A

UCL Institute of Neurology, United Kingdom – Golay X

University Vita-Salute San Raffaele, Milan, Italy- Abetalebi J

Johns Hopkins University, USA - Lu H, Edden R

Mayo Clinic, USA - Vemuri P

Stanford University, USA - Wintermark M

University of California, San Francisco, USA - Rosen H

1H-MRS Metabolites

Proton Magnetic Resonance Spectroscopy (1H-MRS) at 3Tesla

FunctionChange in aMCI / AD

N-acetyl-aspartate (NAA)

Neuronal marker, [NAA] correlates with neuronal

density and function

↓/↓↓

Creatine (Cr) Cerebral energy metabolism marker, relatively stable - *

Choline (Cho) Phospholipid metabolism, reflect membrane synthesis

and degradation

↑/↑↑?

Myo-inositol (mI) Astrocyte/Glial cell marker ↑/↑↑Glutamate (Glu) + Glutamine (Gln)

(= Glx)

Glu excitatory neurotransmitter, Gln precursor/regulatory

na /↓or↑??

Gamma aminobutyric acid (GABA)

inhibitory neurotransmitter na/↓?

Kentarci JMRI 2013

Kentarci et al., Radiology 2008

Kentarci JMRI 2013

Ernst et al. Radiology 1997

In healthy aging, there is an optimal balance in the glutamatergic system. Excess glutamate will be taken up by the glial cells via the excitatory amino acid transporter (EAAT) and converted to glutamine.

Butterfield & Pocernich CNS drugs 2003

Decreased Glx in 2 prior studies:

Antuono et al., Neurology 2001

Hattori et al., NeuroReport 2002

Bai et al., JMRI 2014

Diffusion Tensor Imaging

Color-coded FAFiber tracking done with the TrackVIS software (see trackvis.org)

Stahl Radiology 2007

Differences of FA values in voxel-based analysis after controlling for volumetric atrophy

Anatomic Region t-value

YG>AD

Medial Frontal (R) 9.03

Medial frontal (L) 7.29

Supramarginal (L) 6.61

Cingulate Area(L) 7.28

Inferior Parietal (R) 7.59

Claustrum (L) 7.43

Thalamus (L) 6.91

Extranuclear (R) 7.17

Extranuclear (R) 7.20

Sub-gyral (L) 7.16

YG>OL

Medial Frontal (R) 7.69

Anterior Frontal (R) 6.99

Medial Frontal (L) 7.24

YG-OL YG-AD OL-AD

FA genu 0.004 0.000 0.045

L-AIC/EC 0.013 0.001 NS

R-AIC/EC 0.012 0.000 NS

splenium NS 0.042 NS

L-PIC NS NS NS

R-PIC NS 0.033 NS

number genu 0.001 0.000 NS

L-AIC/EC NS 0.014 0.037

R-AIC/EC NS 0.007 NS

Length(mm) genu 0.001 0.000 NS

Volume (ml) genu 0.022 0.045 NS

p values shown for the difference among 3 groups

Tractography metrics results

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***

*

****

#

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#

##

0

0. 2

0. 4

0. 6

0. 8

1

1. 2

1. 4

1. 6

1. 8

2

DA DR DA DR DA DR DA DR DA DR DA DR

Genu L-AI C R-AI C L-PI C R-PI C Spl en

Diff

usi

vity

(0.

001

mm/s

)

YGOLDAD

Mean ± SEM for axial (DA) and radial (DR) diffusivity for the six fiber bundles in the normal young and old, and AD groups.

#p<0.05 for the difference between the normal young and old adults; *p<0.05 for the difference between the normal young adults and AD

1. Alteration of white matter integrity measured by FA in normal aging involves mainly the frontal lobes, even after correcting for confounding atrophy- indicate that late myelinating fiber bundles more susceptible to aging effect, similar to previous study (Bennett 2009)

2. White matter loss in anterior brain regions were more obvious in AD and extended to posterior regions such as splenium and parietal lobes, with more severe white matter loss in anterior than posterior regions in AD

3. Tractography metrics (volume, number and length in addition to FA) confirmed vulnerable late myelinating fibers in normal aging, and even early myelinating fiber tracts decline in AD

4. Higher DR & DA: AD>OL>YG, reflect myelin breakdown

Bennett et al. Age related differences in multiple measures of white matter integrity: a DTI study of healthy aging. Hum Brain Mapping 2010; 31:378-390.

Smith CD, JAD, 2010

Jack et al., Lancet Neurology 2010

Biomarkers in AD

Visual assessment of Medial Temporal Lobe atrophy by MRI 磁共振目测评估内侧颞叶萎缩

Height of hippocampal formation (A) 海马高度Width between hippocampal formation and brainstem (B) 海马结构与脑干的宽度Width of choroid fissure (C) 脉络膜裂宽度Width of temporal horn (D) 颞角宽度

Sensitivity - 81%Specificity - 67%Accuracy (AD vs controls) – 74%

Interobserver agreement （观察者之间一致性） - kappa 0.59*

Scheltens P et al. J Neuro Neurosurg Psychiatry 1992; 55: 967-972.*Scheltens P et al. J Neurol 1995; 242: 557-560.

Recent VBM (SPM8) hippocampal-ROI study (Guo 2014) achieved 82% accuracy in discriminating AD from HC, attributed to registration errors.

Processing in automated software could be skill-demanding (DARTEL, MARINA), as well as variations in normalization templates, level and type of correction, threshold for statistical map display, choice of smoothing kernel size.

Validation of these automated imaging biomarkers could help in enriching clinical trials of disease modifying agents

Guo et al., Neurosci Bull, 2014

Clinical subtypes of mild cognitive impairment (MCI)

• aMCI: amnesic form of MCI• md-MCI: multiple domain MCI, such as language,

executive function & visuospatial skills• md-MCI+a: multiple domain with a memory

impairment• md-MCI-a: multiple domain without a memory

impairment• sd-MCI-a: single domain non-amnesic MCI, such as

language or visuospatial skills

Experiment in patient with Moyamoya Disease

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Angiography flow (ml/100g/min) arterial arrival time (ms)

Minoshima S et al. Ann Neurol 1997; 42: 85-94.

18F-FDG PET