Quantitative three-dimensional proton magnetic resonance spectroscopy in Multiple Sclerosis and Traumatic Brain Injury Iva...
Presentation outline <ul><li>Introduction to MRI and MR Spectroscopy </li></ul><ul><li>Thesis Aim 1:  To improve quantific...
Magnetic Resonance Imaging (MRI) <ul><li>Advantages over other types of neuroimaging: </li></ul><ul><li>non-ionizing </li>...
MR techniques <ul><li>T1/T2-weighted (conventional) MRI </li></ul><ul><li>Diffusion MRI </li></ul><ul><li>Perfusion MRI </...
Conventional  MRI Anatomy Metabolism vs.  1 H-MRS
Cell classes and metabolite distribution in the brain
Motivation for MRS <ul><ul><li>Disease-induced changes (targets) may manifest in the metabolism ( MRS )  before  the anato...
MRS methods Single voxel  Multivoxel  Whole-brain Pros: Cons: Availability Simplicity Real-time results  Spatial resolutio...
Data interpretation Qualitative Pixel values have numeric meaning  only in relation to neighboring pixels from the same da...
MRS Quantification parameters <ul><li>Acquisition specific </li></ul><ul><li>Repetition time ( TR ) </li></ul><ul><li>Time...
Presentation outline <ul><li>Introduction to MRI and MR Spectroscopy (MRS) </li></ul><ul><li>Thesis Aim 1:  To improve qua...
<ul><li>For the purpose of quantification, the use of a single  T 2  value per metabolite is sufficient for controls of al...
Introduction <ul><li>MRS signal decays with a constant of  T 2 </li></ul><ul><li>Need  TE  = 0  to measure true spin densi...
Introduction Differences in  T 2   need to be taken into account to avoid interpreting them as concentration differences
Methods T 2  calculation 1  Fleysher,  et al . MRM   2007 <ul><li>Two-point method: </li></ul><ul><li>Developed by Fleyshe...
Methods  3D  1 H-MRS <ul><li>PRESS with water suppression: 10 AP ×8 LR × 4 IS  = 320 cm 3  VOI </li></ul><ul><li>TE 1 / TR...
Age dependence of regional proton metabolite  T 2 s in normal brain 1 1 Kirov  et al . MRM 2008 Metabolite  T 2 s in aging...
MRI/MRS – Adolescent WM ROIs: (a) Corona radiata (b) Genu (c) Splenium (d) Posterior WM  Kirov  et al . MRM 2008 Metabolit...
MRI/MRS – Elderly Kirov  et al . MRM 2008 Metabolite  T 2 s in aging
Kirov  et al . MRM 2008 Metabolite  T 2 s in aging <ul><li>GM ROIs: </li></ul><ul><li>Putamen </li></ul><ul><li>Caudate </...
Results: g lobal  T 2 s Histograms of  T 2 s from all 320 voxels for all adolescents and elderly subjects Kirov  et al . M...
Kirov  et al . MRM 2008 Metabolite  T 2 s in aging Results:  regional  T 2 s
Conclusion <ul><li>Intra - and  inter -subject  T 2  variability is low in  all  age groups </li></ul><ul><li>T 2  decreas...
Proton metabolite  T 2 s in RR MS lesions and normal-appearing tissue 1 1 Kirov  et al . Radiology 2010 Metabolite  T 2 s ...
MRI/MRS – RR MS patient 21F Disease duration: 2.6 yrs. EDSS: 1.5 VOI lesion volume: 23 cc Kirov  et al . Radiology 2010 Me...
<ul><li>GM : </li></ul><ul><li>a   caudate </li></ul><ul><li>b  putamen  </li></ul><ul><li>c   globus pallidus  </li></ul>...
ROIs: lesions a   isointense b  hypointense Kirov  et al . Radiology 2010 Metabolite  T 2 s in RR MS
Results:  global  T 2 s Kirov  et al . Radiology 2010 Metabolite  T 2 s in RR MS Histograms of  T 2 s from all 320 voxels ...
Results : regional  T 2 s Kirov  et al . Radiology 2010 Metabolite  T 2 s in RR MS
Conclusion <ul><li>The  intra - and  inter-  patients’  T 2   variations are found to be of the same magnitude as those re...
<ul><li>For the purpose of quantification, the use of a single  T 2  value per metabolite is sufficient for controls of al...
Presentation outline <ul><li>Introduction to MRI and MR Spectroscopy (MRS) </li></ul><ul><li>Aim 1:  To improve quantifica...
<ul><li>Metabolic abnormalities (mI, Cho, Cr) suggestive of astrogliosis and de/re-myelination precede axonal damage (NAA)...
<ul><li>Leading cause of non-traumatic neurological disability in the young and middle aged  </li></ul><ul><li>Cause is un...
Pathogenesis in the MS plaque Adapted from Frohman  et al ., NEJM 2006 <ul><li>T-cell activation towards self antigen </li...
Histological findings <ul><li>Pathology </li></ul><ul><li>blood-brain barrier breakdown </li></ul><ul><li>inflammatory inf...
<ul><li>T 1   and  T 2 -weighted MRI </li></ul>Radiological findings <ul><li>Diffuse  abnormalities are  not </li></ul><ul...
<ul><li>Can  detect diffuse pathology in ‘normal-appearing’ tissue </li></ul>Quantitative MRI Magnetization transfer imagi...
MR Spectroscopy indicates diffuse Multiple Sclerosis activity during remission 1 1 Kirov  et al . JNNP 2009 Diffuse diseas...
Methods: 3D  1 H-MRS PRESS with water suppression: VOI = 8 LR ×10 AP ×4.5 IS  = 360 cm 3   TE/TR  = 35/1800 ms, N=2 6 slic...
<ul><li>The sum of all 480 spectra results in a  single  spectrum indicative of WM status </li></ul><ul><li>1 H-MRS VOI:  ...
Single voxel  vs.  summation Kirov  et al . JNNP 2009 Diffuse disease activity in MS
Methods : post-processing (contd.) <ul><li>Frequency-align in reference to the NAA peak </li></ul><ul><li>Zero-out all vox...
<ul><li>To obtain metabolic concentrations </li></ul><ul><li>To measure atrophy </li></ul>Tissue fraction (T f ) = tissue ...
MRI/MRS – RR MS patient 42F Disease duration: 2.1 yrs. EDSS: 0 VOI lesion volume: 4.2 cc Kirov  et al . JNNP 2009 Diffuse ...
Summed spectra Each spectrum: –  is the sum of all voxels  in the VOI –  represents one subject –  gray line represents th...
Results a Years,  b inside the VOI, in cm 3 ,  c tissue fraction,  d p ‹0.01. *EDSS dominated by permanent visual impairme...
metabolic concentrations tissue  fraction Results Kirov  et al . JNNP 2009 Diffuse disease activity in MS
Metabolites as disease markers Localization Suspected pathology  Finding  mI   ↑   Cr ↑  Cho ↑   –  astrogliosis  de/re-my...
Since NAA loss and atrophy are observed in advanced disease these processes may precede (cause?) axonal damage <ul><li>Glo...
Presentation outline <ul><li>Introduction to MRI and MR Spectroscopy (MRS) </li></ul><ul><li>Thesis Aim 1:  To improve qua...
<ul><li>Defining the limits of metabolic change in the thalamus in mild traumatic brain injury (TBI) may help yield an obj...
Traumatic Brain Injury (TBI) <ul><li>5.3 million  people live with a TBI-related disability </li></ul><ul><li>1.5 million ...
<ul><li>Unlike “moderate” and “severe” TBI, mTBI patients usually have  no MR findings  -> diagnosis may be subjective </l...
<ul><li>Nature of mTBI injury:  Diffuse, heterogeneous, microscopic damage to individual neurons, which does not progress ...
<ul><li>Thalamus </li></ul><ul><li>reciprocal connections  </li></ul><ul><li>with  entire  cortex </li></ul><ul><li>‘ mini...
Characterizing mild TBI with proton MR spectroscopy in the thalamus 1 Subjects Controls:  17 age- and gender-matched Patie...
Methods: 3D  1 H-MRS PRESS with water suppression: VOI = 8 LR ×10 AP ×6 IS  = 480 cm 3   TE/TR  = 135/1600 ms, N=2 8 slice...
Kirov  et al . Brain Injury 2007 1 H-MRS of the thalamus in mTBI MRI/MRS – mTBI patient
Methods:  post-processing Kirov  et al . Brain Injury 2007 1 H-MRS of the thalamus in mTBI
Methods:  post-processing Kirov  et al . Brain Injury 2007 1 H-MRS of the thalamus in mTBI
Thalamic metabolic concentrations Kirov  et al . Brain Injury 2007 1 H-MRS of the thalamus in mTBI Results
Minimal detectable differences:  the minimal differences between patients and controls that would  produce significance ( ...
<ul><li>Detectable differences may be used to: </li></ul><ul><li>define the range of thalamic metabolic changes in mTBI </...
List of publications <ul><li>Kirov II , Liu S, Fleysher R, Fleysher L, Babb J, Herbert J, Gonen O. </li></ul><ul><li>Brain...
<ul><li>Thesis Committee:   </li></ul><ul><li>Qun Chen, Achim Gass, Oded Gonen, Matilde Inglese,  </li></ul><ul><li>Glyn J...
Supplemental slides - MS
Adapted from Hauser  et al ., Neuron 2006 Natural history
Possible causes of astrogliosis <ul><li>‘ Microplaques’ – diffuse blood-brain barrier (BBB) breakdown </li></ul><ul><li>BB...
Implications Model of temporal evolution of pathogenesis in NAWM Diffuse disease activity in MS
Supplemental slides – acute mTBI
Subjects <ul><li>14 patients (3 women) with mild TBI (mean GCS = 14.5) </li></ul><ul><li>Recent injury  </li></ul><ul><li>...
Methods
Results <ul><li>VOI </li></ul><ul><li>Patients: </li></ul><ul><li>average tissue volume fraction (0.94) </li></ul><ul><li>...
Supplemental slides - miscellaneous
T 1 -weighting bias in  T 2  measurement with DSE
Detectable Differences ? = 8.8 mM or a 13% difference from the healthy controls’ mean
MRS Methodology <ul><li>3D hybrid of 1D Hadamard with 2D chemical shift imaging 1 </li></ul>1 Gonen, MRM 1997, MRM 1998
<ul><li>Adolescents (n=4)  </li></ul><ul><li>Young adults (n=8)  </li></ul><ul><li>Middle age (n=2)  </li></ul><ul><li>Eld...
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Thesis Defence

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This is the presentation I gave for my thesis defense in November 2009.

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  • MRI has several clear advantages over other types of neuroimaging. It uses low frequency radiation in the radio wave spectrum. That is unlike CT which uses X-rays and PET and SPECT which use radioactive isotopes. Other non-ionizing techniques, such as EEG, MEG and near-infrared spec, can only image the cerebral cortex. MRI, on the other hand, can visualize the anatomy and assess various types of tissue function in the ENTIRE brain. EEG, magnetoencephalography MEG and near-infrared imaging NIRI
  • By far, the most popular MR technique is the T1/T2 weighted MRI, known as conventional MRI. Beyond that, there is a plethora of other MR techniques which add sensitivity and specificity to different types of injury. The MR tool used in this dissertation is MR Spectroscopy or MRS.
  • Conventional MRI image, in this case a T2-weighted FLAIR image, visualizes brain anatomy, whereas MRS gives information about metabolism from a defined brain region called a voxel. A proton MRS spectrum of the brain contains the peaks of four main metabolites: mI, Cho, Cr and NAA. The protons which contribute to the respective peaks are circled on this slide. The areas of each peak is proportional to the concentration of that metabolite. Conventional MRI, which provides anatomical images can be supplemented with MRS, which provides local metabolic information. MRS is the only non-invasive method for assessing human biochemistry in vivo As a clinical tool, it received approval of the FDA in 1995. Proton MRS (1H-MRS), in particular, is best suited for clinical applications since it uses the standard hardware of a MR scanner, and thus is readily incorporated in a routine MR protocol. In this way, localized MRS data adds specificity to structural MRI with information about local metabolism.
  • Let’s talk briefly about cell classes and the distribution of those metabolites in the brain. The CNS has 2 classes of cells – neurons and glia. The neuron receives innervation via its dendrites and if that input is excitatory it generates an action potential which travels through the axon, innervating the dendrites of other neurons. The major types of glia are the astrocytes and oligodendrocytes. The astrocytes contact the neuronal cell body providing structural and functional support. They also line the capillaries and their end-feet form the blood brain barrier. Oligodendrocytes secrete a lipid-rich membrane, which envelopes the neuronal axon, enabling the propagation of the action potential. In MS, as you will see later, myelin breakdown due to inflammation results in impaired signal propagation and subsequent axonal damage, resulting in a variety of neurological deficits. Cr and Cho are present in all 3 cell types, but are most abundant in oligodendrocytes and least in neurons. mI is unique to astrocytes, and NAA is unique to neurons, rendering the two markers for the respective cell type.
  • The motivation for MRS is that……. Therefore, … &gt;&gt;&gt;&gt; Presumably the beginning of a disease process is characterized by metabolic changes, which precede (or maybe even give rise to) changes in anatomy. Therefore, one of the potential uses of MRS is in earlier diagnosis.
  • A word about Data intepretation in MRS. Unlike cMRI, which is qualitative, in MRS values are usually converted to absolute concentrations. And while…. &gt;&gt;&gt;&gt;&gt; To understand why T2 times are important, we have to discuss the nature of data interpretation in MRS. Unlike conventional MRI, MRS data is quantitative. In MRI the relationship between pixels is relative. Qualitative MRI data is not comparable to other MRI scans, or even to MRI scans of the same person scanned at a different time. &gt;&gt;&gt;
  • We therefore need to account for many parameters that influence MRS quantification. Some parameters are set by the acquisition protocol. These are known and include TR, TE and voxel size. You have to take into account possible inhomogeneities in Bo and B1. And finally, you have to be aware that variations in the molecular environment can affect T1 and T2 relaxation times.
  • A quick introduction on how T2 times are related to quantification. Only at the junction of the function and y-axis is the signal = concentration. Therefore we would need a TE=0 to measure true concentration. But TE cannot be equal to 0, so the signal is T2-weighted. Therefore T2s are needed to find spin density, but they are not obtained for every patient, and the use of one T2 has been assumed to be ok for all tissue types and all people. In order to be sure that we are measuring true concentrations of disease changes and not different T2 times, we need to address this issue.
  • Here is a hypothetical example of how differences in T2 can bias results. Both patient and control have the same concentration of this metabolite, but different T2s. If we assume that patients’ T2 is the same as the controls’ we will infer that the patient has lower concentration of this metabolite
  • Measuring T2 is done by taking several measurements at diff. TEs and since c (concentration) is always the same, the signal decay curve and T2 can be extrapolated. Usually only the TEs are changed, keeping all other acquisition parameters the same, including the number of averages. Our method does not put such restrictions and optimizes all parameters per unit time. It shows that the most efficient use of 4 measurements is to take data at only 2 TEs, with different number of averages at each one. At TE1, 1 average, at TE2, 3 averages. This makes sense because at the short TE the SNR is very high and we don’t need more than 1 average. At the long TE, however, we take 3 averages, because the SNR is much worse. By optimizing protocol most averaging is done at the long TE, instead of having the same number of averages at both short and long TE. The method defines the TEs the following way: Method says TE1=0 or as close as possible to 0. The sequence that we have cannot go under 35ms. Optimal TE2 is very long, but there is a great signal decay at long TEs. T2 guess – error of incorrect guess is very small in the range of 150- 250. bad SNR = need averaging.
  • In the first study we looked at the “age dependence ….”.
  • We measured the T2s in 5 WM and 5 GM structures. Four of the WM ROIs are shown in this figure. This also shows the placement of the MRS VOI in the brain of an adolescent volunteer. To the right are the spectra at both TEs, with the ones obtained at 260ms magnified 2.5 times. Compare this to this anatomical section in the elderly.
  • The most dramatic difference of course is in the enlarged ventricles, result of normal brain atrophy that occurs with age. The rate that this happens is ¼%/year. Similar SNR , resolution, between adolescent and elderly.
  • One histogram represents one person. Each histogram shows the T2s of all 320 voxels of all 4 slices per person. T2 values are on the x-axis, and their frequency is on the y-axis. The histogram width is a measure of the T2 variations in that individual. The fact that for each metabolite histogram widths are similar in all people, indicates no change in intra-individual T2 variations. The other take home message from this figure is that in terms of T2s, people are also similar to one another and that does not change with age. This is evidenced by the fact that all histograms are overlapping. There are 2 take home messages from this figure. The T2 variations within individuals are similar in all people, of all age groups. the 2 age-extreme cohorts we can assess any age effects on the T2s: 1. No significant change in the mean T2s. 2. No change in the intra -subject distribution – the width of the histograms for adolescents and elderly are very similar. 3. No marked difference b/n individuals – the histograms at about age groups are stacked together in a similar fashion. Because we know the lower limit of the instrumental contribution to the CV we can estimate that almost all of the histograms width is due to biological variation.
  • This is a plot of age versus T2 separated by tissue type. Differences between GM and WM: 30% for NAA, 16% for Cr, and 10% for Cho. (most likely due to diffusion – in WM there is only perpendicular diffusion so relaxation rate is slower). It is not due to iron because the rates of T2 decline are the same for both WM and GM and there is more iron deposition in GM. Iron probably exerts a mesoscopic effect which is not seen here. The differences between T2s of either WM or GM in the adolescents and the elderly are: 12% for NAA, 6% for Cr, and 9% for Cho. It is likely that the observed T2 therefore, is a result of only the true T2 and diffusion effects. These age-corrections for the T2 s can improve the accuracy of absolute metabolic quantification by up to half the 12%, 6% and 9% differences of NAA, Cr and Cho between the young and elderly compared with the use of a single T2 per metabolite.
  • At what TE is the quantification error produced by the range of T2s 10%? This range includes 1, 2 and 3. Of course at shorter TEs, the error becomes smaller. ----At what TE is the quantification error produced by neglecting the range of T2 s (between structures, individuals and age groups) less than 10%? We accept 10% as acceptable error. Then we ask at what TE can we neglect the range of T2s that we measure. Here are 2 t2s that are very different. Let them represent the largest diff. in t2s that we find. The 2 t2s may be of diff. structures, people, and age groups, but represent the largest difference between t2s. Other way to say question is: if the range of t2s is neglected (i.e. you use one or the other) what is the incurred quantification error for the TE that you are using. So even if there is sign. difference between T2s, what’s important is to what error in quantification this will lead.
  • Emphasize the heterogeneity of the cohort.
  • PRESS is 90-180-180 pulses
  • 10 structures will be probed as well as both hypo- and iso-intense lesions
  • intra -subject T2 variations = FWHH of the histograms
  • The exact cause of MS is unknown. It is believed that an unknown antigen, perhaps a virus, triggers a T cell mediated auto-immune response. Activated lymphocytes digest the basement membrane of the blood-brain-barrier and enter the central nervous system. There they initiate a pro-inflammatory cascade which involves many cytokines including interferon-gamma and interleukin-17. This results in the accumulation of T, B cells, macrophages and microglia which destroy the myelin sheaths of axons. In the process, nitric oxide, oxygen free radicals and glutamate are released and these may damage the naked axons. Ca influx is also harmful. Continued attacks in the same location cause axonal transection and astrogliosis. Oligodendrocyte progenitors are recruited to the site of damage and some remyelination can take place, but it is usually incomplete.
  • Autopsy and biopsy findings confirm all of these processes in MS plaques. There is other type of pathology, however, which is diffuse, or in other words, found throughout the rest of the brain. Findings there are less pronounced/dramatic and are not consistent across studies, but include ……… In advanced disease there are reports of inflammation and demyelination.
  • What does the disease look like in-vivo on MRI? The gold standard in diagnosis and treatment monitoring of MS is T1 and T2-weighted MRI, due to their ability to detect the focal type of damage, manifesting as hyperintensities on this T2-weighted example. However, it is relatively non-specific to the occurring pathology inside the lesion and these lesions do not correlate well with clinical disability. T1 and T2-weighted MRI also lack sensitivity to detect the diffuse abnormalities and outside of the lesions tissue is normal-appearing.
  • The pathology in in normal-appearing tissue, however, is detectable with quantitative MR methods such as MRS. These methods have found abnormalities in all disease stages, MS subgroups, and some have been found to correlate with disability and lesion evolution.
  • In our study we used MRS and found diffuse abnormalities studying 21 recently diagnosed, mildly disabled, patients, during remission. 15-age and gender matched controls were also recruited.
  • The 360 cc VOI is composed of mostly WM, so our post-processing approach was to sum all 480 voxels in order to obtain a single spectrum indicative of diffuse processes throughout the WM.
  • On this slide we compare our approach to a single voxel experiment. While SNRs are comparable, the spectral resolution of the summation approach is far superior. This exploits the fact that the B0 homogeneity is better across small voxels, and the narrow linewidths are preserved in the sum. To achieve this the spectra need to be aligned the spectra before the summation. &gt;&gt;&gt;&gt; 1. better B0 homogeneity across small voxels. 2. better SNR by the square root of the number of voxels, because averaging all voxels mimics increasing the number of acquisitions that many times.
  • The whole post-processing method is presented here.
  • Absolute amounts were converted in concentrations by dividing by the tissue fraction inside the VOI. The tissue fraction is also a measure of atrophy inside the VOI, with low values corresponding to high atrophy.
  • Here only the results from the patients are shown. Average lesion volume inside the VOI was 2%, there was no atrophy or NAA loss. In contrast there were significant elevations in Cr, Cho and mI.
  • Box plots of these values reveal 9% increase for Cr, 14% for Cho and 20% for mI. Note that there was no statistical difference in the NAA or tissue fraction.
  • To interpret these findings we again look at the metabolite localization within the various CNS cell types. The size of the font is proportional to the amount of that metabolite in one cell type in respect to the others. NAA is unique to neurons and mI is only found in astrocytes. Therefore, our findings, we interpret as astrogliosis and de/remyelination.
  • Connectivity maps are gotten using diffusion imaging using a tractography algorhythm. “ connectivity based GM segmentation”.
  • Months from TBI: 2 months to 7 years
  • Peak areas were estimated by an automated fitting software (FITT)1, which uses acquisition specific information. Quantification errors due to the use of one T2 value per metabolite in the thalamus: NAA: 3.5 % Cr: 6.7 % Cho: 7.2 % Relative concentrations were converted to absolute by calculating a conversion factor that normalized the mean of each metabolite in the controls to its literature value and then applying it to patients and controls.
  • - We would need to recruit more than 60 patients to see significance at the 5% level. - The min. detectable differences define the sensitivity of our measurement. - They are the smallest metabolite concentration changes which would produce significant differences between patients and controls - To see significance, we can either improve the sensitivity of the technique or increase cohorts
  • ---If the mean of the patient distribution were outside of these ranges, we would have seen a significant difference. Since we did not, then the changes caused by mTBI are within that range. Can we define the limits of thalamic metabolic change imparted by mTBI? Yes, that is the same as defining the sensitivity of our technique. E.g.: The change in NAA must be between +/-13% with respect to the controls, otherwise we would have seen significant difference
  • At the long TE, magnetization has more time to recover between the pulses, but less at the end resulting in a different steady state. This results in a factor that does not cancel out in the equation for T2 calculation. This may result in an underestimation of T2s by as much as 30%. This can be overcome by a long TR or saturating the longitudinal magnetization and providing the same recovery time at all TEs. 20% uncertainty in T1 values leads to less thatn 3% bias in T2s.
  • Can we define the limits of thalamic metabolic change imparted by mTBI? Yes, that is the same as defining the sensitivity of our technique. E.g.: The change in NAA must be between +/-13% with respect to the controls, otherwise we would have seen significant difference
  • Thesis Defence

    1. 1. Quantitative three-dimensional proton magnetic resonance spectroscopy in Multiple Sclerosis and Traumatic Brain Injury Ivan Kirov Advisor: Oded Gonen Department of Physiology and Neuroscience Sackler Institute of Biomedical Sciences, New York University
    2. 2. Presentation outline <ul><li>Introduction to MRI and MR Spectroscopy </li></ul><ul><li>Thesis Aim 1: To improve quantification precision by measuring the extent to which T 2 changes in controls and Multiple Sclerosis influence apparent metabolic concentrations </li></ul><ul><li>Thesis Aim 2: To study diffuse metabolic changes in early relapsing-remitting Multiple Sclerosis </li></ul><ul><li>Thesis Aim 3: To assess (localized) thalamic damage in mild Traumatic Brain Injury </li></ul>
    3. 3. Magnetic Resonance Imaging (MRI) <ul><li>Advantages over other types of neuroimaging: </li></ul><ul><li>non-ionizing </li></ul><ul><li>not limited to cerebral cortex </li></ul><ul><li>can visualize anatomy and assess various types of tissue function </li></ul>
    4. 4. MR techniques <ul><li>T1/T2-weighted (conventional) MRI </li></ul><ul><li>Diffusion MRI </li></ul><ul><li>Perfusion MRI </li></ul><ul><li>Functional MRI </li></ul><ul><li>Magnetization transfer MRI </li></ul><ul><li>MR relaxometry </li></ul><ul><li>MR Spectroscopy (MRS) </li></ul>
    5. 5. Conventional MRI Anatomy Metabolism vs. 1 H-MRS
    6. 6. Cell classes and metabolite distribution in the brain
    7. 7. Motivation for MRS <ul><ul><li>Disease-induced changes (targets) may manifest in the metabolism ( MRS ) before the anatomy ( MRI ) </li></ul></ul><ul><ul><li>Corollary : By the time you see targets in MRI, you are merely engaging in “damage control” </li></ul></ul>
    8. 8. MRS methods Single voxel Multivoxel Whole-brain Pros: Cons: Availability Simplicity Real-time results Spatial resolution (4 – 20 cc) Spatial coverage (0.4 – 2% of brain) Spatial resolution (0.75 – 4 cc) Spatial coverage (10 – 30% of brain) Metabolic maps Lengthy post-processing “ Voxel bleed” Samples entire brain Short acquisition No misregistration errors in serial scans No localization Only NAA is quantified 3D hybrid of 1D Hadamard with 2D chemical shift imaging Gonen et al. , MRM 1997, MRM 1998
    9. 9. Data interpretation Qualitative Pixel values have numeric meaning only in relation to neighboring pixels from the same dataset Information is derived from differences in contrast and is visually interpreted Quantitative Values are converted to absolute concentrations (Preferred) comparison is to values in controls conventional MRI vs. MRS
    10. 10. MRS Quantification parameters <ul><li>Acquisition specific </li></ul><ul><li>Repetition time ( TR ) </li></ul><ul><li>Time to echo ( TE ) </li></ul><ul><li>Voxel size </li></ul><ul><li>Molecular environment factors </li></ul><ul><li>Longitudinal relaxation time ( T 1 ) </li></ul><ul><li>Transverse relaxation time ( T 2 ) </li></ul><ul><li>Instrumental </li></ul><ul><li>Magnetic field inhomogeneities in </li></ul><ul><li>External magnetic field (B 0 ) </li></ul><ul><li>RF-induced magnetic field (B 1 ) </li></ul>
    11. 11. Presentation outline <ul><li>Introduction to MRI and MR Spectroscopy (MRS) </li></ul><ul><li>Thesis Aim 1: To improve quantification precision by measuring the extent to which T 2 changes in controls and Multiple Sclerosis influence apparent metabolic concentrations </li></ul><ul><li>Thesis Aim 2: To study diffuse metabolic changes in early relapsing-remitting Multiple Sclerosis </li></ul><ul><li>Thesis Aim 3: To assess (localized) thalamic damage in mild Traumatic Brain Injury </li></ul>
    12. 12. <ul><li>For the purpose of quantification, the use of a single T 2 value per metabolite is sufficient for controls of all ages and patients with relapsing-remitting Multiple Sclerosis </li></ul>Aim 1 Hypothesis
    13. 13. Introduction <ul><li>MRS signal decays with a constant of T 2 </li></ul><ul><li>Need TE = 0 to measure true spin density (concentration, c ) </li></ul><ul><li>However: </li></ul><ul><li>The measured signal is T 2 -weighted </li></ul><ul><li>T 2 s are used to find spin density </li></ul><ul><li>T 2 s are not obtained with every scan </li></ul>
    14. 14. Introduction Differences in T 2 need to be taken into account to avoid interpreting them as concentration differences
    15. 15. Methods T 2 calculation 1 Fleysher, et al . MRM 2007 <ul><li>Two-point method: </li></ul><ul><li>Developed by Fleysher et al. 1 </li></ul><ul><li>Optimizes all acquisition </li></ul><ul><li>parameters per unit time: </li></ul><ul><ul><li>One average (N 1 =1) at the shortest TE ( TE 1 =35 ms) </li></ul></ul><ul><ul><li>Three averages (N 2 =3) at long, TE 2 = TE 1 +1.25 × T 2 o = 260 ms </li></ul></ul><ul><li>( T 2 o is a T 2 guess = 180 ms) </li></ul>
    16. 16. Methods 3D 1 H-MRS <ul><li>PRESS with water suppression: 10 AP ×8 LR × 4 IS = 320 cm 3 VOI </li></ul><ul><li>TE 1 / TR 1 =35/1260 ms, N=1 </li></ul><ul><li>TE 2 / TR 2 =260/1260 ms, N=3 </li></ul><ul><li>4 slices, FOV=16 AP ×16 LR cm 2 </li></ul><ul><li>Voxel number: 320 </li></ul><ul><li>Voxel size: 1.0 AP ×1.0 LR ×1.0 IS = 1 cm 3 </li></ul>
    17. 17. Age dependence of regional proton metabolite T 2 s in normal brain 1 1 Kirov et al . MRM 2008 Metabolite T 2 s in aging <ul><li>20 healthy individuals comprising 4 age groups: </li></ul><ul><ul><li>Adolescents, mean age 13 (n = 4) </li></ul></ul><ul><ul><li>Young adults, mean age 26 (n = 8) </li></ul></ul><ul><ul><li>Middle-aged, mean age 51 (n = 2) </li></ul></ul><ul><ul><li>Elderly, mean age 74 (n = 6) </li></ul></ul>Subjects
    18. 18. MRI/MRS – Adolescent WM ROIs: (a) Corona radiata (b) Genu (c) Splenium (d) Posterior WM Kirov et al . MRM 2008 Metabolite T 2 s in aging
    19. 19. MRI/MRS – Elderly Kirov et al . MRM 2008 Metabolite T 2 s in aging
    20. 20. Kirov et al . MRM 2008 Metabolite T 2 s in aging <ul><li>GM ROIs: </li></ul><ul><li>Putamen </li></ul><ul><li>Caudate </li></ul><ul><li>Globus pallidus </li></ul><ul><li>Thalamus </li></ul><ul><li>Cingulate gyrus </li></ul><ul><li>WM ROIs: </li></ul><ul><li>(f) Centrum </li></ul><ul><li>semiovale </li></ul>
    21. 21. Results: g lobal T 2 s Histograms of T 2 s from all 320 voxels for all adolescents and elderly subjects Kirov et al . MRM 2008 Metabolite T 2 s in aging
    22. 22. Kirov et al . MRM 2008 Metabolite T 2 s in aging Results: regional T 2 s
    23. 23. Conclusion <ul><li>Intra - and inter -subject T 2 variability is low in all age groups </li></ul><ul><li>T 2 decreases at a rate of < 1 ms/year for all metabolites </li></ul><ul><ul><ul><li>Using one T 2 value per metabolite, </li></ul></ul></ul><ul><li>(1) anywhere in the brain </li></ul><ul><li>(2) of all healthy subjects </li></ul><ul><li>(3) of any age </li></ul><ul><li>would impact quantification precision </li></ul><ul><li>by < ±10% for TE s under 100 ms </li></ul><ul><li>Quantification precision can be improved by using the reported regional T 2 times </li></ul><ul><li>and formulas for obtaining age-specific metabolite T 2 s </li></ul>Kirov et al . MRM 2008 Metabolite T 2 s in aging
    24. 24. Proton metabolite T 2 s in RR MS lesions and normal-appearing tissue 1 1 Kirov et al . Radiology 2010 Metabolite T 2 s in RR MS Subjects
    25. 25. MRI/MRS – RR MS patient 21F Disease duration: 2.6 yrs. EDSS: 1.5 VOI lesion volume: 23 cc Kirov et al . Radiology 2010 Metabolite T 2 s in RR MS
    26. 26. <ul><li>GM : </li></ul><ul><li>a caudate </li></ul><ul><li>b putamen </li></ul><ul><li>c globus pallidus </li></ul><ul><li>d thalamus </li></ul><ul><li>e cingulate gyrus </li></ul><ul><li>WM : </li></ul><ul><li>f centrum semiovale </li></ul><ul><li>g genu </li></ul><ul><li>h corona radiata </li></ul><ul><li>i splenium </li></ul><ul><li>j posterior WM </li></ul>ROIs: normal-appearing tissue Kirov et al . Radiology 2010 Metabolite T 2 s in RR MS
    27. 27. ROIs: lesions a isointense b hypointense Kirov et al . Radiology 2010 Metabolite T 2 s in RR MS
    28. 28. Results: global T 2 s Kirov et al . Radiology 2010 Metabolite T 2 s in RR MS Histograms of T 2 s from all 320 voxels for all 10 patients
    29. 29. Results : regional T 2 s Kirov et al . Radiology 2010 Metabolite T 2 s in RR MS
    30. 30. Conclusion <ul><li>The intra - and inter- patients’ T 2 variations are found to be of the same magnitude as those reported in controls (Kirov et al. , MRM 2008) </li></ul><ul><li>The differences between patients and controls are of the order of the intra -subject T 2 variations of both patients and controls </li></ul><ul><ul><ul><li>Using one T 2 value per metabolite, </li></ul></ul></ul><ul><li>(1) anywhere in the brain </li></ul><ul><li>(2) of RR MS patients and their controls </li></ul><ul><li>would impact quantification precision </li></ul><ul><li>by < ±10% for TE s under 100 ms </li></ul><ul><li>Quantification precision can be improved by using the reported regional and lesion T 2 times </li></ul>Kirov et al . Radiology 2010 Metabolite T 2 s in RR MS
    31. 31. <ul><li>For the purpose of quantification, the use of a single T 2 value per metabolite is sufficient for controls of all ages and patients with relapsing-remitting Multiple Sclerosis </li></ul>Aim 1 Hypothesis
    32. 32. Presentation outline <ul><li>Introduction to MRI and MR Spectroscopy (MRS) </li></ul><ul><li>Aim 1: To improve quantification precision by measuring the extent to which T 2 changes in controls and Multiple Sclerosis influence apparent metabolic concentrations </li></ul><ul><li>Aim 2: To study diffuse metabolic changes in early relapsing-remitting Multiple Sclerosis </li></ul><ul><li>Aim 3: To assess (localized) thalamic damage in mild Traumatic Brain Injury </li></ul>
    33. 33. <ul><li>Metabolic abnormalities (mI, Cho, Cr) suggestive of astrogliosis and de/re-myelination precede axonal damage (NAA) and atrophy in normal-appearing tissue in Multiple Sclerosis </li></ul>Aim 2 Hypothesis
    34. 34. <ul><li>Leading cause of non-traumatic neurological disability in the young and middle aged </li></ul><ul><li>Cause is unknown, but is both genetic and environmental </li></ul><ul><li>More common in women and away from equator </li></ul><ul><li>Onset at ~30 years of age </li></ul><ul><li>2.5 million cases worldwide; 350,000 in the US </li></ul>Multiple Sclerosis
    35. 35. Pathogenesis in the MS plaque Adapted from Frohman et al ., NEJM 2006 <ul><li>T-cell activation towards self antigen </li></ul><ul><li>Infiltration of the blood-brain-barrier </li></ul><ul><li>Pro-inflammatory cascade in the CNS </li></ul><ul><li>Degradation of myelin </li></ul><ul><li>……………………………… . </li></ul><ul><li>Axonal transection </li></ul><ul><li>Astrogliosis </li></ul>
    36. 36. Histological findings <ul><li>Pathology </li></ul><ul><li>blood-brain barrier breakdown </li></ul><ul><li>inflammatory infiltrates </li></ul><ul><li>demyelination </li></ul><ul><li>astrogliosis </li></ul><ul><li>axonal transection </li></ul><ul><li>reduced axonal density </li></ul><ul><li>astrogliosis </li></ul><ul><li>(inflammation) </li></ul><ul><li>(demyelination) </li></ul>Type Focal (plaques) Diffuse Autopsy and biopsy
    37. 37. <ul><li>T 1 and T 2 -weighted MRI </li></ul>Radiological findings <ul><li>Diffuse abnormalities are not </li></ul><ul><li>detected </li></ul><ul><li>tissue is ‘normal-appearing’ </li></ul><ul><li>Global – atrophy </li></ul><ul><li>end-stage manifestation of various pathologies </li></ul><ul><li>Focal – lesions </li></ul><ul><li>non-specific for pathology </li></ul><ul><li>weak correlation with clinical disability </li></ul>
    38. 38. <ul><li>Can detect diffuse pathology in ‘normal-appearing’ tissue </li></ul>Quantitative MRI Magnetization transfer imaging Diffusion tensor imaging T 1 and T 2 relaxation time mapping MR Spectroscopy Radiological findings
    39. 39. MR Spectroscopy indicates diffuse Multiple Sclerosis activity during remission 1 1 Kirov et al . JNNP 2009 Diffuse disease activity in MS Subjects <ul><li>21 relapsing-remitting MS patients </li></ul><ul><ul><li>Recently diagnosed (~2 years) </li></ul></ul><ul><ul><li>Mildly disabled (mean EDSS = 1.4) </li></ul></ul><ul><ul><li>All on medication </li></ul></ul><ul><ul><li>All in remission </li></ul></ul><ul><li>15 age- and gender-matched controls </li></ul>
    40. 40. Methods: 3D 1 H-MRS PRESS with water suppression: VOI = 8 LR ×10 AP ×4.5 IS = 360 cm 3 TE/TR = 35/1800 ms, N=2 6 slices, FOV=16 AP ×16 LR cm 2 Voxel number: 480 Voxel size 1.0 AP ×1.0 LR ×0.75 IS = 0.75 cm 3 Kirov et al . JNNP 2009 Diffuse disease activity in MS
    41. 41. <ul><li>The sum of all 480 spectra results in a single spectrum indicative of WM status </li></ul><ul><li>1 H-MRS VOI: </li></ul><ul><li>360 cm 3 </li></ul><ul><li>480 voxels </li></ul><ul><li>~80% WM </li></ul>Methods: post-processing approach Kirov et al . JNNP 2009 Diffuse disease activity in MS
    42. 42. Single voxel vs. summation Kirov et al . JNNP 2009 Diffuse disease activity in MS
    43. 43. Methods : post-processing (contd.) <ul><li>Frequency-align in reference to the NAA peak </li></ul><ul><li>Zero-out all voxels outside of the VOI </li></ul><ul><li>Sum remaining voxels </li></ul><ul><li>Calculate peak areas using the FITT software </li></ul><ul><li>Phantom replacement </li></ul><ul><li>Correct for T 1 and T 2 relaxation time differences between in- vivo (Traber, 2004; Kirov, 2008) and in-vitro </li></ul>Kirov et al . JNNP 2009 Diffuse disease activity in MS
    44. 44. <ul><li>To obtain metabolic concentrations </li></ul><ul><li>To measure atrophy </li></ul>Tissue fraction (T f ) = tissue volume/VOI volume Low T f = High atrophy MIDAS segmentation package: De Santi, 2001 Methods : segmentation Kirov et al . JNNP 2009 Diffuse disease activity in MS
    45. 45. MRI/MRS – RR MS patient 42F Disease duration: 2.1 yrs. EDSS: 0 VOI lesion volume: 4.2 cc Kirov et al . JNNP 2009 Diffuse disease activity in MS
    46. 46. Summed spectra Each spectrum: – is the sum of all voxels in the VOI – represents one subject – gray line represents the FITT function Results Kirov et al . JNNP 2009 Diffuse disease activity in MS
    47. 47. Results a Years, b inside the VOI, in cm 3 , c tissue fraction, d p ‹0.01. *EDSS dominated by permanent visual impairment in one eye. Kirov et al . JNNP 2009 Diffuse disease activity in MS
    48. 48. metabolic concentrations tissue fraction Results Kirov et al . JNNP 2009 Diffuse disease activity in MS
    49. 49. Metabolites as disease markers Localization Suspected pathology Finding mI ↑ Cr ↑ Cho ↑ – astrogliosis de/re-myelination Results Kirov et al . JNNP 2009 Diffuse disease activity in MS
    50. 50. Since NAA loss and atrophy are observed in advanced disease these processes may precede (cause?) axonal damage <ul><li>Global WM status in early RR MS: </li></ul><ul><li>No axonal dysfunction </li></ul><ul><li>But </li></ul><ul><li>Astrogliosis, de -/ re -myelination </li></ul><ul><li>possibly in response to diffuse inflammation </li></ul>Conclusion Kirov et al . JNNP 2009 Diffuse disease activity in MS
    51. 51. Presentation outline <ul><li>Introduction to MRI and MR Spectroscopy (MRS) </li></ul><ul><li>Thesis Aim 1: To improve quantification precision by measuring the extent to which T 2 changes in controls and Multiple Sclerosis influence apparent metabolic concentrations </li></ul><ul><li>Thesis Aim 2: To study diffuse metabolic changes in early relapsing-remitting Multiple Sclerosis </li></ul><ul><li>Thesis Aim 3: To assess (localized) thalamic damage in mild Traumatic Brain Injury </li></ul>
    52. 52. <ul><li>Defining the limits of metabolic change in the thalamus in mild traumatic brain injury (TBI) may help yield an objective criterion for differentiating ‘mild’ from more severe TBI. </li></ul>Aim 3 Hypothesis
    53. 53. Traumatic Brain Injury (TBI) <ul><li>5.3 million people live with a TBI-related disability </li></ul><ul><li>1.5 million new cases/year </li></ul><ul><li>$56 billion in medical expenses and lost productivity cost </li></ul><ul><li>10% - 20% prevalence among soldiers in Iraq and Afghanistan </li></ul><ul><li>85% of TBI cases are “mild” </li></ul>
    54. 54. <ul><li>Unlike “moderate” and “severe” TBI, mTBI patients usually have no MR findings -> diagnosis may be subjective </li></ul><ul><li>No biological tools to assess prognosis, progression or even the presence of disease </li></ul><ul><li>7% - 33% of people with mTBI never recover </li></ul><ul><li>(the “Miserable Minority”) </li></ul><ul><li>Symptoms: memory deficits, dizziness, headache, fatigue, sleep disorders, attention deficits, speech problems, depression, difficulties in decision making, etc . </li></ul>Mild Traumatic Brain Injury (mTBI)
    55. 55. <ul><li>Nature of mTBI injury: Diffuse, heterogeneous, microscopic damage to individual neurons, which does not progress to structural (MRI-visible) abnormalities </li></ul><ul><li>The Challenge: How and where to detect such changes </li></ul>Need a reliable biomarker for mTBI Motivation for study
    56. 56. <ul><li>Thalamus </li></ul><ul><li>reciprocal connections </li></ul><ul><li>with entire cortex </li></ul><ul><li>‘ mini-cortex’ </li></ul><ul><li>controls cortical integrity </li></ul>1 H-MRS – can detect occult damage by measuring [NAA], [Cr], [Cho] Neuronal injury Wallerian/retrograde degeneration 1 Behrens, Nature Neuroscience 2003 1 <ul><li>Related to patients symptoms: </li></ul><ul><li>Neurobehavioral </li></ul><ul><li>(cognition, irritability, </li></ul><ul><li>insomnia, fatigue, etc .) </li></ul><ul><li>Sensory </li></ul><ul><li>(headaches, blurred vision, </li></ul><ul><li>diminished smell/taste, etc .) </li></ul>Motivation for study
    57. 57. Characterizing mild TBI with proton MR spectroscopy in the thalamus 1 Subjects Controls: 17 age- and gender-matched Patients: 1 Kirov et al . Brain Injury 2007 1 H-MRS of the thalamus in mTBI
    58. 58. Methods: 3D 1 H-MRS PRESS with water suppression: VOI = 8 LR ×10 AP ×6 IS = 480 cm 3 TE/TR = 135/1600 ms, N=2 8 slices, FOV=16 AP ×16 LR cm 2 Voxel number: 640 Voxel size: 1.0 AP ×1.0 LR ×0.75 IS = 0.75 cm 3 Kirov et al . Brain Injury 2007 1 H-MRS of the thalamus in mTBI
    59. 59. Kirov et al . Brain Injury 2007 1 H-MRS of the thalamus in mTBI MRI/MRS – mTBI patient
    60. 60. Methods: post-processing Kirov et al . Brain Injury 2007 1 H-MRS of the thalamus in mTBI
    61. 61. Methods: post-processing Kirov et al . Brain Injury 2007 1 H-MRS of the thalamus in mTBI
    62. 62. Thalamic metabolic concentrations Kirov et al . Brain Injury 2007 1 H-MRS of the thalamus in mTBI Results
    63. 63. Minimal detectable differences: the minimal differences between patients and controls that would produce significance ( p < 0.05) Kirov et al . Brain Injury 2007 1 H-MRS of the thalamus in mTBI Results
    64. 64. <ul><li>Detectable differences may be used to: </li></ul><ul><li>define the range of thalamic metabolic changes in mTBI </li></ul><ul><li>distinguish ‘mild’ from more severe TBI </li></ul><ul><li>improve animal model design </li></ul>Kirov et al . Brain Injury 2007 1 H-MRS of the thalamus in mTBI Conclusion Metabolite Controls Patients Detectable Difference [NAA] 10.08 ± 0.3 9.60 ± 0.34 +/- 13.0% [Cr] 5.62 ± 0.18 5.44 ± 0.19 +/- 13.5% [Cho] 2.08 ± 0.09 2.03 ± 0.10 +/- 18.8%
    65. 65. List of publications <ul><li>Kirov II , Liu S, Fleysher R, Fleysher L, Babb J, Herbert J, Gonen O. </li></ul><ul><li>Brain metabolites proton T 2 mapping at 3 Tesla in relapsing-remitting Multiple Sclerosis </li></ul><ul><li>Radiology (2010) In Press </li></ul><ul><li>2. Kirov II , Patil V, Babb J, Rusinek H, Herbert J, Gonen O. </li></ul><ul><li>MR Spectroscopy indicates diffuse Multiple Sclerosis activity during remission </li></ul><ul><li>Journal of Neurology, Neurosurgery and Psychiatry 80, 1330-1336 (2009) </li></ul><ul><li>3. Fleysher R, Fleysher L, Kirov I , Hess D, Liu S, Gonen O </li></ul><ul><li>Retrospective correction for T 1 -weighting bias in T 2 values obtained with various spectroscopic </li></ul><ul><li>spin-echo acquisition schemes </li></ul><ul><li>Magnetic Resonance Imaging 27, 1410-1419 (2009) </li></ul><ul><li>4. Kirov II , Fleysher L, Fleysher R, Patil V, Liu S, Gonen O. </li></ul><ul><li>Age Dependence of Regional Proton Metabolites T 2 Relaxation Times in the Human Brain at 3 T </li></ul><ul><li>Magnetic Resonance in Medicine 60, 790-795 (2008) </li></ul><ul><li>5. Kirov I , Fleysher L, Babb J, Silver J, Grossman R, Gonen O. </li></ul><ul><li>Characterizing ‘mild’ in Traumatic Brain Injury with proton MR Spectroscopy in the thalamus: </li></ul><ul><li>initial findings </li></ul><ul><li>Brain Injury 21, 1147-1154 (2007) </li></ul>
    66. 66. <ul><li>Thesis Committee: </li></ul><ul><li>Qun Chen, Achim Gass, Oded Gonen, Matilde Inglese, </li></ul><ul><li>Glyn Johnson </li></ul><ul><li>Center for Biomedical Imaging </li></ul><ul><li>Oded Gonen (Advisor) </li></ul><ul><li>James Babb </li></ul><ul><li>Lazar Fleysher </li></ul><ul><li>Roman Fleysher </li></ul><ul><li>Robert Grossman </li></ul><ul><li>David Hess </li></ul><ul><li>Joseph Herbert </li></ul><ul><li>Songtao Liu </li></ul><ul><li>Vishal Patil </li></ul>Acknowledgements Nissa Perry Joseph Reaume Daniel Rigotti Henry Rusinek Jonathan Silver William Wu Wafaa Zaaraoui Ke Zhang
    67. 67. Supplemental slides - MS
    68. 68. Adapted from Hauser et al ., Neuron 2006 Natural history
    69. 69. Possible causes of astrogliosis <ul><li>‘ Microplaques’ – diffuse blood-brain barrier (BBB) breakdown </li></ul><ul><li>BBB – astrocytes differentiate from BBB signals and up-regulate BBB impermeability </li></ul><ul><li>Global hypoperfusion – ischemia may result in astroglial proliferation </li></ul>
    70. 70. Implications Model of temporal evolution of pathogenesis in NAWM Diffuse disease activity in MS
    71. 71. Supplemental slides – acute mTBI
    72. 72. Subjects <ul><li>14 patients (3 women) with mild TBI (mean GCS = 14.5) </li></ul><ul><li>Recent injury </li></ul><ul><li>(mean time from TBI = 19 days, range 1-54) </li></ul><ul><li>10 reported symptoms </li></ul><ul><li>3 were on medication </li></ul><ul><li>9 controls </li></ul>
    73. 73. Methods
    74. 74. Results <ul><li>VOI </li></ul><ul><li>Patients: </li></ul><ul><li>average tissue volume fraction (0.94) </li></ul><ul><li>NAA (7.3 mM), Cr (5.9 mM), Cho (1.5 mM), mI (3.7 mM) </li></ul><ul><li>Controls: </li></ul><ul><li>average tissue volume fraction (0.94) </li></ul><ul><li>NAA (7.5 mM), Cr (5.5 mM), Cho (1.4 mM), mI (3.3 mM) (p = 0.047, 0.031) </li></ul><ul><li>Whole brain </li></ul><ul><li>Patients: </li></ul><ul><li>average brain volume (1162 cc) </li></ul><ul><li>WBNAA (11.1 mM) </li></ul><ul><li>Controls: </li></ul><ul><li>average brain volume (1207 cc) </li></ul><ul><li>WBNAA (12.2 mM) </li></ul>
    75. 75. Supplemental slides - miscellaneous
    76. 76. T 1 -weighting bias in T 2 measurement with DSE
    77. 77. Detectable Differences ? = 8.8 mM or a 13% difference from the healthy controls’ mean
    78. 78. MRS Methodology <ul><li>3D hybrid of 1D Hadamard with 2D chemical shift imaging 1 </li></ul>1 Gonen, MRM 1997, MRM 1998
    79. 79. <ul><li>Adolescents (n=4) </li></ul><ul><li>Young adults (n=8) </li></ul><ul><li>Middle age (n=2) </li></ul><ul><li>Elderly (n=6) </li></ul>Aging and the brain Subjects Age Brain changes with age 28 51 74 13 <ul><ul><li>Parenchyma loss (atrophy) </li></ul></ul><ul><ul><li>Increase in the fraction of small neurons </li></ul></ul><ul><ul><li>Reduction of water content </li></ul></ul><ul><ul><li>Axonal and myelin degeneration </li></ul></ul><ul><ul><li>Progressive accumulation of </li></ul></ul><ul><ul><li>paramagnetic ions ( +3 Fe) </li></ul></ul>1 Jara, Top Magn Reson Imaging , 2006 Brain iron and aging 1

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