John Kornak

Longitudinal stability of MRI for mapping brain change using tensor-based morphometry

Measures of brain change can be computed from sequential MRI scans, providing valuable information on disease progression, e.g., for patient monitoring and drug trials. Tensor-based morphometry (TBM) creates maps of these brain changes, visualizing the 3D profile and rates of tissue growth or atrophy, but its sensitivity depends on the contrast and geometric stability of the images. As part of the Alzheimers Disease Neuroimaging Initiative (ADNI), 17 normal elderly subjects were scanned twice (at a 2-week interval) with several 3D 1.5 T MRI pulse sequences: high and low flip angle SPGR/FLASH (from which Synthetic T1 images were generated), MP-RAGE, IR-SPGR (N = 10) and MEDIC (N = 7) scans. For each subject and scan type, a 3D deformation map aligned baseline and follow-up scans, computed with a nonlinear, inverse-consistent elastic registration algorithm. Voxelwise statistics, in ICBM stereotaxic space, visualized the profile of mean absolute change and its cross-subject variance; these maps were then compared using permutation testing. Image stability depended on: (1) the pulse sequence; (2) the transmit/receive coil type (birdcage versus phased array); (3) spatial distortion corrections (using MEDIC sequence information); (4) B1-field intensity inhomogeneity correction (using N3). SPGR/FLASH images acquired using a birdcage coil had least overall deviation. N3 correction reduced coil type and pulse sequence differences and improved scan reproducibility, except for Synthetic T1 images (which were intrinsically corrected for B1-inhomogeneity). No strong evidence favored B0 correction. Although SPGR/FLASH images showed least deviation here, pulse sequence selection for the ADNI project was based on multiple additional image analyses, to be reported elsewhere.

Amyloid vs FDG-PET in the differential diagnosis of AD and FTLD.

Objective: To compare the diagnostic performance of PET with the amyloid ligand Pittsburgh compound B (PiB-PET) to fluorodeoxyglucose (FDG-PET) in discriminating between Alzheimer disease (AD) and frontotemporal lobar degeneration (FTLD). Methods: Patients meeting clinical criteria for AD (n = 62) and FTLD (n = 45) underwent PiB and FDG-PET. PiB scans were classified as positive or negative by 2 visual raters blinded to clinical diagnosis, and using a quantitative threshold derived from controls (n = 25). FDG scans were visually rated as consistent with AD or FTLD, and quantitatively classified based on the region of lowest metabolism relative to controls. Results: PiB visual reads had a higher sensitivity for AD (89.5% average between raters) than FDG visual reads (77.5%) with similar specificity (PiB 83%, FDG 84%). When scans were classified quantitatively, PiB had higher sensitivity (89% vs 73%) while FDG had higher specificity (83% vs 98%). On receiver operating characteristic analysis, areas under the curve for PiB (0.888) and FDG (0.910) were similar. Interrater agreement was higher for PiB (κ = 0.96) than FDG (κ = 0.72), as was agreement between visual and quantitative classification (PiB κ = 0.88–0.92; FDG κ = 0.64–0.68). In patients with known histopathology, overall classification accuracy (2 visual and 1 quantitative classification per patient) was 97% for PiB (n = 12 patients) and 87% for FDG (n = 10). Conclusions: PiB and FDG showed similar accuracy in discriminating AD and FTLD. PiB was more sensitive when interpreted qualitatively or quantitatively. FDG was more specific, but only when scans were classified quantitatively. PiB slightly outperformed FDG in patients with known histopathology.

Age effects on atrophy rates of entorhinal cortex and hippocampus

The effects of age, subcortical vascular disease, apolipoprotein E (APOE) epsilon4 allele and hypertension on entorhinal cortex (ERC) and hippocampal atrophy rates were explored in a longitudinal MRI study with 42 cognitively normal (CN) elderly subjects from 58 to 87 years old. The volumes of the ERC, hippocampus, and white matter hyperintensities (WMH) and the presence of lacunes were assessed on MR images. Age was significantly associated with increased atrophy rates of 0.04+/-0.02% per year for ERC and 0.05+/-0.02% per year for hippocampus. Atrophy rates of hippocampus, but not that of ERC increased with presence of lacunes, in addition to age. WMH, APOE epsilon4 and hypertension had no significant effect on atrophy rates. In conclusion, age and presence of lacunes should be taken into consideration in imaging studies of CN subjects and AD patients to predict AD progression and assess the response to treatment trials.

3D characterization of brain atrophy in Alzheimer's disease and mild cognitive impairment using tensor-based morphometry

Tensor-based morphometry (TBM) creates three-dimensional maps of disease-related differences in brain structure, based on nonlinearly registering brain MRI scans to a common image template. Using two different TBM designs (averaging individual differences versus aligning group average templates), we compared the anatomical distribution of brain atrophy in 40 patients with Alzheimers disease (AD), 40 healthy elderly controls, and 40 individuals with amnestic mild cognitive impairment (aMCI), a condition conferring increased risk for AD. We created an unbiased geometrical average image template for each of the three groups, which were matched for sex and age (mean age: 76.1 years+/-7.7 SD). We warped each individual brain image (N=120) to the control group average template to create Jacobian maps, which show the local expansion or compression factor at each point in the image, reflecting individual volumetric differences. Statistical maps of group differences revealed widespread medial temporal and limbic atrophy in AD, with a lesser, more restricted distribution in MCI. Atrophy and CSF space expansion both correlated strongly with Mini-Mental State Exam (MMSE) scores and Clinical Dementia Rating (CDR). Using cumulative p-value plots, we investigated how detection sensitivity was influenced by the sample size, the choice of search region (whole brain, temporal lobe, hippocampus), the initial linear registration method (9- versus 12-parameter), and the type of TBM design. In the future, TBM may help to (1) identify factors that resist or accelerate the disease process, and (2) measure disease burden in treatment trials.

Alzheimer’s Disease Neuroimaging Initiative: A one-year follow up study using tensor-based morphometry correlating degenerative rates, biomarkers and cognition

Tensor-based morphometry can recover three-dimensional longitudinal brain changes over time by nonlinearly registering baseline to follow-up MRI scans of the same subject. Here, we compared the anatomical distribution of longitudinal brain structural changes, over 12 months, using a subset of the ADNI dataset consisting of 20 patients with Alzheimers disease (AD), 40 healthy elderly controls, and 40 individuals with mild cognitive impairment (MCI). Each individual longitudinal change map (Jacobian map) was created using an unbiased registration technique, and spatially normalized to a geometrically-centered average image based on healthy controls. Voxelwise statistical analyses revealed regional differences in atrophy rates, and these differences were correlated with clinical measures and biomarkers. Consistent with prior studies, we detected widespread cerebral atrophy in AD, and a more restricted atrophic pattern in MCI. In MCI, temporal lobe atrophy rates were correlated with changes in mini-mental state exam (MMSE) scores, clinical dementia rating (CDR), and logical/verbal learning memory scores. In AD, temporal atrophy rates were correlated with several biomarker indices, including a higher CSF level of p-tau protein, and a greater CSF tau/beta amyloid 1-42 (ABeta42) ratio. Temporal lobe atrophy was significantly faster in MCI subjects who converted to AD than in non-converters. Serial MRI scans can therefore be analyzed with nonlinear image registration to relate ongoing neurodegeneration to a variety of pathological biomarkers, cognitive changes, and conversion from MCI to AD, tracking disease progression in 3-dimensional detail.

Body mass index and magnetic resonance markers of brain integrity in adults.

Obesity and being overweight during adulthood have been consistently linked to increased risk for development of dementia later in life, especially Alzheimers disease. They have also been associated with cognitive dysfunction and brain structural alterations in otherwise healthy adults. Although proton magnetic resonance spectroscopy may distinguish between neuronal and glial components of the brain and may point to neurobiological mechanisms underlying brain atrophy and cognitive changes, no spectroscopic studies have yet assessed the relationships between adiposity and brain metabolites.

Optimizing power to track brain degeneration in Alzheimer’s disease and mild cognitive impairment with tensor-based morphometry: An ADNI study of 515 subjects

Tensor-based morphometry (TBM) is a powerful method to map the 3D profile of brain degeneration in Alzheimers disease (AD) and mild cognitive impairment (MCI). We optimized a TBM-based image analysis method to determine what methodological factors, and which image-derived measures, maximize statistical power to track brain change. 3D maps, tracking rates of structural atrophy over time, were created from 1030 longitudinal brain MRI scans (1-year follow-up) of 104 AD patients (age: 75.7+/-7.2 years; MMSE: 23.3+/-1.8, at baseline), 254 amnestic MCI subjects (75.0+/-7.2 years; 27.0+/-1.8), and 157 healthy elderly subjects (75.9+/-5.1 years; 29.1+/-1.0), as part of the Alzheimers Disease Neuroimaging Initiative (ADNI). To determine which TBM designs gave greatest statistical power, we compared different linear and nonlinear registration parameters (including different regularization functions), and different numerical summary measures derived from the maps. Detection power was greatly enhanced by summarizing changes in a statistically-defined region-of-interest (ROI) derived from an independent training sample of 22 AD patients. Effect sizes were compared using cumulative distribution function (CDF) plots and false discovery rate methods. In power analyses, the best method required only 48 AD and 88 MCI subjects to give 80% power to detect a 25% reduction in the mean annual change using a two-sided test (at alpha=0.05). This is a drastic sample size reduction relative to using clinical scores as outcome measures (619 AD/6797 MCI for the ADAS-Cog, and 408 AD/796 MCI for the Clinical Dementia Rating sum-of-boxes scores). TBM offers high statistical power to track brain changes in large, multi-site neuroimaging studies and clinical trials of AD.

ASL Perfusion MRI Predicts Cognitive Decline and Conversion From MCI to Dementia

We compared the predictive value of cerebral perfusion as measured by arterial-spin labeling magnetic resonance imaging (ASL-MRI) with MRI-derived hippocampal volume for determining future cognitive and functional decline and subsequent conversion from mild cognitive impairment to dementia. Forty-eight mild cognitive impairment subjects received structural and ASL-MRI scans at baseline and clinical and neuropsychologic assessments annually. Thirteen subjects became demented during the period of longitudinal observation (2.7±1.0 y). Cox regression analyses suggest that baseline hippocampal volume [relative risk (RR)=0.99, P=0.004], baseline right inferior parietal (RR=0.64, P=0.01) and right middle frontal (RR=0.73, P=0.01) perfusion were associated with conversion to dementia. Results from linear mixed effects modeling suggest that baseline perfusion from the right precuneus predicted subsequent declines in Clinical Dementia Rating Sum of Boxes (P=0.002), Functional Activates Questionnaire (P=0.01), and selective attention (ie, Stroop switching, P=0.009) whereas baseline perfusion from the right middle frontal cortex predicted subsequent episodic memory decline (ie, total recognition discriminability score from the California Verbal Learning Test, P=0.03). These results suggest that hypoperfusion as detected by ASL-MRI can predict subsequent clinical, functional, and cognitive decline and may be useful for identifying candidates for future Alzheimer disease treatment trials.

Male–female differences in the association between incident hip fracture and proximal femoral strength: A finite element analysis study

Hip fracture risk is usually evaluated using dual energy X-ray absorptiometry (DXA) or quantitative computed tomography (QCT) which provide surrogate measures for proximal femoral strength. However, proximal femoral strength can best be estimated explicitly by combining QCT with finite element (FE) analysis. To evaluate this technique for predicting hip fracture in older men and women, we performed a nested age- and sex-matched case-control study in the Age Gene/Environment Susceptibility (AGES) Reykjavik cohort. Baseline (pre-fracture) QCT scans of 5500 subjects were obtained. During 4-7 years follow-up, 51 men and 77 women sustained hip fractures. Ninety-seven men and 152 women were randomly selected as age- and sex-matched controls. FE-strength of the left hip of each subject for stance (F(Stance)) and posterolateral fall (F(Fall)) loading, and total femur areal bone mineral density (aBMD) were computed from the QCT data. F(Stance) and F(Fall) in incident hip fracture subjects were 13%-25% less than in control subjects (p ≤ 0.006) after controlling for demographic parameters. The difference between FE strengths of fracture and control subjects was disproportionately greater in men (stance, 22%; fall, 25%) than in women (stance, 13%; fall, 18%) (p ≤ 0.033), considering that F(Stance) and F(Fall) in fracture subjects were greater in men than in women (p < 0.001). For men, F(Stance) was associated with hip fracture after accounting for aBMD (p = 0.013). These data indicate that F(Stance) provides information about fracture risk that is beyond that provided by aBMD (p = 0.013). These findings support further exploration of possible sex differences in the predictors of hip fracture and of sex-specific strategies for using FE analysis to manage osteoporosis.

Risk of Thyroid Cancer Based on Thyroid Ultrasound Imaging Characteristics: Results of a Population-Based Study

IMPORTANCE There is wide variation in the management of thyroid nodules identified on ultrasound imaging. OBJECTIVE To quantify the risk of thyroid cancer associated with thyroid nodules based on ultrasound imaging characteristics. METHODS Retrospective case-control study of patients who underwent thyroid ultrasound imaging from January 1, 2000, through March 30, 2005. Thyroid cancers were identified through linkage with the California Cancer Registry. RESULTS A total of 8806 patients underwent 11,618 thyroid ultrasound examinations during the study period, including 105 subsequently diagnosed as having thyroid cancer. Thyroid nodules were common in patients diagnosed as having cancer (96.9%) and patients not diagnosed as having thyroid cancer (56.4%). Three ultrasound nodule characteristics--microcalcifications (odds ratio [OR], 8.1; 95% CI, 3.8-17.3), size greater than 2 cm (OR, 3.6; 95% CI, 1.7-7.6), and an entirely solid composition (OR, 4.0; 95% CI, 1.7-9.2)--were the only findings associated with the risk of thyroid cancer. If 1 characteristic is used as an indication for biopsy, most cases of thyroid cancer would be detected (sensitivity, 0.88; 95% CI, 0.80-0.94), with a high false-positive rate (0.44; 95% CI, 0.43-0.45) and a low positive likelihood ratio (2.0; 95% CI, 1.8-2.2), and 56 biopsies will be performed per cancer diagnosed. If 2 characteristics were required for biopsy, the sensitivity and false-positive rates would be lower (sensitivity, 0.52; 95% CI, 0.42-0.62; false-positive rate, 0.07; 95% CI, 0.07-0.08), the positive likelihood ratio would be higher (7.1; 95% CI, 6.2-8.2), and only 16 biopsies will be performed per cancer diagnosed. Compared with performing biopsy of all thyroid nodules larger than 5 mm, adoption of this more stringent rule requiring 2 abnormal nodule characteristics to prompt biopsy would reduce unnecessary biopsies by 90% while maintaining a low risk of cancer (5 per 1000 patients for whom biopsy is deferred). CONCLUSIONS AND RELEVANCE Thyroid ultrasound imaging could be used to identify patients who have a low risk of cancer for whom biopsy could be deferred. On the basis of these results, these findings should be validated in a large prospective cohort.