Citations

AbstractMotivationDiseases that progress slowly are often studied by observing cohorts at different stages of disease for short periods of time. The Alzheimer's Disease Neuroimaging Initiative (ADNI) follows elders with various degrees of cognitive impairment, from normal to impaired. The study includes a rich panel of novel cognitive tests, biomarkers, and brain images collected every 6 months for as long as 6 years. The relative timing of the observations with respect to disease pathology is unknown. We propose a general semiparametric model and iterative estimation procedure to estimate simultaneously the pathological timing and long‐term growth curves. The resulting estimates of long‐term progression are fine‐tuned using cognitive trajectories derived from the long‐term “Personnes Agées Quid” study.ResultsWe demonstrate with simulations that the method can recover long‐term disease trends from short‐term observations. The method also estimates temporal ordering of individuals with respect to disease pathology, providing subject‐specific prognostic estimates of the time until onset of symptoms. When the method is applied to ADNI data, the estimated growth curves are in general agreement with prevailing theories of the Alzheimer's disease cascade. Other data sets with common outcome measures can be combined using the proposed algorithm.AvailabilitySoftware to fit the model and reproduce results with the statistical software R is available as the grace package. ADNI data can be downloaded from the Laboratory of NeuroImaging.

Aberrant connectivity is implicated in many neurological and psychiatric disorders, including Alzheimer’s disease and schizophrenia. However, other than a few disease-associated candidate genes, we know little about the degree to which genetics play a role in the brain networks; we know even less about specific genes that influence brain connections. Twin and family-based studies can generate estimates of overall genetic influences on a trait, but genome-wide association scans (GWASs) can screen the genome for specific variants influencing the brain or risk for disease. To identify the heritability of various brain connections, we scanned healthy young adult twins with high-field, high-angular resolution diffusion MRI. We adapted GWASs to screen the brain’s connectivity pattern, allowing us to discover genetic variants that affect the human brain’s wiring. The association of connectivity with the SPON1 variant at rs2618516 on chromosome 11 (11p15.2) reached connectome-wide, genome-wide significance after stringent statistical corrections were enforced, and it was replicated in an independent subsample. rs2618516 was shown to affect brain structure in an elderly population with varying degrees of dementia. Older people who carried the connectivity variant had significantly milder clinical dementia scores and lower risk of Alzheimer’s disease. As a posthoc analysis, we conducted GWASs on several organizational and topological network measures derived from the matrices to discover variants in and around genes associated with autism ( MACROD2 ), development ( NEDD4 ), and mental retardation ( UBE2A ) significantly associated with connectivity. Connectome-wide, genome-wide screening offers substantial promise to discover genes affecting brain connectivity and risk for brain diseases.

Abstract Abstract #3103 Introduction: Lymphedema is well-known in the arm but can also occur in the breast. Diagnosis is currently non-standardized and based on clinical impression. We sought to determine if skin thickness could serve as a useful measure in the diagnosis of breast lymphedema (BLE).
 Methods: Patients undergoing unilateral non-mastectomy procedures were enrolled on this prospective clinical study preoperatively and evaluated for signs and symptoms of lymphedema in the operated breast in the postoperative period. Patients with established BLE were also studied. BLE diagnosis was independent of ultrasound findings and was based on a graded physical exam targeting clinical signs of edema and erythema. Skin thickness was measured with ultrasound at the 6 o'clock position of both breasts. Differences in skin thickness were assessed by the ratio of skin thickness in the operated and contralateral breasts, with comparison of means by two-sample t-test. Receiver operating characteristic curves were constructed to estimate the area under the curve (AUC) and to determine optimal cutpoints.
 Results: Ninety-seven women were studied; 85 were enrolled preoperatively and 12 postoperatively after a diagnosis of BLE. Median length of follow-up overall was 8 months. Of the 97 women, 46 had BLE at one or more follow-up visit with median time to first diagnosis of 3 months. Mean measured skin thickness was 2.3 ± 0.5mm in unaffected breasts and 3.2 ± 1.0 mm in operated breasts. Women with BLE had significantly greater skin thickness in the operated breast compared to the contralateral breast, with a mean skin thickness ratio of 1.83 ± 0.57 in patients with BLE compared to 1.18 ± 0.40 among those without BLE (p<0.0001). Skin thickness ratio provided excellent discrimination for BLE, with an area under the curve (AUC) of 0.86. At a ratio of 1.32 or greater (32% greater skin thickness in operated versus contralateral breast), sensitivity and specificity for diagnosing BLE were 81% and 82% respectively. A single measure of skin thickness in the operated breast also correlated well with diagnosis of BLE, with skin thickness of 3.2 mm or greater demonstrating sensitivity of 78% and specificity of 82%, AUC 0.82.
 Conclusions: Breast lymphedema results in skin edema and increased skin thickness that can be quantified with ultrasound. Measuring breast skin thickness- either as a single reading or as a ratio to the unaffected breast- may be useful in the diagnosis of breast lymphedema. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 3103.

The Clinical Core of the Alzheimer's Disease Neuroimaging Initiative (ADNI) has provided clinical, operational, and data management support to ADNI since its inception. This article reviews the activities and accomplishments of the core in support of ADNI aims. These include the enrollment and follow‐up of more than 800 subjects in the three original cohorts: healthy controls, amnestic mild cognitive impairment (now referred to as late MCI, or LMCI), and mild Alzheimer's disease (AD) in the first phase of ADNI (ADNI 1), with baseline longitudinal, clinical, and cognitive assessments. These data, when combined with genetic, neuroimaging, and cerebrospinal fluid measures, have provided important insights into the neurobiology of the AD spectrum. Furthermore, these data have facilitated the development of novel clinical trial designs. ADNI has recently been extended with funding from an NIH Grand Opportunities (GO) award, and the new ADNI GO phase has been launched; this includes the enrollment of a new cohort, called early MCI, with milder episodic memory impairment than the LMCI group. An application for a further 5 years of ADNI funding (ADNI 2) was recently submitted. This funding would support ongoing follow‐up of the original ADNI 1 and ADNI GO cohorts, as well as additional recruitment into all categories. The resulting data would provide valuable data on the earliest stages of AD, and support the development of interventions in these critically important populations.