Summary: Accuracy in in vivo quantitation of brain func tion with positron emission tomography (PET) has often been limited by partial volume effects. This limitation be comes prominent in studies of aging and degenerative brain diseases where partial volume effects vary with dif ferent degrees of atrophy. The present study describes how the actual gray matter (GM) tracer concentration can be estimated using an algorithm that relates the regional fraction of GM to partial volume effects. The regional fraction of GM was determined by magnetic resonance imaging (MRI). The procedure is designated as GM PET. In computer simulations and phantom studies, the GM PET algorithm permitted a 100% recovery of the actual tracer concentration in neocortical GM and hippocam pus, irrespective of the GM volume. GM PET was apPositron emission tomography (PET) permits in vestigation of physiological and biochemical pro cesses in human brain in vivo, and has yielded new insights into both normal physiology and diseases (Kuhl et aI. , 1982;Foster, 1983; Wagner et aI. , 1983; Frost et aI., 1985;Phelps and Mazziotta, 1985;Frost, 1986; Yamaguchi et aI. , 1986; Yoshii et aI., Abbreviations used: AU, arbitrary units; FWHM, full width at half-maximum; OM, gray matter; MRI, magnetic resonance im aging; PET, positron emission tomography; RMSE, relative mean-squared error; ROI, region of interest; SPOR, spoiled grass; WM, white matter. 571plied in a test case of temporal lobe epilepsy revealing an increase in radiotracer activity in GM that was undetec ted in the PET image before correction for partial volume effects. In computer simulations, errors in the segmenta tion of GM and errors in registration of PET and MRI images resulted in less than 15% inaccuracy in the GM PET image. In conclusion, GM PET permits accurate de termination of the actual radiotracer concentration in hu man brain GM in vivo. The method differentiates whether a change in the apparent radiotracer concentration re flects solely an alteration in GM volume or rather a change in radiotracer concentration per unit volume of GM. Key Words: Brain gray matter-Positron emission tomography-Magnetic resonance imaging-Partial vol ume effects-Aging-Dementia-Brain atrophy.1988; Fowler, 1990; Frost and Wagner, 1990; Leen ders et aI., 1990;Martin et al., 1991; Mayberg et aI. , 1991). Nevertheless, a limitation of PET remains: its relatively poor spatial resolution. As a result, PET quantification, especially in structures smaller than two times the full width at half-maximum (FWHM) of the tomograph, is affected by partial volume effects (Hoffmann et aI. , 1979). Given that the in-plane FWHM of current PET instruments ranges from 2.6 mm (Valk et aI. , 1990) to about 14 mm, tracer activity in many brain structures, in cluding the neocortex, is often underestimated. In neocortex, gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) spaces are convo luted, and cannot be resolved using PET instrumen tation; a cortical PET signal thus reflects the aver age tracer concentr...

Longitudinal studies indicate that declines in cognition and memory accelerate after age 70 years. The neuroanatomic and neurophysiologic underpinnings of cognitive change are unclear, as there is little information on longitudinal brain changes. We are conducting a longitudinal neuroimaging study of nondemented older participants in the Baltimore Longitudinal Study of Aging. This report focuses on age and sex differences in brain structure measured by magnetic resonance imaging during the first two annual evaluations. Cross-sectional results from 116 participants aged 59-85 years reveal significantly larger ventricular volumes and smaller gray and white matter volumes in older compared with younger participants and in men compared with women. Regional brain volumes show that the effects of age and sex are not uniform across brain regions. Age differences are greatest for the parietal region. Sex differences tend to be larger for frontal and temporal than parietal and occipital regions. Longitudinal analysis demonstrates an increase of 1526 mm(3) in ventricular volume over 1 year, but no detectable change in total or regional brain volumes. Definition of the pattern and rate of longitudinal brain changes will facilitate the detection of pathological brain changes, which may be predictors of dementia.

Background and Purpose Cerebral magnetic resonance imaging often detects abnormalities whose significance is unknown. The prevalence and correlates of findings such as ventricular enlargement, sulcal widening, and increased white matter signal intensity were examined in 303 men and women aged 65 to 95 years participating in a multicenter study of cardiovascular disease.Methods Cerebral magnetic resonance imaging was performed and interpreted according to a standard protocol, and findings were correlated with measures of cardiovascular disease and its risk factors.Results Measures of cerebral atrophy increased with age and were greater in men than in women (each P<.01). Ventricular enlargement and sulcal widening were associated with prior stroke, hypertension, diabetes, and white race (each P<.03). Extent of white matter hyperintensity was associated with age, prior stroke, hypertension, and use of diuretics (each

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Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. In particular, MRI-derived brain age has been used as a comprehensive biomarker of brain health that can identify both advanced and resilient ageing individuals via deviations from typical brain ageing. Imaging signatures of various brain diseases, including schizophrenia and Alzheimer’s disease, have also been identified using machine learning. Prior efforts to derive these indices have been hampered by the need for sophisticated and not easily reproducible processing steps, by insufficiently powered or diversified samples from which typical brain ageing trajectories were derived, and by limited reproducibility across populations and MRI scanners. Herein, we develop and test a sophisticated deep brain network (DeepBrainNet) using a large (n = 11 729) set of MRI scans from a highly diversified cohort spanning different studies, scanners, ages and geographic locations around the world. Tests using both cross-validation and a separate replication cohort of 2739 individuals indicate that DeepBrainNet obtains robust brain-age estimates from these diverse datasets without the need for specialized image data preparation and processing. Furthermore, we show evidence that moderately fit brain ageing models may provide brain age estimates that are most discriminant of individuals with pathologies. This is not unexpected as tightly-fitting brain age models naturally produce brain-age estimates that offer little information beyond age, and loosely fitting models may contain a lot of noise. Our results offer some experimental evidence against commonly pursued tightly-fitting models. We show that the moderately fitting brain age models obtain significantly higher differentiation compared to tightly-fitting models in two of the four disease groups tested. Critically, we demonstrate that leveraging DeepBrainNet, along with transfer learning, allows us to construct more accurate classifiers of several brain diseases, compared to directly training classifiers on patient versus healthy control datasets or using common imaging databases such as ImageNet. We, therefore, derive a domain-specific deep network likely to reduce the need for application-specific adaptation and tuning of generic deep learning networks. We made the DeepBrainNet model freely available to the community for MRI-based evaluation of brain health in the general population and over the lifespan.

In this population-based study of older adults, although all measures of blood pressure were strongly and directly related to the risk of coronary and cerebrovascular events, SBP was the best single predictor of cardiovascular events.

Malignant melanoma is a prime example of cancers that respond poorly to various treatment modalities including chemotherapy. A number of chemotherapeutic agents have been shown recently to act by inducing apoptosis, a type of cell death antagonized by the bcl-2 gene. Human melanoma expresses Bcl-2 in up to 90% of all cases. In the present study we demonstrate that bcl-2 antisense oligonucleotide treatment improves the chemosensitivity of human melanoma grown in severe combined immunodeficient (SCID) mice. Our findings suggest that reduction of Bcl-2 in melanoma, and possibly also in a variety of other tumors, may be a novel and rational approach to improve chemosensitivity and treatment outcome.

Sulcal width, ventricular size, and white matter signal intensity change with age, sex, and race. Knowledge of these changes is important in appropriate interpretation of MR images of the elderly.