Brain parenchimal fraction and tissue volume compartments ...



Prediction of brain “senescence” evolution by system analysis approach modelling of non-linear three-stage kinetics.

Svetlana Egorovaa, Guibert Ulric Crevecoeurd (?), Zsuzsanna Liptaka, Lifeng Liua, Dominik Meiera, Nan-kuei Chena, Ron J. Killianyc, Marilyn S. Albertb, Charles R.G. Guttmanna,*

a Center for Neurological Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA 02115; b Departments of Psychiatry and Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA 02115; c Departments of Anatomy and Neurobiology, and Neurology, Boston University School of Medicine, Boston, MA, USA 02115; Scientific Glass Institute (InV), 10 Boulevard Defontaine, B 6000 Charleroi, Belgium (?).

Introduction: The evolution kinetics or aging of systems under given operating conditions as three non linear stage (growth and development, steady and gradual decline) is a common feature of systems, as well as biological systems [Crevecour, 2001]. The human brain (as system composed of interlinked cells working together adapting to operating conditions and challenges) goes through several large-scale changes as the individual progresses from embryo through to old age: pre-natal development (neurogenesis and programmed cell death); adolescence (synaptic pruning); aging (a decline in function and a change in gene expression). Senescence is the state or process of aging with deteriorative change that causes increased mortality (senex Latin - old man or old age). Organismal senescence is the aging of organisms and generally characterized by the declining ability to respond to stress, increasing homeostatic imbalance and increased risk of disease with death as the ultimate consequence of aging. Theory of systems failure in engineering or reliability of systems theory of biological aging [Gavrilov and Gavrilova 2001] was able to explain the exponential increment of age-related failure (hazard) rates. We propose that changes over time of a normalized human brain parenchymal volume (BPF) as parameter representative of the system (brain) could be possible to use as biomarker to estimate and predict time to “BPF senescence” based on the model of non-linear three-stage kinetics [Crevecour, 1993, 1994, 2001].

Methods: Cross-sectional data of 192 17-88 age subjects’ normalized human brain parenchymal volume (BPF) from validated and reproducible MRI morphometry/volumetry (TDS+) [Wei et al. 2002], [Warfield et al. 1995] [Guttmann et al. 1999] [Kikinis et al. 1992]. System analysis based on the model of non-linear three-stage kinetics [Crevecour, 2001] was kindly provided through freely available request on web site ()

Results: Presented analysis of age-heterogeneous crossectional data shows that BPF with time follow a typical "hill-shaped" ageing curve [Crevecour, 2001]: which rapidly increases during childhood, stabilizes at a maximum at time t2 then slowly decreases up to a critical time ti from which the decrease can speed up to a final collapse of the system: [pic], where H(t)=HPV(t), i.e. measured BPF, and "[pic]" is the time derivative of the global ageing function for the system: E(t)=kexp(αt)tβ with 0 ................
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