1
Diagnostics and Prognostics with High Dimensional Spatial-Temporal
Data: From Structures to Human Brains
Yan Xue
1
, Yuxiang Zhou
2
, Yongming Liu
3
1,3
Arizona State University, Tempe, AZ, 85287, USA
yxue37@asu.edu
Yongming.Liu@asu.edu
2
Mayo Clinic Hospital, Phoenix, AZ 85054, USA
zhou.yuxiang@mayo.edu
ABSTRACT
Diagnostics and prognostics with high-dimensional spatial-
temporal data require innovative methodologies due to the
inherent complexity of such datasets. This thesis explores the
challenges of diagnostics and prognostics in high-
dimensional spatial-temporal data, extending from physical
structures to complex human brain analyses through resting-
state functional magnetic resonance imaging (rs-fMRI).
Drawing an analogy to engineering structural health
monitoring using spatial-temporal vibration data, the
approach leverages techniques from engineering diagnostics
and prognostics data analytics to handle clinical problems
with similar characteristics. A pioneering approach is
developed to analyze multimodal datasets that not only
include advanced rs-fMRI features—Amplitude of Low-
frequency Fluctuations (ALFF), Regional Homogeneity
(ReHo), Euler Characteristics (EC), and Fractal Analysis—
but also encompass a wide array of clinical data. This
integration includes infant developmental metrics such as
birth weight and gestational age, maternal health factors like
BMI and fat mass, and environmental influences including
dietary intake and mental health during pregnancy. The study
establishes a robust computational framework that uses
advanced machine learning algorithms to analyze the
interplay of these diverse data types, enhancing the precision
and predictive power of our models for early childhood
development. Initial validations have demonstrated the
effectiveness of this comprehensive approach in identifying
ADHD, with ongoing efforts aimed at expanding the
methodology to address a broader range of developmental
disorders. This work not only advances the diagnostic and
prognostic capabilities in medical imaging but also
significantly contributes to the field of Prognostics and
Health Management (PHM). By providing a solid foundation
for managing and understanding high-dimensional and
multimodal spatial-temporal data across various disciplines,
it bridges the gap between engineering and clinical
diagnostics, demonstrating the potential for cross-
disciplinary innovation.
1. PROBLEM STATEMENT:
The application of high-dimensional spatial-temporal data
analysis, specifically through technologies such as resting-
state functional magnetic resonance imaging (rs-fMRI),
presents significant engineering challenges in the diagnostic
and prognostic domains, ranging from structural analysis to
neurological disorders. While rs-fMRI offers unparalleled
insights into brain function, leveraging this data for the early
diagnosis of developmental disorders such as ADHD
involves navigating vast datasets and complex interactions
within the brain (Canario et al., 2021; Zhang et al., 2019).
Current methodologies in engineering and data science often
struggle to efficiently process and extract actionable insights
from such multidimensional data, limiting their practical
utility in clinical settings (Gupta et al., 2022).
Drawing an analogy from engineering prognostics, where
spatial-temporal vibration data is used to monitor structural
health, similar challenges are faced in clinical prognostics
with rs-fMRI data. Both fields require the development of
