Multimodal fusion in neuroimaging combines data from multiple imaging modalities to
overcome the fundamental limitations of individual modalities. Neuroimaging fusion can …

Brain diseases, including tumors and mental and neurological disorders, seriously threaten
the health and well-being of millions of people worldwide. Structural and functional …

Brain functional networks have been traditionally studied considering only interactions
between pairs of regions, neglecting the richer information encoded in higher orders of …

The accurate diagnosis of Alzheimer's disease (AD) and its early stage, ie, mild cognitive
impairment, is essential for timely treatment and possible delay of AD. Fusion of multimodal …

Functional Magnetic Resonance Imaging (fMRI) studies have become increasingly popular
both with clinicians and researchers as they are capable of providing unique insights into …

Investigations of the neuro-physiological correlates of mental loads, or states, have attracted
significant attention recently, as it is particularly important to evaluate mental fatigue in …

Background Brain age is a biomarker that predicts chronological age using neuroimaging
features. Deviations of this predicted age from chronological age is considered a sign of age …

In this work, we show how permutation methods can be applied to combination analyses
such as those that include multiple imaging modalities, multiple data acquisitions of the …

The recent developments in time-domain diffuse optics that rely on physical concepts (eg,
time-gating and null distance) and advanced photonic components (eg, vertical cavity …

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional,
and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of …