Sadia Shakil

• Ph.D.
• Research Assistant Professor at Chinese University of Hong Kong

Our ability to share memories constitutes a social foundation of our world. When exposed to another person's memory, individuals can mentally reconstruct the events described, even if they were not present during the related events. However, the extent to which the neuronal connectivity patterns elicited by the mental reconstruction of an event mir...

The rapid development of e-Health provides elderly consumers with more convenient medical services. Alzheimer’s disease is one of the major diseases that threaten the health of the elderly. Its early detection is vital for its effective treatment and management. In this study, an end-to-end model, individual-to-group graph convolutional network (IG...

Mental stress has become one of the major reasons for the failure of students or their poor performance in the traditional limited-duration examination system. In this study, we aim to find the relationship between the student's level of stress and the deterioration of their subsequent examination results. Furthermore, we want to explore if differe...

Real-time gait event detection (GED) system can be utilized for gait analysis and tracking fitness activities. GED for various types of terrains (e.g., stair-walk, uneven surfaces, etc.) is still an open research problem. This study presents an inertial sensor-based approach for real-time GED system that works for diverse terrains in an uncontrolle...

Our emotions and sentiments are influenced by naturalistic stimuli such as the movies we watch and the songs we listen to, accompanied by changes in our brain activation. Comprehension of these brain-activation dynamics can assist in identification of any associated neurological condition such as stress and depression, leading towards making inform...

During the last decade, computer vision and machine learning have revolutionized the world in every way possible. Deep Learning is a sub field of machine learning that has shown remarkable results in every field especially biomedical field due to its ability of handling huge amount of data. Its potential and ability have also been applied and teste...

Electromyography (EMG) is the study of electrical activity in the muscles. We classify EMG signals from surface electrodes (channels) using Artificial Neural Network (ANN). We evaluate classification performance of 10 different hand motions using several feature-channel combinations with wrapper method. Highest classification accuracy of 98.7% is a...

Alzheimer's disease (AD) is the most common form of dementia, accounting for 50–75% of all cases, with a greater proportion of individuals affected at older age range. A single moderate or severe traumatic brain injury (TBI) is associated with accelerated aging and increased risk for dementia. The fastest growth in the elderly population is taking...

Objective: In this paper we explore the dependence of sliding window correlation (SWC) results on different parameters of correlating signals. The SWC is extensively used to explore the dynamics of functional connectivity (FC) networks using resting-state functional MRI (rsfMRI) scans. These scanned signals are often non-stationary and comprised o...

Measures of whole-brain activity, from techniques such as functional Magnetic Resonance Imaging, provide a means to observe the brain's dynamical operations. However, interpretation of whole-brain dynamics has been stymied by the inherently high-dimensional structure of brain activity. The present research addresses this challenge through a series...

Measures of whole-brain activity, from techniques such as functional Magnetic Resonance Imaging, provide a means to observe the brain’s dynamical operations. However, interpretation of whole- brain dynamics has been stymied by the inherently high-dimensional structure of brain activity. The present research addresses this challenge through a series...

The BOLD signal reflects hemodynamic events within the brain, which in turn are driven by metabolic changes and neural activity. However, the link between BOLD changes and neural activity is indirect and can be influenced by a number of non-neuronal processes. Motion and physiological cycles have long been known to affect the BOLD signal and are pr...

The brain is inherently multiscalar in both space and time. We argue that this multiscalar nature is reflected in the blood oxygenation level dependent (BOLD) fluctuations used to map functional connectivity. We present evidence that global fluctuations in activity, quasiperiodic spatiotemporal patterns, and aperiodic time-varying activity coexist...

This study presents a new algorithm to adaptively detect change points of functional connectivity networks in the brain. It uses scans from resting-state functional magnetic resonance imaging (rsfMRI) which is one of the major tools to investigate intrinsic brain functionality. Different regions of the resting brain form networks that change states...

A promising recent development in the study of brain function is the dynamic analysis of resting-state functional MRI scans, which can enhance understanding of normal cognition and alterations that result from brain disorders. One widely used method of capturing the dynamics of functional connectivity is sliding window correlation (SWC). However, i...

Sliding window correlation (SWC) is one of the most popular methods to study the dynamics of functional connectivity from resting-state functional MRI scans. These scanned signals are often non-stationary and normally bandpass filtered before the SWC analysis, so there are more than one frequency component present in the correlating signals. In thi...

Resting state functional MRI (rs-fMRI) and functional connectivity mapping have become widely used tools in the human neuroimaging community and their use is rapidly spreading into the realm of rodent research as well. One of the many attractive features of rs-fMRI is that it is readily translatable from humans to animals and back again. Changes in...

Different regions in the resting brain exhibit non-stationary functional connectivity (FC) over time. In this paper, a simple and efficient framework of clustering the variability in FC of a rat's brain at rest is proposed. This clustering process reveals areas that are always connected with a chosen region, called seed voxel, along with the areas...

Resting state functional magnetic resonance imaging (fMRI) can identify network alterations that occur in complex psychiatric diseases and behaviors, but its interpretation is difficult because the neural basis of the infraslow BOLD fluctuations is poorly understood. Previous results link dynamic activity during the resting state to both infraslow...