Hamed received his PhD in Electrical & Computer Engineering from Johns Hopkins University. With his background in Artificial Intelligence, Machine Learning, Statistical Signal/Image Processing and passion in software prototyping and proof of concept, he is interested in methodology development and application of AI in neuroimaging, computational neuroscience, and interdisciplinary research and development.
Before joining Stanford, he worked a Data Scientist at World Bank Group in Washington, DC where he used his background and research skills leveraging AI for innovative solutions and showcase effectiveness of technology-driven solutions in real-world contexts through design thinking research and PoV prototyping, including Computer Vision, Generative AI (LLMs), and NLP.
During his PhD, he worked on introducing new approaches for assessing time-varying functional brain connectivity. Currently, as a Postdoctoral Research Fellow, his interests are focused on use of data driven techniques and machine learning for neuroimaging in particular for assessing functional connectivity.
Hamed has shown a track record of applying research and problem solving across various domains and its corresponding domain data such as Healthcare, Financial and Public Sector, Energy and Interdisciplinary Engineering domains.
Honors & Awards
Awarded Emerging Clean Energy Leadership Fellowship, NC Sustainable Energy Association
Commitment to Service for Dedication and Work Award, NC Sustainable Energy Association
PhD, Johns Hopkins University, Electrical & Computer Engineering (2022)
MS, Johns Hopkins University, Electrical & Computer Engineering (2017)
MS, NC State University, Mechanical Engineering (2013)
BS, University of Tehran, Mechanical Engineering (2009)
Current Research and Scholarly Interests
Machine Learning, Neuroimaging, Computer Vision,Deep Learning, Signal Processing
Mode decomposition-based time-varying phase synchronization for fMRI
2022; 261: 119519
Recently, there has been significant interest in measuring time-varying functional connectivity (TVC) between different brain regions using resting-state functional magnetic resonance imaging (rs-fMRI) data. One way to assess the relationship between signals from different brain regions is to measure their phase synchronization (PS) across time. However, this requires the a priori choice of type and cut-off frequencies for the bandpass filter needed to perform the analysis. Here we explore alternative approaches based on the use of various mode decomposition (MD) techniques that provide a more data driven solution to this issue. These techniques allow for the data driven decomposition of signals jointly into narrow-band components at different frequencies, thus fulfilling the requirements needed to measure PS. We explore several variants of MD, including empirical mode decomposition (EMD), bivariate EMD (BEMD), noise-assisted multivariate EMD (na-MEMD), and introduce the use of multivariate variational mode decomposition (MVMD) in the context of estimating time-varying PS. We contrast the approaches using a series of simulations and application to rs-fMRI data. Our results show that MVMD outperforms other evaluated MD approaches, and further suggests that this approach can be used as a tool to reliably investigate time-varying PS in rs-fMRI data.
View details for DOI 10.1016/j.neuroimage.2022.119519
View details for Web of Science ID 000889462400006
View details for PubMedID 35905810
View details for PubMedCentralID PMC9451171
Dynamic Functional Brain Connectivity Underlying Temporal Summation of Pain in Fibromyalgia
ARTHRITIS & RHEUMATOLOGY
2022; 74 (4): 700-710
Abnormal central pain processing is a leading cause of pain in fibromyalgia (FM) and is perceptually characterized with the psychophysical measure of temporal summation of pain (TSP). TSP is the perception of increasingly greater pain in response to repetitive or tonic noxious stimuli. Previous neuroimaging studies have used static (i.e., summary) measures to examine the functional magnetic resonance imaging (fMRI) correlates of TSP in FM. However, functional brain activity rapidly and dynamically reorganizes over time, and, similarly, TSP is a temporally evolving process. This study was undertaken to demonstrate how a complete understanding of the neural circuitry supporting TSP in FM thus requires a dynamic measure that evolves over time.We utilized novel methods for analyzing dynamic functional brain connectivity in patients with FM in order to examine how TSP-associated fluctuations are linked to the dynamic functional reconfiguration of the brain. In 84 FM patients and age- and sex-matched healthy controls, we collected high-temporal-resolution fMRI data during a resting state and during a state in which sustained cuff pressure pain was applied to the leg.FM patients experienced greater TSP than healthy controls (mean ± SD TSP score 17.93 ± 19.24 in FM patients versus 9.47 ± 14.06 in healthy controls; P = 0.028), but TSP scores varied substantially between patients. In the brain, the presence versus absence of TSP in patients with FM was marked by more sustained enmeshment between sensorimotor and salience networks during the pain period. Furthermore, dynamic enmeshment was noted solely in FM patients with high TSP, as interactions with all other brain networks were dampened during the pain period.This study elucidates the dynamic brain processes underlying facilitated central pain processing in FM. Our findings will enable future investigation of dynamic symptoms in FM.
View details for DOI 10.1002/art.42013
View details for Web of Science ID 000766958300001
View details for PubMedID 34725971
Phase-locking of resting-state brain networks with the gastric basal electrical rhythm
2021; 16 (1): e0244756
A network of myenteric interstitial cells of Cajal in the corpus of the stomach serves as its "pacemaker", continuously generating a ca 0.05 Hz electrical slow wave, which is transmitted to the brain chiefly by vagal afferents. A recent study combining resting-state functional MRI (rsfMRI) with concurrent surface electrogastrography (EGG), with cutaneous electrodes placed on the epigastrium, found 12 brain regions with activity that was significantly phase-locked with this gastric basal electrical rhythm. Therefore, we asked whether fluctuations in brain resting state networks (RSNs), estimated using a spatial independent component analysis (ICA) approach, might be synchronized with the stomach. In the present study, in order to determine whether any RSNs are phase-locked with the gastric rhythm, an individual participant underwent 22 scanning sessions; in each, two 15-minute runs of concurrent EGG and rsfMRI data were acquired. EGG data from three sessions had weak gastric signals and were excluded; the other 19 sessions yielded a total of 9.5 hours of data. The rsfMRI data were analyzed using group ICA; RSN time courses were estimated; for each run, the phase-locking value (PLV) was computed between each RSN and the gastric signal. To assess statistical significance, PLVs from all pairs of "mismatched" data (EGG and rsfMRI data acquired on different days) were used as surrogate data to generate a null distribution for each RSN. Of a total of 18 RSNs, three were found to be significantly phase-locked with the basal gastric rhythm, namely, a cerebellar network, a dorsal somatosensory-motor network, and a default mode network. Disruptions to the gut-brain axis, which sustains interoceptive feedback between the central nervous system and the viscera, are thought to be involved in various disorders; manifestation of the infra-slow rhythm of the stomach in brain rsfMRI data could be useful for studies in clinical populations.
View details for DOI 10.1371/journal.pone.0244756
View details for Web of Science ID 000607070700008
View details for PubMedID 33400717
View details for PubMedCentralID PMC7785240
Evaluating phase synchronization methods in fMRI: A comparison study and new approaches
2021; 228: 117704
In recent years there has been growing interest in measuring time-varying functional connectivity between different brain regions using resting-state functional magnetic resonance imaging (rs-fMRI) data. One way to assess the relationship between signals from different brain regions is to measure their phase synchronization (PS) across time. There are several ways to perform such analyses, and we compare methods that utilize a PS metric together with a sliding window, referred to here as windowed phase synchronization (WPS), with those that directly measure the instantaneous phase synchronization (IPS). In particular, IPS has recently gained popularity as it offers single time-point resolution of time-resolved fMRI connectivity. In this paper, we discuss the underlying assumptions required for performing PS analyses and emphasize the importance of band-pass filtering the data to obtain valid results. Further, we contrast this approach with the use of Empirical Mode Decomposition (EMD) to achieve similar goals. We review various methods for evaluating PS and introduce a new approach within the IPS framework denoted the cosine of the relative phase (CRP). We contrast methods through a series of simulations and application to rs-fMRI data. Our results indicate that CRP outperforms other tested methods and overcomes issues related to undetected temporal transitions from positive to negative associations common in IPS analysis. Further, in contrast to phase coherence, CRP unfolds the distribution of PS measures, which benefits subsequent clustering of PS matrices into recurring brain states.
View details for DOI 10.1016/j.neuroimage.2020.117704
View details for Web of Science ID 000617722700023
View details for PubMedID 33385554
View details for PubMedCentralID PMC8011682
Investigating the impact of autocorrelation on time-varying connectivity
2019; 197: 37-48
In recent years, a number of studies have reported on the existence of time-varying functional connectivity (TVC) in resting-state functional magnetic resonance imaging (rs-fMRI) data. The sliding-window technique is currently one of the most commonly used methods to estimate TVC. Although previous studies have shown that autocorrelation can negatively impact estimates of static functional connectivity, its impact on TVC estimates is not well known at this time. In this paper, we show both theoretically and empirically that the existence of autocorrelation within a time series can inflate the sampling variability of TVC estimated using the sliding-window technique. This can in turn increase the risk of misinterpreting noise as true TVC and negatively impact subsequent estimation of whole-brain time-varying FC profiles, or "brain states". The latter holds as more variable input measures lead to more variable output measures in the state estimation procedure. Finally, we demonstrate that prewhitening the data prior to analysis can lower the variance of the estimated TVC and improve brain state estimation. These results suggest that careful consideration is required when making inferences on TVC.
View details for DOI 10.1016/j.neuroimage.2019.04.042
View details for Web of Science ID 000472161500004
View details for PubMedID 31022568
View details for PubMedCentralID PMC6684286
- Application priority of GSHP systems in the climate conditions of the United States (vol 13, pg 1, 2017) ADVANCES IN BUILDING ENERGY RESEARCH 2019; 13 (1): III
- Methodology for energy strategy to prescreen the feasibility of Ground Source Heat Pump systems in residential and commercial buildings in the United States ENERGY STRATEGY REVIEWS 2017; 18: 53-62
Economic Analysis of Ground Source Heat Pumps in North Carolina
AMER SOC HEATING, REFRIGERATING AND AIR-CONDITIONING ENGS. 2014
View details for Web of Science ID 000346573500004
PREDICTION OF FORCED CONVECTION FLOW IN A PARALLEL PLATE CHANNEL FILLED WITH POROUS MEDIA
AMER SOC MECHANICAL ENGINEERS. 2009: 631-635
View details for Web of Science ID 000286415600083