Qolamreza (Ray) Razlighi, PhD
- Assistant Professor of Neuroimaging (in Neurology and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain)
- Directory of Quantitative Neuroimaging Laboratory (QNL)
Negative BOLD Response. The main research project in QNL is to investigate the neural and neurophysiological mechanisms underpinning negative BOLD response (NBR). Although there is emerging evidence that sheds light on the mechanism underlying task-based positive BOLD response, the accompanying NBR is mostly unknown (Bentley et al., 2016; Hayden et al., 2009; Mullinger et al., 2014), even though studies investigating NBR started as early as the introduction of the BOLD-fMRI (Shmuel et al., 2002; Smith et al., 2004). One reason for this lack of progress is the possibility that brain regions exhibiting NBR for a specific task may have separate underlying mechanisms. For instance, the NBR often detected in the vicinity of the PBR has mostly been explained by hemodynamics of the neurovascular system rather than by underlying neuronal activity (Hu and Huang, 2015), whereas NBR often detected in the brain DMN regions has been associated with suppression of neuronal activity (Bentley et al., 2016; Hayden et al., 2009). We categorize NBR into four different groups based on its possible underlying mechanism; 1) NBR in the vicinity of the PBR (Hu and Huang, 2015), 2) NBR in the contralateral hemisphere (Smith et al., 2004), 3) NBR in the DMN regions (Hayden et al., 2009), 4) NBR in the ventricles (Bright et al., 2014; Thomas et al., 2013). We feel this categorization is important, as each type of the NBR likely has a distinct underlying mechanism.
Hierarchical Structure of the Brain’s Large Scale Networks. The topography of the default mode network (DMN) can be obtained using two different functional magnetic resonance imaging (fMRI) techniques: spontaneous but organized synchrony in the low-frequency fluctuations of resting-state fMRI (rs-fMRI), known as “functional connectivity,” or from the consistent and robust deactivations in task-based fMRI (tb-fMRI), here referred to as “negative BOLD response”. These two methods are fundamentally different, but on the basis of topographic similarity their results have often been used interchangeably to reflect brain’s resting-state, baseline or intrinsic activities, and the current consensus in the field appears to be that they are both representative of the same neurophysiological processes. However, recent evidence suggests that these two fMRI techniques measure two separate but overlapping processes (see Figure above). For example, our preliminary results suggest that the spatial and temporal expression of the DMN’s functional connectivity remains intact during task performance and it is equivalent to the expression of the functional connectivity at rest; other groups report comparable findings. Also, in our recent work we were able to impose an alteration in the task-based negative BOLD response in the DMN regions by shifting cognitive attention from one sensory stimulus to another, but this manipulation did not cause any changes to the underlying functional connectivity of the DMN.
Both functional connectivity and negative BOLD response in DMN regions have been reported to be disrupted with normal aging, the pre-clinical stage of Alzheimer’s disease, mild cognitive impairment and Alzheimer’s disease. However, since the assumption in the field is that the DMN’s functional connectivity and negative BOLD response are representative of the same underlying neurophysiological process, there has been no study, to our knowledge, investigating the cascade of the events in which functional connectivity or negative BOLD response get disrupted. We hypothesize that brain activities are executed by a set of functional sub-systems that have a hierarchical structure such that functional connectivity is representative of a lower level process than is the task-based network of negative BOLD response. Functional connectivity networks are representative of the processes that provide the underlying infrastructure for the functional sub-systems represented by the task-based network of BOLD response. If this is the case, a disruption in the task-based BOLD response should not necessarily be accompanied by an alteration in the underlying functional connectivity (as we have shown in our preliminary data below), whereas any disruption in the functional connectivity must result in an alteration in the task-based BOLD response. We aim to test our hypothesis using three experimental designs.
Pre-processing of resting-state BOLD fMRI data. The problem of fMRI pre-processing is profound and urgently needs rigorous attention to ensure the continuing high quality of basic and clinical neuroimaging research in the future, particularly given the propensity for ever-more complex derivation of functional-connectivity based biomarkers in resting-state fMRI. Extracting BOLD signal from fMRI data is challenging due to many reasons including but not limited to: high scanner and thermal noise, irregular sampling in interleaved slice acquisition, physiological noise, motion and artifactual related contamination, and inter-subject variability. Even more challenging is the interaction between these phenomena that somehow makes their optimal correction almost impossible. For instance, the interaction between slice timing and motion prevented the field to be able to consent on the order of which their corrections should be applied. Here in QNL we aim at using signal and image processing techniques to optimally extract the BOLD signal from the fMRI data. We have multiple projects tackling different pre-processing pipeline.
We have developed an optimal technique to extract the BOLD signal from interleaved fMRI data. In his project we use a simple signal reconstruction method that has been used in the field of signal processing for many years to correct of irregular sampling problem in interleaved slice acquisition. We have shown the superiority of this optimal method in compare to the existing methods and it’s interactions with other fMRI artifacts and processing steps. The software is implemented in C++ and is available for sharing online through github software development and sharing database.
A longstanding problem in functional neuroimaging studies of cognitive aging is that age-related changes in brain morphology make it difficult to co-register brains, a key step for studies comparing task-related activation in young and old groups. To demonstrate the severity of the problem, the video to the right shows 29 participants’ brains after spatial normalization. The corresponding regions are shown with the same color. The video clearly shows the magnitude of the variability for each voxel between subjects. To address this issue, we are developing a region-based spatial normalization (RBSN) technique that will increase the accuracy of the fMRI data localization. The prevailing spatial normalization method tries to align all regions of the brain at once. RBSN, on the other hand, aligns each neuroanatomical region of the human brain independently. RBSN will thus provide more accurate localization of activation and ensure that group analyses test the same brain area in each study participant. Better between-participant registration also provides additional statistical power to detect activation in regions that may not have reached the significance level using prevailing methods (i.e.it will reduce type II error). It may also rule out previously noted areas of activation that were detected for artifactual reasons (i.e., it will reduce type I error).
Online web-based fMRI simulator. A comprehensive fMRI simulator not only helps in evaluating and improving the already-developed BOLD extraction methods for false-positive detection, but it is also the only available tool that can evaluation false-negatives. The fMRI simulator can also keep the whole BOLD extraction pipeline constant while testing the effects of changes to a single step of the process. For example, both head motion and spatial normalization can have a catastrophic effect on fMRI results. Head motion interacts with interleaved slice acquisition. To be able to determine the optimal method for motion correction, it is necessary to first eliminate the effects of both spatial variability between subjects, and slice acquisition timing. This is possible only through the use of a comprehensive fMRI simulator, which we are in the process of developing currently.
- Department of Neurology
Division of Cognitive Neuroscience
(in addition to English)
- Farsi (Persian)
Education & Training
- PhD, Electrical Engineering , University of Texas
- Gertrude H. Sergievsky Center
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain
Dr. Razlighi was the Program Chair of the 4th annual meeting of the New York Metropolitan Imaging Research Symposium (NYMIRS2017).
Biomedical Engineering E4840 section 001
Functional Brain Imaging
327 Seeley W. Mudd Building
IEEE: Senior Member
Honors & Awards
National Institutes of Health career award (K01) in 2013
Columbia University research initiatives in science and engineering (RISE) award in 2016,
Taub Institute Alzheimer's disease research center (ADRC) pilot award in 2016,
Irving Institute/integrating special populations (ISP) pilot award in 2017.
ANALYZING AGE-RELATED CHANGES OF BRAIN ACTIVATION IN SUBJECTS NATIVE SPACE (Federal Gov)
Sep 1 2013 - May 31 2018
MEDICAL-IMAGING SYMPOSIUM (Private)
Nov 29 2017 - Dec 1 2017
EXPLORING COGNITIVE AGING USING REFERENCE ABILITY NEURAL NETWORKS (Federal Gov)
Sep 1 2011 - May 31 2017
IMAGING OF COGNITION, LEARNING AND MEMORY IN AGING (Federal Gov)
Sep 1 2011 - May 31 2017
PREDICTORS OF SEVERITY IN ALZHEIMER S DISEASE (Federal Gov)
Sep 1 2011 - May 31 2017
NEUROPSYCHOLOGY AND COGNITION IN AGING (Federal Gov)
Jun 15 1998 - Apr 30 2015
- David Parker, PhD Student
- Hengda He, PhD Student
- Amirreza Sedaghat, PhD Student
- Victor Issa Garcia, Graduate Student
For a complete list of publications, please visit PubMed.gov