Doctor of Philosophy, New Jersey Institute Of Technology (2017)
Master of Science, New Jersey Institute Of Technology (2012)
MAPBOT: Meta-analytic parcellation based on text, and its application to the human thalamus
2017; 157: 716–32
Meta-analysis of neuroimaging results has proven to be a popular and valuable method to study human brain functions. A number of studies have used meta-analysis to parcellate distinct brain regions. A popular way to perform meta-analysis is typically based on the reported activation coordinates from a number of published papers. However, in addition to the coordinates associated with the different brain regions, the text itself contains considerably amount of additional information. This textual information has been largely ignored in meta-analyses where it may be useful for simultaneously parcellating brain regions and studying their characteristics. By leveraging recent advances in document clustering techniques, we introduce an approach to parcellate the brain into meaningful regions primarily based on the text features present in a document from a large number of studies. This new method is called MAPBOT (Meta-Analytic Parcellation Based On Text). Here, we first describe how the method works and then the application case of understanding the sub-divisions of the thalamus. The thalamus was chosen because of the substantial body of research that has been reported studying this functional and structural structure for both healthy and clinical populations. However, MAPBOT is a general-purpose method that is applicable to parcellating any region(s) of the brain. The present study demonstrates the powerful utility of using text information from neuroimaging studies to parcellate brain regions.
View details for DOI 10.1016/j.neuroimage.2017.06.032
View details for Web of Science ID 000410539800059
View details for PubMedID 28629976
Functional topography of the thalamocortical system in human
BRAIN STRUCTURE & FUNCTION
2016; 221 (4): 1971–84
Various studies have indicated that the thalamus is involved in controlling both cortico-cortical information flow and cortical communication with the rest of the brain. Detailed anatomy and functional connectivity patterns of the thalamocortical system are essential to understanding the cortical organization and pathophysiology of a wide range of thalamus-related neurological and neuropsychiatric diseases. The current study used resting-state fMRI to investigate the topography of the human thalamocortical system from a functional perspective. The thalamus-related cortical networks were identified by performing independent component analysis on voxel-based thalamic functional connectivity maps across a large group of subjects. The resulting functional brain networks were very similar to well-established resting-state network maps. Using these brain network components in a spatial regression model with each thalamic voxel's functional connectivity map, we localized the thalamic subdivisions related to each brain network. For instance, the medial dorsal nucleus was shown to be associated with the default mode, the bilateral executive, the medial visual networks; and the pulvinar nucleus was involved in both the dorsal attention and the visual networks. These results revealed that a single nucleus may have functional connections with multiple cortical regions or even multiple functional networks, and may be potentially related to the function of mediation or modulation of multiple cortical networks. This observed organization of thalamocortical system provided a reference for studying the functions of thalamic sub-regions. The importance of intrinsic connectivity-based mapping of the thalamocortical relationship is discussed, as well as the applicability of the approach for future studies.
View details for DOI 10.1007/s00429-015-1018-7
View details for Web of Science ID 000375558600012
View details for PubMedID 25924563
Regional homogeneity of resting-state fMRI contributes to both neurovascular and task activation variations
MAGNETIC RESONANCE IMAGING
2013; 31 (9): 1492–1500
The task induced blood oxygenation level dependent signal changes observed using functional magnetic resonance imaging (fMRI) are critically dependent on the relationship between neuronal activity and hemodynamic response. Therefore, understanding the nature of neurovascular coupling is important when interpreting fMRI signal changes evoked via task. In this study, we used regional homogeneity (ReHo), a measure of local synchronization of the BOLD time series, to investigate whether the similarities of one voxel with the surrounding voxels are a property of neurovascular coupling. FMRI scans were obtained from fourteen subjects during bilateral finger tapping (FTAP), digit-symbol substitution (DSST) and periodic breath holding (BH) paradigm. A resting-state scan was also obtained for each of the subjects for 4min using identical imaging parameters. Inter-voxel correlation analyses were conducted between the resting-state ReHo, resting-state amplitude of low frequency fluctuations (ALFF), BH responses and task activations within the masks related to task activations. There was a reliable mean voxel-wise spatial correlation between ReHo and other neurovascular variables (BH responses and ALFF). We observed a moderate correlation between ReHo and task activations (FTAP: r=0.32; DSST: r=0.22) within the task positive network and a small yet reliable correlation within the default mode network (DSST: r=-0.08). Subsequently, a linear regression was used to estimate the contribution of ReHo, ALFF and BH responses to the task activated voxels. The unique contribution of ReHo was minimal. The results suggest that regional synchrony of the BOLD activity is a property that can explain the variance of neurovascular coupling and task activations; but its contribution to task activations can be accounted for by other neurovascular factors such as the ALFF.
View details for DOI 10.1016/j.mri.2013.07.005
View details for Web of Science ID 000325839700005
View details for PubMedID 23969197
View details for PubMedCentralID PMC3873744