A few thoughts on brain ROIs

Liu, Tianming

doi:10.1007/s11682-011-9123-6

Cited by 50 publications

(64 citation statements)

References 56 publications

(87 reference statements)

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“…7. Our interpretation of this discrepancy is that functional connectivity analysis results heavily depend on the selection of ROIs in the brain (Liu 2011). In (Rathi et al 2011), the ROIs were defined by 90 anatomically-defined brain regions, such as gyri determined by the traditional Automated Anatomical Labeling (AAL) atlas, and then fMRI signals were averaged within each anatomic region.…”

Section: Discussionmentioning

confidence: 99%

“…However, determination of meaningful and consistent ROIs for brain connectivity mapping is a very challenging task, as outlined in a recent review article in (Liu 2011). Briefly, these challenges include unclear boundaries between cortical regions, remarkable variability of anatomy and function across different brains and high nonlinearity of ROI properties, e.g., a slight change of the size, shape or location could significantly alter the structural and/or functional connectivity of the ROI in consideration (Liu 2011). One promising solution to these challenges is to maximize the group-wise consistency of diffusion tensor imaging (DTI)-derived structural connection profiles within a corresponding ROI across subjects, as demonstrated in (Zhu et al 2011).…”

Section: Introductionmentioning

confidence: 99%

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Increased cortico-subcortical functional connectivity in schizophrenia

Zhang

et al. 2011

Brain Imaging and Behavior

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It has been widely reported that structural and functional connectivities are disturbed in cortical networks in schizophrenia (SZ). However, much less is known about the structural and functional connectivities between cortical and subcortical regions in SZ. Here, diffusion tensor imaging (DTI) data was used to identify consistent cortico-subcortical structural connection patterns across SZ patients and controls, and thus 13 common cortical Regions of Interest (ROIs) were determined. DTI and resting state fMRI (R-fMRI) datasets were used to assess the structural and functional connectivities between the 13 cortical ROIs and 12 subcortical regions in 8 SZ patients and 10 normal controls. It was found that there are significantly increased functional connectivities for 7 cortico-subcortical connections between the 13 cortical ROIs and 12 subcortical regions. Among most of these connections, the functional connectivity strength was doubled in SZ in comparison to controls. The cortical ROIs with functional hyper-connectivities to subcortical regions are localized in frontal and parietal lobes. However, no significant difference in the structural connectivity between these cortical and subcortical regions was found between SZ and controls. Additional analysis results showed 4 significantly increased and 2 significantly decreased cortico-cortical connections. Our study results suggest the functional hyper-connectivity between cortical and subcortical regions, adding further evidence to literature findings that SZ is a disorder of connectivity between components of large-scale brain networks. The result of either increased or decreased functional connectivities among cortical ROIs exhibits the complex pattern of disturbance of brain networks in SZ.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Increased cortico-subcortical functional connectivity in schizophrenia

Zhang

et al. 2011

Brain Imaging and Behavior

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“…The identification of associations between anatomically distinct time series is referred to as "functional connectivity" [140]. The ability to identify consistent, reproducible, and accurate regions of interest is the key to developing connectivity maps [142]. Using a new strategy to develop cortical landmarks (dense individualized and common connectivity-based cortical landmarks, DICCOLs), Li et al [143] used functional connectomics signatures to identify 10 brain regions with structurally disrupted landmarks that could be used to distinctly identify prenatal cocaine exposed brains from that of controls.…”

Section: Novel Applications Of Imaging Methods and Statistical Technimentioning

confidence: 99%

Review of Current Neuroimaging Studies of the Effects of Prenatal Drug Exposure: Brain Structure and Function

Willford¹,

Smith²,

Kuhn³

et al. 2016

Recent Advances in Drug Addiction Research and Clinical Applications

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Neuroimaging tools have provided novel methods for understanding the impact of prenatal drug exposure on brain structure and function and its relation to development and behavior. Information gained from neuroimaging studies allows for the investigation of how prenatal drug exposure alters the typical developmental trajectory. The current prevalence and characteristics of prenatal drug exposure and its implications for vulnerable periods of brain development are reviewed. Structural and functional neuroimaging methods are introduced with examples of how study results from prenatal alcohol, cocaine, marijuana, and tobacco exposure further our understanding of the neurodevelopment impact of prenatal drug exposure. Prenatal drug neuroimaging studies have advanced our understanding of mechanisms and functional deficits associated with prenatal drug exposure. Studies have identified brain circuits associated with the default mode network, inhibitory control, and working memory that show differences in function as a result of prenatal drug exposure. The information gained from studies of prenatal drug exposure on brain structure and function can be used to make connections between animal models and human studies of prenatal drug exposure, identify biomarkers of documented effects of prenatal drug exposure on behavior, and inform prevention and intervention programs for young children.

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“…Voxels and ROIs were determined manually based on neuroscience domain knowledge or automatically based on activation detection using task-fMRI. Although voxels and ROIs-based methods are easy to implement and effective in many existing works, their reproducibility, generalizability and reliability have been limited due to the lack of a common and individualized representation of human brain architecture as pointed out in (Liu 2011;Chen et al 2014). To be specific, voxel-based methods pose difficulties in assessing the consistency of encoding models across subjects due to the intrinsic variability of brain structure and functions and thus the lack of precise voxelwise correspondence between subjects (Liu 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Although voxels and ROIs-based methods are easy to implement and effective in many existing works, their reproducibility, generalizability and reliability have been limited due to the lack of a common and individualized representation of human brain architecture as pointed out in (Liu 2011;Chen et al 2014). To be specific, voxel-based methods pose difficulties in assessing the consistency of encoding models across subjects due to the intrinsic variability of brain structure and functions and thus the lack of precise voxelwise correspondence between subjects (Liu 2011). Recently, we developed and validated a novel data-driven strategy, namely DICCCOL (dense individualized and common connectivity-based cortical landmarks) , to discover consistent and corresponding structural landmarks across various brains.…”

Section: Introductionmentioning

confidence: 99%

Encoding brain network response to free viewing of videos

Han

Zhao

et al. 2014

Cogn Neurodyn

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A challenging goal for cognitive neuroscience researchers is to determine how mental representations are mapped onto the patterns of neural activity. To address this problem, functional magnetic resonance imaging (fMRI) researchers have developed a large number of encoding and decoding methods. However, previous studies typically used rather limited stimuli representation, like semantic labels and Wavelet Gabor filters, and largely focused on voxel-based brain patterns. Here, we present a new fMRI encoding model to predict the human brain's responses to free viewing of video clips which aims to deal with this limitation. In this model, we represent the stimuli using a variety of representative visual features in the computer vision community, which can describe the global color distribution, local shape and spatial information and motion information contained in videos, and apply the functional connectivity to model the brain's activity pattern evoked by these video clips. Our experimental results demonstrate that brain network responses during free viewing of videos can be robustly and accurately predicted across subjects by using visual features. Our study suggests the feasibility of exploring cognitive neuroscience studies by computational image/video analysis and provides a novel concept of using the brain encoding as a test-bed for evaluating visual feature extraction.

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A few thoughts on brain ROIs

Cited by 50 publications

References 56 publications

Increased cortico-subcortical functional connectivity in schizophrenia

Increased cortico-subcortical functional connectivity in schizophrenia

Review of Current Neuroimaging Studies of the Effects of Prenatal Drug Exposure: Brain Structure and Function

Encoding brain network response to free viewing of videos

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