Individuals diagnosed with major depressive disorder (MDD) often ruminate about their depression ... more Individuals diagnosed with major depressive disorder (MDD) often ruminate about their depression and their life situations, impairing their concentration and performance on daily tasks. We examined whether rumination might be due to a deficit in the ability to expel negative information from short-term memory (STM), and fMRI was used to examine the neural structures involved in this ability. MDD and healthy control (HC) participants were tested using a directed-forgetting procedure in a short-term item recognition task. As predicted, MDD participants had more difficulty than did HCs in expelling negative, but not positive, words from STM. Overall, the neural networks involved in directed forgetting were similar for both groups, but the MDDs exhibited more spatial variability in activation in the left inferior frontal gyrus (a region critical for inhibiting irrelevant information), which may contribute to their relative inability to inhibit negative information.
Major Depressive Disorder (MDD) is among the most prevalent of all psychiatric disorders and is t... more Major Depressive Disorder (MDD) is among the most prevalent of all psychiatric disorders and is the single most burdensome disease worldwide. In attempting to understand the profound deficits that characterize MDD across multiple domains of functioning, researchers have identified aberrations in brain structure and function in individuals diagnosed with this disorder. In this review we synthesize recent data from human neuroimaging studies in presenting an integrated neural network framework for understanding the impairments experienced by individuals with MDD. We discuss the implications of these findings for assessment of and intervention for MDD. We conclude by offering directions for future research that we believe will advance our understanding of neural factors that contribute to the etiology and course of depression, and to recovery from this debilitating disorder.
Scientists are beginning to document abnormalities in white matter connectivity in major depressi... more Scientists are beginning to document abnormalities in white matter connectivity in major depressive disorder (MDD). Recent developments in diffusion-weighted image analyses, including tractography clustering methods, may yield improved characterization of these white matter abnormalities in MDD. In this study, we acquired diffusion-weighted imaging data from MDD participants and matched healthy controls. We analyzed these data using two tractography clustering methods: automated fiber quantification (AFQ) and the maximum density path (MDP) procedure. We used AFQ to compare fractional anisotropy (FA; an index of water diffusion) in these two groups across major white matter tracts. Subsequently, we used the MDP procedure to compare FA differences in fiber paths related to the abnormalities in major fiber tracts that were identified using AFQ. FA was higher in the bilateral corticospinal tracts (CSTs) in MDD (p's < 0.002). Secondary analyses using the MDP procedure detected pri...
Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging, Jan 29, 2014
Diffusion-weighted imaging allows for in vivo assessment of white matter structure, which can be ... more Diffusion-weighted imaging allows for in vivo assessment of white matter structure, which can be used to assess aberrations associated with disease. Several new methods permit the automated assessment of important white matter characteristics. In the current study we used Automated Fiber Quantification (AFQ) to assess differences between depressed and nondepressed individuals in 18 major white matter tracts. We then used the Maximum Density Path (MDP) method to further characterize group differences identified with AFQ. The results of the AFQ analyses indicated that fractional anisotropy (FA; an index of white matter integrity) along bilateral corticospinal tracts (CST) was higher in depressed than in nondepressed individuals. MDP analyses revealed that white matter anomalies were restricted to four subregions that included the corona radiata and the internal and external capsules. These results provide further evidence that MDD is associated with abnormalities in cortical-to-subcor...
Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging, Apr 1, 2014
Graph theory is increasingly used in the field of neuroscience to understand the large-scale netw... more Graph theory is increasingly used in the field of neuroscience to understand the large-scale network structure of the human brain. There is also considerable interest in applying machine learning techniques in clinical settings, for example, to make diagnoses or predict treatment outcomes. Here we used support-vector machines (SVMs), in conjunction with whole-brain tractography, to identify graph metrics that best differentiate individuals with Major Depressive Disorder (MDD) from nondepressed controls. To do this, we applied a novel feature-scoring procedure that incorporates iterative classifier performance to assess feature robustness. We found that small-worldness, a measure of the balance between global integration and local specialization, most reliably differentiated MDD from nondepressed individuals. Post-hoc regional analyses suggested that heightened connectivity of the subcallosal cingulate gyrus (SCG) in MDDs contributes to these differences. The current study provides a...
Individuals diagnosed with major depressive disorder (MDD) often ruminate about their depression ... more Individuals diagnosed with major depressive disorder (MDD) often ruminate about their depression and their life situations, impairing their concentration and performance on daily tasks. We examined whether rumination might be due to a deficit in the ability to expel negative information from short-term memory (STM), and fMRI was used to examine the neural structures involved in this ability. MDD and healthy control (HC) participants were tested using a directed-forgetting procedure in a short-term item recognition task. As predicted, MDD participants had more difficulty than did HCs in expelling negative, but not positive, words from STM. Overall, the neural networks involved in directed forgetting were similar for both groups, but the MDDs exhibited more spatial variability in activation in the left inferior frontal gyrus (a region critical for inhibiting irrelevant information), which may contribute to their relative inability to inhibit negative information.
Major Depressive Disorder (MDD) is among the most prevalent of all psychiatric disorders and is t... more Major Depressive Disorder (MDD) is among the most prevalent of all psychiatric disorders and is the single most burdensome disease worldwide. In attempting to understand the profound deficits that characterize MDD across multiple domains of functioning, researchers have identified aberrations in brain structure and function in individuals diagnosed with this disorder. In this review we synthesize recent data from human neuroimaging studies in presenting an integrated neural network framework for understanding the impairments experienced by individuals with MDD. We discuss the implications of these findings for assessment of and intervention for MDD. We conclude by offering directions for future research that we believe will advance our understanding of neural factors that contribute to the etiology and course of depression, and to recovery from this debilitating disorder.
Scientists are beginning to document abnormalities in white matter connectivity in major depressi... more Scientists are beginning to document abnormalities in white matter connectivity in major depressive disorder (MDD). Recent developments in diffusion-weighted image analyses, including tractography clustering methods, may yield improved characterization of these white matter abnormalities in MDD. In this study, we acquired diffusion-weighted imaging data from MDD participants and matched healthy controls. We analyzed these data using two tractography clustering methods: automated fiber quantification (AFQ) and the maximum density path (MDP) procedure. We used AFQ to compare fractional anisotropy (FA; an index of water diffusion) in these two groups across major white matter tracts. Subsequently, we used the MDP procedure to compare FA differences in fiber paths related to the abnormalities in major fiber tracts that were identified using AFQ. FA was higher in the bilateral corticospinal tracts (CSTs) in MDD (p's < 0.002). Secondary analyses using the MDP procedure detected pri...
Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging, Jan 29, 2014
Diffusion-weighted imaging allows for in vivo assessment of white matter structure, which can be ... more Diffusion-weighted imaging allows for in vivo assessment of white matter structure, which can be used to assess aberrations associated with disease. Several new methods permit the automated assessment of important white matter characteristics. In the current study we used Automated Fiber Quantification (AFQ) to assess differences between depressed and nondepressed individuals in 18 major white matter tracts. We then used the Maximum Density Path (MDP) method to further characterize group differences identified with AFQ. The results of the AFQ analyses indicated that fractional anisotropy (FA; an index of white matter integrity) along bilateral corticospinal tracts (CST) was higher in depressed than in nondepressed individuals. MDP analyses revealed that white matter anomalies were restricted to four subregions that included the corona radiata and the internal and external capsules. These results provide further evidence that MDD is associated with abnormalities in cortical-to-subcor...
Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging, Apr 1, 2014
Graph theory is increasingly used in the field of neuroscience to understand the large-scale netw... more Graph theory is increasingly used in the field of neuroscience to understand the large-scale network structure of the human brain. There is also considerable interest in applying machine learning techniques in clinical settings, for example, to make diagnoses or predict treatment outcomes. Here we used support-vector machines (SVMs), in conjunction with whole-brain tractography, to identify graph metrics that best differentiate individuals with Major Depressive Disorder (MDD) from nondepressed controls. To do this, we applied a novel feature-scoring procedure that incorporates iterative classifier performance to assess feature robustness. We found that small-worldness, a measure of the balance between global integration and local specialization, most reliably differentiated MDD from nondepressed individuals. Post-hoc regional analyses suggested that heightened connectivity of the subcallosal cingulate gyrus (SCG) in MDDs contributes to these differences. The current study provides a...
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