Publications

Journal Articles


  • Anomalous gray matter structural networks in major depressive disorder. Biological psychiatry Singh, M. K., Kesler, S. R., Hadi Hosseini, S. M., Kelley, R. G., Amatya, D., Hamilton, J. P., Chen, M. C., Gotlib, I. H. 2013; 74 (10): 777-785

    Abstract

    BACKGROUND: Major depressive disorder (MDD) is characterized by abnormalities in structure, function, and connectivity in several brain regions. Few studies have examined how these regions are organized in the brain or investigated network-level structural aberrations that might be associated with depression. METHODS: We used graph analysis to examine the gray matter structural networks of individuals diagnosed with MDD (n = 93) and a demographically similar healthy comparison group (n = 151) with no history of psychopathology. The efficiency of structural networks for processing information was determined by quantifying local interconnectivity (clustering) and global integration (path length). We also compared the groups on the contributions of high-degree nodes (i.e., hubs) and regional network measures, including degree (number of connections in a node) and betweenness (fraction of short path connections in a node). RESULTS: Depressed participants had significantly decreased clustering in their brain networks across a range of network densities. Compared with control subjects, depressed participants had fewer hubs primarily in medial frontal and medial temporal areas, had higher degree in the left supramarginal gyrus and right gyrus rectus, and had higher betweenness in the right amygdala and left medial orbitofrontal gyrus. CONCLUSIONS: Networks of depressed individuals are characterized by a less efficient organization involving decreased regional connectivity compared with control subjects. Regional connections in the amygdala and medial prefrontal cortex may play a role in maintaining or adapting to depressive pathology. This is the first report of anomalous large-scale gray matter structural networks in MDD and provides new insights concerning the neurobiological mechanisms associated with this disorder.

    View details for DOI 10.1016/j.biopsych.2013.03.005

    View details for PubMedID 23601854

  • Comparing connectivity pattern and small-world organization between structural correlation and resting-state networks in healthy adults. NeuroImage Hosseini, S. M., Kesler, S. R. 2013; 78: 402-414

    Abstract

    In recent years, coordinated variations in brain morphology (e.g. volume, thickness, surface area) have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks (SCNs). However, it remains unclear how morphometric correlations relate to functional connectivity between brain regions. Resting-state networks (RSNs), derived from coordinated variations in neural activity at rest, have been shown to reflect connectivity between functionally related regions as well as, to some extent, anatomical connectivity between brain regions. Therefore, it is intriguing to investigate similarities between SCN and RSN to help identify how morphometric correlations relate to connections defined by resting-state connectivity. We investigated the similarities in connectivity patterns and small-world organization between SCN, derived from correlations of regional gray matter volume across individuals, and RSN in 36 healthy individuals. The results showed a significant similarity between SCN and RSN (60% for positive connections and 40% for negative connections) that might be explained by shared experience-related functional connectivity underlying both SCN and RSN. Conversely, the small-world parameters of the networks were significantly different, suggesting that SCN topological parameters cannot be regarded as a substitute for topological organization in resting-state networks. While our data suggest that using structural correlation networks can be useful in understanding alterations in structural associations in various brain disorders, it should be noted that a portion of the observed alterations might be explained by factors other than those reflecting resting-state connectivity.

    View details for DOI 10.1016/j.neuroimage.2013.04.032

    View details for PubMedID 23603348

  • Cognitive Training for Improving Executive Function in Chemotherapy-Treated Breast Cancer Survivors CLINICAL BREAST CANCER Kesler, S., Hosseini, S. M., Heckler, C., Janelsins, M., Palesh, O., Mustian, K., Morrow, G. 2013; 13 (4): 299-306

    Abstract

    BACKGROUND: A majority of breast cancer (BC) survivors, particularly those treated with chemotherapy, experience long-term cognitive deficits that significantly reduce quality of life. Among the cognitive domains most commonly affected include executive functions (EF), such as working memory, cognitive flexibility, multitasking, planning, and attention. Previous studies in other populations have shown that cognitive training, a behavioral method for treating cognitive deficits, can result in significant improvements in a number of cognitive skills, including EF. MATERIALS AND METHODS: In this study, we conducted a randomized controlled trial to investigate the feasibility and preliminary effectiveness of a novel, online EF training program in long-term BC survivors. A total of 41 BC survivors (21 active, 20 wait list) completed the 48 session training program over 12 weeks. The participants were, on average, 6 years after therapy. Results: Cognitive training led to significant improvements in cognitive flexibility, verbal fluency and processing speed, with marginally significant downstream improvements in verbal memory as assessed via standardized measures. Self-ratings of EF skills, including planning, organizing, and task monitoring, also were improved in the active group compared with the wait list group. CONCLUSIONS: Our findings suggest that EF skills may be improved even in long-term survivors by using a computerized, home-based intervention program. These improvements may potentially include subjective EF skills, which suggest a transfer of the training program to real-world behaviors.

    View details for DOI 10.1016/j.clbc.2013.02.004

    View details for Web of Science ID 000321239600011

    View details for PubMedID 23647804

  • Default mode network connectivity distinguishes chemotherapy-treated breast cancer survivors from controls PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Kesler, S. R., Wefel, J. S., Hosseini, S. M., Cheung, M., Watson, C. L., Hoeft, F. 2013; 110 (28): 11600-11605

    Abstract

    Breast cancer (BC) chemotherapy is associated with cognitive changes including persistent deficits in some individuals. We tested the accuracy of default mode network (DMN) resting state functional connectivity patterns in discriminating chemotherapy treated (C+) from non-chemotherapy (C-) treated BC survivors and healthy controls (HC). We also examined the relationship between DMN connectivity patterns and cognitive function. Multivariate pattern analysis was used to classify 30 C+, 27 C-, and 24 HC, which showed significant accuracy for discriminating C+ from C- (91.23%, P < 0.0001) and C+ from HC (90.74%, P < 0.0001). The C- group did not differ significantly from HC (47.06%, P = 0.60). Lower subjective memory function was correlated (P < 0.002) with greater hyperplane distance (distance from the linear decision function that optimally separates the groups). Disrupted DMN connectivity may help explain long-term cognitive difficulties following BC chemotherapy.

    View details for DOI 10.1073/pnas.1214551110

    View details for Web of Science ID 000321827000085

    View details for PubMedID 23798392

  • Influence of Choice of Null Network on Small-World Parameters of Structural Correlation Networks PLOS ONE Hosseini, S. M., Kesler, S. R. 2013; 8 (6)

    Abstract

    In recent years, coordinated variations in brain morphology (e.g., volume, thickness) have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks. Recent evidence suggests that brain networks constructed in this manner are inherently more clustered than random networks of the same size and degree. Thus, null networks constructed by randomizing topology are not a good choice for benchmarking small-world parameters of these networks. In the present report, we investigated the influence of choice of null networks on small-world parameters of gray matter correlation networks in healthy individuals and survivors of acute lymphoblastic leukemia. Three types of null networks were studied: 1) networks constructed by topology randomization (TOP), 2) networks matched to the distributional properties of the observed covariance matrix (HQS), and 3) networks generated from correlation of randomized input data (COR). The results revealed that the choice of null network not only influences the estimated small-world parameters, it also influences the results of between-group differences in small-world parameters. In addition, at higher network densities, the choice of null network influences the direction of group differences in network measures. Our data suggest that the choice of null network is quite crucial for interpretation of group differences in small-world parameters of structural correlation networks. We argue that none of the available null models is perfect for estimation of small-world parameters for correlation networks and the relative strengths and weaknesses of the selected model should be carefully considered with respect to obtained network measures.

    View details for DOI 10.1371/journal.pone.0067354

    View details for Web of Science ID 000321148400063

    View details for PubMedID 23840672

  • Topological properties of large-scale structural brain networks in children with familial risk for reading difficulties NEUROIMAGE Hosseini, S. M., Black, J. M., Soriano, T., Bugescu, N., Martinez, R., Raman, M. M., Kesler, S. R., Hoeft, F. 2013; 71: 260-274

    Abstract

    Developmental dyslexia is a neurobiological deficit characterized by persistent difficulty in learning to read in children and adults who otherwise possess normal intelligence. Functional and structural connectivity data suggest that developmental dyslexia could be a disconnection syndrome. However, whether abnormalities in connectivity exist in beginning readers at-risk for reading difficulties is unknown. Using graph-theoretical analysis, we investigated differences in global and regional topological properties of structural brain networks in 42 beginning readers with (FH+) and without (FH-) familial risk for reading difficulties. We constructed separate structural correlation networks based on measures of surface area and cortical thickness. Results revealed changes in topological properties in brain regions known to be abnormal in dyslexia (left supramarginal gyrus, left inferior frontal gyrus) in the FH+ group mainly in the network constructed from measures of cortical surface area. We also found alterations in topological properties in regions that are not often advertised as dyslexia but nonetheless play important role in reading (left posterior cingulate, hippocampus, and left precentral gyrus). To our knowledge, this is the first report of altered topological properties of structural correlation networks in children at risk for reading difficulty, and motivates future studies that examine the mechanisms underlying how these brain networks may mediate the influences of family history on reading outcome.

    View details for DOI 10.1016/j.neuroimage.2013.01.013

    View details for Web of Science ID 000316154400026

    View details for PubMedID 23333415

  • Compensatory Effort Parallels Midbrain Deactivation during Mental Fatigue: An fMRI Study PLOS ONE Nakagawa, S., Sugiura, M., Akitsuki, Y., Hosseini, S. M., Kotozaki, Y., Miyauchi, C. M., Yomogida, Y., Yokoyama, R., Takeuchi, H., Kawashima, R. 2013; 8 (2)

    Abstract

    Fatigue reflects the functioning of our physiological negative feedback system, which prevents us from overworking. When fatigued, however, we often try to suppress this system in an effort to compensate for the resulting deterioration in performance. Previous studies have suggested that the effect of fatigue on neurovascular demand may be influenced by this compensatory effort. The primary goal of the present study was to isolate the effect of compensatory effort on neurovascular demand. Healthy male volunteers participated in a series of visual and auditory divided attention tasks that steadily increased fatigue levels for 2 hours. Functional magnetic resonance imaging scans were performed during the first and last quarter of the study (Pre and Post sessions, respectively). Tasks with low and high attentional load (Low and High conditions, respectively) were administrated in alternating blocks. We assumed that compensatory effort would be greater under the High-attentional-load condition compared with the Low-load condition. The difference was assessed during the two sessions. The effect of compensatory effort on neurovascular demand was evaluated by examining the interaction between load (High vs. Low) and time (Pre vs. Post). Significant fatigue-induced deactivation (i.e., Pre>Post) was observed in the frontal, temporal, occipital, and parietal cortices, in the cerebellum, and in the midbrain in both the High and Low conditions. The interaction was significantly greater in the High than in the Low condition in the midbrain. Neither significant fatigue-induced activation (i.e., Pre[PreE- PostE]) may reflect suppression of the negative feedback system that normally triggers recuperative rest to maintain homeostasis.

    View details for DOI 10.1371/journal.pone.0056606

    View details for Web of Science ID 000315602700086

    View details for PubMedID 23457592

  • Multivariate Pattern Analysis of fMRI in Breast Cancer Survivors and Healthy Women Journal of the International Neuropsychological Society Hosseini, S., Kesler, S. 2013
  • Altered resting state functional brain network topology in chemotherapy-treated breast cancer survivors NEUROBIOLOGY OF DISEASE Bruno, J., Hosseini, S. M., Kesler, S. 2012; 48 (3): 329-338

    Abstract

    Many women with breast cancer, especially those treated with chemotherapy, experience cognitive decline due in part to neurotoxic brain injury. Recent neuroimaging studies suggest widespread brain structural abnormalities pointing to disruption of large-scale brain networks. We applied resting state functional magnetic resonance imaging and graph theoretical analysis to examine the connectome in breast cancer survivors treated with chemotherapy relative to healthy comparison women. Compared to healthy females, the breast cancer group displayed altered global brain network organization characterized by significantly decreased global clustering as well as disrupted regional network characteristics in frontal, striatal and temporal areas. Breast cancer survivors also showed significantly increased self-report of executive function and memory difficulties compared to healthy females. These results suggest that topological organization of both global and regional brain network properties may be disrupted following breast cancer and chemotherapy. This pattern of altered network organization is believed to result in reduced efficiency of parallel information transfer. This is the first report of alterations in large-scale functional brain networks in this population and contributes novel information regarding the neurobiologic mechanisms underlying breast cancer-related cognitive impairment.

    View details for DOI 10.1016/j.nbd.2012.07.009

    View details for Web of Science ID 000309694000007

    View details for PubMedID 22820143

  • GAT: A Graph-Theoretical Analysis Toolbox for Analyzing Between-Group Differences in Large-Scale Structural and Functional Brain Networks PLOS ONE Hosseini, S. M., Hoeft, F., Kesler, S. R. 2012; 7 (7)

    Abstract

    In recent years, graph theoretical analyses of neuroimaging data have increased our understanding of the organization of large-scale structural and functional brain networks. However, tools for pipeline application of graph theory for analyzing topology of brain networks is still lacking. In this report, we describe the development of a graph-analysis toolbox (GAT) that facilitates analysis and comparison of structural and functional network brain networks. GAT provides a graphical user interface (GUI) that facilitates construction and analysis of brain networks, comparison of regional and global topological properties between networks, analysis of network hub and modules, and analysis of resilience of the networks to random failure and targeted attacks. Area under a curve (AUC) and functional data analyses (FDA), in conjunction with permutation testing, is employed for testing the differences in network topologies; analyses that are less sensitive to the thresholding process. We demonstrated the capabilities of GAT by investigating the differences in the organization of regional gray-matter correlation networks in survivors of acute lymphoblastic leukemia (ALL) and healthy matched Controls (CON). The results revealed an alteration in small-world characteristics of the brain networks in the ALL survivors; an observation that confirm our hypothesis suggesting widespread neurobiological injury in ALL survivors. Along with demonstration of the capabilities of the GAT, this is the first report of altered large-scale structural brain networks in ALL survivors.

    View details for DOI 10.1371/journal.pone.0040709

    View details for Web of Science ID 000306406700047

    View details for PubMedID 22808240

  • Altered small-world properties of gray matter networks in breast cancer BMC NEUROLOGY Hosseini, S. M., Koovakkattu, D., Kesler, S. R. 2012; 12

    Abstract

    Breast cancer survivors, particularly those treated with chemotherapy, are at significantly increased risk for long-term cognitive and neurobiologic impairments. These deficits tend to involve skills that are subserved by distributed brain networks. Additionally, neuroimaging studies have shown a diffuse pattern of brain structure changes in chemotherapy-treated breast cancer survivors that might impact large-scale brain networks.We therefore applied graph theoretical analysis to compare the gray matter structural networks of female breast cancer survivors with a history of chemotherapy treatment and healthy age and education matched female controls.Results revealed reduced clustering coefficient and small-world index in the brain network of the breast cancer patients across a range of network densities. In addition, the network of the breast cancer group had less highly interactive nodes and reduced degree/centrality in the frontotemporal regions compared to controls, which may help explain the common impairments of memory and executive functioning among these patients.These results suggest that breast cancer and chemotherapy may decrease regional connectivity as well as global network organization and integration, reducing efficiency of the network. To our knowledge, this is the first report of altered large-scale brain networks associated with breast cancer and chemotherapy.

    View details for DOI 10.1186/1471-2377-12-28

    View details for Web of Science ID 000306755500001

    View details for PubMedID 22632066

  • Decoding what one likes or dislikes from single-trial fNIRS measurements NEUROREPORT Hosseini, S. M., Mano, Y., Rostami, M., Takahashi, M., Sugiura, M., Kawashima, R. 2011; 22 (6): 269-273

    Abstract

    Recent functional neuroimaging studies have shown the possibility of decoding human mental states from their brain activity using noninvasive neuroimaging techniques. In this study, we applied multivariate pattern classification, in conjunction with a short interval of functional near-infrared spectroscopy measurements of the anterior frontal cortex, to decode whether a human likes or dislikes a presented visual object; an ability that is quite beneficial for a number of clinical and technological applications. A variety of objects comprising sceneries, cars, foods, and animals were used as the stimuli. The results showed the possibility of predicting subjective preference from a short interval of functional near-infrared spectroscopy measurements of the anterior frontal regions. In addition, the pattern localization results showed the neuroscientific validity of the constructed classifier.

    View details for DOI 10.1097/WNR.0b013e3283451f8f

    View details for Web of Science ID 000288987800003

    View details for PubMedID 21372746

  • Changes in neural correlates of outcome feedback processing during implicit learning Open Neuroscience Journal Rostami M, Hosseini SMH, Takahashi M, Sugiura M, Kawashima R 2011; 5: 24-30
  • Aging and decision making under uncertainty: Behavioral and neural evidence for the preservation of decision making in the absence of learning in old age NEUROIMAGE Hosseini, S. M., Rostami, M., Yomogida, Y., Takahashi, M., Tsukiura, T., Kawashima, R. 2010; 52 (4): 1514-1520

    Abstract

    Decision making under uncertainty is an essential component of everyday life. Recent psychological studies suggest that older adults, despite age-related neurological decline, can make advantageous decisions when information about the contingencies of the outcomes is available. In this study, a two-choice prediction paradigm has been used, in conjunction with functional magnetic resonance imaging (fMRI), to investigate the effects of normal aging on neural substrates underlying uncertain decision making in the absence of learning that have not been addressed in previous neuroimaging studies. Neuroimaging results showed that both the healthy older and young adults recruited a network of brain regions comprising the right dorsolateral prefrontal cortex, bilateral inferior parietal lobule, medial frontal cortex, and right lateral orbitofrontal cortex during the prediction task. As was hypothesized, the performance of older adults in the prediction task was not impaired compared to young adults. Although no significant age-related increases in brain activity have been found, we observed an age-related decrease in activity in the right inferior parietal lobule. We speculate that the observed age-related decrease in parietal activity could be explained by age-related differences in decision making behavior revealed by questionnaire results and maximizing scores. Together, this study demonstrates behavioral and neural evidence for the preservation of decision making in older adults when information about the contingencies of the outcome is available.

    View details for DOI 10.1016/j.neuroimage.2010.05.008

    View details for Web of Science ID 000280695200039

    View details for PubMedID 20472072

  • Neural bases of goal-directed implicit learning NEUROIMAGE Rostami, M., Hosseini, S. M., Takahashi, M., Sugiura, M., Kawashima, R. 2009; 48 (1): 303-310

    Abstract

    Several neuropsychological and neuroimaging studies have been performed to clarify the neural bases of implicit learning, but the question of which brain regions are involved in different forms of implicit learning, including goal-directed learning and habit learning, has not yet been resolved. The present study sought to clarify the mechanisms of goal-directed implicit learning by examining the sugar production factory (SPF) task in conjunction with functional magnetic resonance imaging (fMRI). Several brain regions were identified that contribute to learning in the SPF task. Significant learning-related decreases in brain activity were found in the right inferior parietal lobule (IPL), left superior frontal gyrus, right medial frontal gyrus, cerebellar vermis, and left inferior frontal gyrus, while significant learning-related increases in activity were observed in the right inferior frontal gyrus, left precenteral gyrus and, left precuneus. Among these regions, we speculate that the IPL and medial frontal gyrus may specifically be involved in the early stage of goal-directed implicit learning. We also attempted to investigate the role of the striatum, which has a significant role in habit learning, during learning of the SPF task. The results of ROI analysis showed no learning-related change in the activity of the striatum. Although some of the observed learning-related activations in this study have also been previously reported in neuroimaging studies of habit learning, the possibility that specific brain regions involved in goal-direct implicit learning cannot be excluded.

    View details for DOI 10.1016/j.neuroimage.2009.06.007

    View details for Web of Science ID 000269321100033

    View details for PubMedID 19524051

  • Analyzing control display movement compatibility: A neuroimaging study LNCS Hosseini SMH, Rostami M, Takahashi M, Miura N, Sugiura M, Kawashima R 2009; 5639: 187-196
  • Combining static/dynamic fault trees and event trees using Bayesian networks LNCS Hosseini SMH, Takahashi M 2007; 4680: 93-99
  • Event tree analysis with dependent branches using Bayesian networks Proceedings of PSAM'08 Hosseini SMH, Takahashi M 2006
  • Dynamic Bayesian networks: Modeling problem Proceedings of PSAM'08 Hosseini SMH, Takahashi M 2006

Stanford Medicine Resources: