Hadi Hosseini is a computational/cognitive neuroscientist investigating large-scale structural and functional brain networks in various neuropsychiatric disorders using multimodal neuroimaging, graph theoretical and multivariate pattern analyses techniques. He is also developing novel NIRS-based neurofeedback interventions for enhancing executive functions. Dr. Hosseini has been co-teaching the Neuroimaging Research Methods (Psyc250) at Stanford since 2012.

Academic Appointments

Honors & Awards

  • Career Development Award (K25), NIA (2016-2021)
  • NARSAD Young Investigator Award, Brain & Behavior Research Foundation (2016-2018)
  • CHRI Pilot Early Career Award, Lucile Packard Foundation for Children?s Health (2015-2016)
  • ICGP Junior investigator Award, International College of Geriatric Psychoneuropharmacology (ICGP) (Aug 2015)
  • CNI Innovation Grant, Stanford Center for Cognitive and Neurobiological Imaging (2015)
  • Best Presentation Award, APRC-IBRO School of Neuroimaging (Nov 2010)
  • SAND Travel award, SAND5 Conference (May 2010)
  • MEXT Full Scholarship for PhD Program, Tohoku University (2005-2008)

Research & Scholarship

Current Research and Scholarly Interests

Our lab?s research portfolio crosses multiple disciplines including computational neuropsychiatry, cognitive neuroscience, multimodal neuroimaging and neurocognitive rehabilitation. Our computational neuropsychiatry research mainly involves investigating alterations in the organization of connectome in various neurodevelopmental and neurocognitive disorders using state of the art neuroimaging techniques (fMRI, sMRI, DWI, functional NIRS) combined with novel computational methods (graph theoretical and multivariate pattern analyses).

The ultimate goal of our research is to translate the findings from computational neuropsychiatry research toward developing personalized interventions. We have been developing personalized interventions that integrate computerized cognitive rehabilitation, real-time functional brain imaging and neurofeedback, as well as virtual reality (VR) tailored toward targeted rehabilitation of the affected brain networks in patients with neurocognitive disorders.


2016-17 Courses


All Publications

  • Altered Integration of Structural Covariance Networks in Young Children With Type 1 Diabetes. Human brain mapping Hosseini, S. M., Mazaika, P., Mauras, N., Buckingham, B., Weinzimer, S. A., Tsalikian, E., White, N. H., Reiss, A. L. 2016; 37 (11): 4034-4046


    Type 1 diabetes mellitus (T1D), one of the most frequent chronic diseases in children, is associated with glucose dysregulation that contributes to an increased risk for neurocognitive deficits. While there is a bulk of evidence regarding neurocognitive deficits in adults with T1D, little is known about how early-onset T1D affects neural networks in young children. Recent data demonstrated widespread alterations in regional gray matter and white matter associated with T1D in young children. These widespread neuroanatomical changes might impact the organization of large-scale brain networks. In the present study, we applied graph-theoretical analysis to test whether the organization of structural covariance networks in the brain for a cohort of young children with T1D (N?=?141) is altered compared to healthy controls (HC; N?=?69). While the networks in both groups followed a small world organization-an architecture that is simultaneously highly segregated and integrated-the T1D network showed significantly longer path length compared with HC, suggesting reduced global integration of brain networks in young children with T1D. In addition, network robustness analysis revealed that the T1D network model showed more vulnerability to neural insult compared with HC. These results suggest that early-onset T1D negatively impacts the global organization of structural covariance networks and influences the trajectory of brain development in childhood. This is the first study to examine structural covariance networks in young children with T1D. Improving glycemic control for young children with T1D might help prevent alterations in brain networks in this population. Hum Brain Mapp, 2016. © 2016 Wiley Periodicals, Inc.

    View details for DOI 10.1002/hbm.23293

    View details for PubMedID 27339089

  • Sex differences in neural and behavioral signatures of cooperation revealed by fNIRS hyperscanning SCIENTIFIC REPORTS Baker, J. M., Liu, N., Cui, X., Vrticka, P., Saggar, M., Hosseini, S. M., Reiss, A. L. 2016; 6


    Researchers from multiple fields have sought to understand how sex moderates human social behavior. While over 50 years of research has revealed differences in cooperation behavior of males and females, the underlying neural correlates of these sex differences have not been explained. A missing and fundamental element of this puzzle is an understanding of how the sex composition of an interacting dyad influences the brain and behavior during cooperation. Using fNIRS-based hyperscanning in 111 same- and mixed-sex dyads, we identified significant behavioral and neural sex-related differences in association with a computer-based cooperation task. Dyads containing at least one male demonstrated significantly higher behavioral performance than female/female dyads. Individual males and females showed significant activation in the right frontopolar and right inferior prefrontal cortices, although this activation was greater in females compared to males. Female/female dyad's exhibited significant inter-brain coherence within the right temporal cortex, while significant coherence in male/male dyads occurred in the right inferior prefrontal cortex. Significant coherence was not observed in mixed-sex dyads. Finally, for same-sex dyads only, task-related inter-brain coherence was positively correlated with cooperation task performance. Our results highlight multiple important and previously undetected influences of sex on concurrent neural and behavioral signatures of cooperation.

    View details for DOI 10.1038/srep26492

    View details for Web of Science ID 000377330900001

    View details for PubMedID 27270754

  • Altered Brain Network Segregation in Fragile X Syndrome Revealed by Structural Connectomics. Cerebral cortex (New York, N.Y. : 1991) Bruno, J. L., Hosseini, S. M., Saggar, M., Quintin, E. M., Raman, M. M., Reiss, A. L. 2016


    Fragile X syndrome (FXS), the most common inherited cause of intellectual disability and autism spectrum disorder, is associated with significant behavioral, social, and neurocognitive deficits. Understanding structural brain network topology in FXS provides an important link between neurobiological and behavioral/cognitive symptoms of this disorder. We investigated the connectome via whole-brain structural networks created from group-level morphological correlations. Participants included 100 individuals: 50 with FXS and 50 with typical development, age 11-23 years. Results indicated alterations in topological properties of structural brain networks in individuals with FXS. Significantly reduced small-world index indicates a shift in the balance between network segregation and integration and significantly reduced clustering coefficient suggests that reduced local segregation shifted this balance. Caudate and amygdala were less interactive in the FXS network further highlighting the importance of subcortical region alterations in the neurobiological signature of FXS. Modularity analysis indicates that FXS and typically developing groups' networks decompose into different sets of interconnected sub networks, potentially indicative of aberrant local interconnectivity in individuals with FXS. These findings advance our understanding of the effects of fragile X mental retardation protein on large-scale brain networks and could be used to develop a connectome-level biological signature for FXS.

    View details for DOI 10.1093/cercor/bhw055

    View details for PubMedID 27009247

  • Task-based neurofeedback training: A novel approach toward training executive functions. NeuroImage Hosseini, S. M., Pritchard-Berman, M., Sosa, N., Ceja, A., Kesler, S. R. 2016; 134: 153-9


    Cognitive training is an emergent approach to improve cognitive functions in various neurodevelopmental and neurodegenerative diseases. However, current training programs can be relatively lengthy, making adherence potentially difficult for patients with cognitive difficulties. Previous studies suggest that providing individuals with real-time feedback about the level of brain activity (neurofeedback) can potentially help them learn to control the activation of specific brain regions. In the present study, we developed a novel task-based neurofeedback training paradigm that benefits from the effects of neurofeedback in parallel with computerized training. We focused on executive function training given its core involvement in various developmental and neurodegenerative diseases. Near-infrared spectroscopy (NIRS) was employed for providing neurofeedback by measuring changes in oxygenated hemoglobin in the prefrontal cortex. Of the twenty healthy adult participants, ten received real neurofeedback (NFB) on prefrontal activity during cognitive training, and ten were presented with sham feedback (SHAM). Compared with SHAM, the NFB group showed significantly improved executive function performance including measures of working memory after four sessions of training (100min total). The NFB group also showed significantly reduced training-related brain activity in the executive function network including right middle frontal and inferior frontal regions compared with SHAM. Our data suggest that providing neurofeedback along with cognitive training can enhance executive function after a relatively short period of training. Similar designs could potentially be used for patient populations with known neuropathology, potentially helping them to boost/recover the activity in the affected brain regions.

    View details for DOI 10.1016/j.neuroimage.2016.03.035

    View details for PubMedID 27015711

  • Neural correlates of cognitive intervention in persons at risk of developing Alzheimer's disease FRONTIERS IN AGING NEUROSCIENCE Hosseini, S. M., Kramer, J. H., Kesler, S. R. 2014; 6


    Cognitive training is an emergent approach that has begun to receive increased attention in recent years as a non-pharmacological, cost-effective intervention for Alzheimer's disease (AD). There has been increasing behavioral evidence regarding training-related improvement in cognitive performance in early stages of AD. Although these studies provide important insight about the efficacy of cognitive training, neuroimaging studies are crucial to pinpoint changes in brain structure and function associated with training and to examine their overlap with pathology in AD. In this study, we reviewed the existing neuroimaging studies on cognitive training in persons at risk of developing AD to provide an overview of the overlap between neural networks rehabilitated by the current training methods and those affected in AD. The data suggest a consistent training-related increase in brain activity in medial temporal, prefrontal, and posterior default mode networks, as well as increase in gray matter structure in frontoparietal and entorhinal regions. This pattern differs from the observed pattern in healthy older adults that shows a combination of increased and decreased activity in response to training. Detailed investigation of the data suggests that training in persons at risk of developing AD mainly improves compensatory mechanisms and partly restores the affected functions. While current neuroimaging studies are quite helpful in identifying the mechanisms underlying cognitive training, the data calls for future multi-modal neuroimaging studies with focus on multi-domain cognitive training, network level connectivity, and individual differences in response to training.

    View details for DOI 10.3389/fnagi.2014.00231

    View details for Web of Science ID 000340934900001

    View details for PubMedID 25206335

  • Multivariate pattern analysis of FMRI in breast cancer survivors and healthy women. Journal of the International Neuropsychological Society Hosseini, S. M., Kesler, S. R. 2014; 20 (4): 391-401


    Advances in breast cancer (BC) treatments have resulted in significantly improved survival rates. However, BC chemotherapy is often associated with several side effects including cognitive dysfunction. We applied multivariate pattern analysis (MVPA) to functional magnetic resonance imaging (fMRI) to find a brain connectivity pattern that accurately and automatically distinguishes chemotherapy-treated (C+) from non-chemotherapy treated (C-) BC females and healthy female controls (HC). Twenty-seven C+, 29 C-, and 30 HC underwent fMRI during an executive-prefrontal task (Go/Nogo). The pattern of functional connectivity associated with this task discriminated with significant accuracy between C+ and HC groups (72%, p = .006) and between C+ and C- groups (71%, p = .012). However, the accuracy of discrimination between C- and HC was not significant (51%, p = .46). Compared with HC, behavioral performance of the C+ and C- groups during the task was intact. However, the C+ group demonstrated altered functional connectivity in the right frontoparietal and left supplementary motor area networks compared to HC, and in the right middle frontal and left superior frontal gyri networks, compared to C-. Our results provide further evidence that executive function performance may be preserved in some chemotherapy-treated BC survivors through recruitment of additional neural connections. (JINS, 2013, 19, 1-11).

    View details for DOI 10.1017/S1355617713001173

    View details for PubMedID 24135221

  • 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


    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


    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

  • 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


    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

  • 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


    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

  • 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


    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)


    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


    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


    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

  • 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


    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


    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

  • Neural signature of developmental coordination disorder in the structural connectome independent of comorbid autism DEVELOPMENTAL SCIENCE Caeyenberghs, K., Taymans, T., Wilson, P. H., Vanderstraeten, G., Hosseini, H., Van Waelvelde, H. 2016; 19 (4): 599-612


    Children with autism spectrum disorders (ASD) often exhibit motor clumsiness (Developmental Coordination Disorder, DCD), i.e. they struggle with everyday tasks that require motor coordination like dressing, self-care, and participating in sport and leisure activities. Previous studies in these neurodevelopmental disorders have demonstrated functional abnormalities and alterations of white matter microstructural integrity in specific brain regions. These findings suggest that the global organization of brain networks is affected in DCD and ASD and support the hypothesis of a 'dys-connectivity syndrome' from a network perspective. No studies have compared the structural covariance networks between ASD and DCD in order to look for the signature of DCD independent of comorbid autism. Here, we aimed to address the question of whether abnormal connectivity in DCD overlaps that seen in autism or comorbid DCD-autism. Using graph theoretical analysis, we investigated differences in global and regional topological properties of structural brain networks in 53 children: 8 ASD children with DCD (DCD+ASD), 15 ASD children without DCD (ASD), 11 with DCD only, and 19 typically developing (TD) children. We constructed separate structural correlation networks based on cortical thickness derived from Freesurfer. The children were assessed on the Movement-ABC and the Beery Test of Visual Motor Integration. Behavioral results demonstrated that the DCD group and DCD+ASD group scored on average poorer than the TD and ASD groups on various motor measures. Furthermore, although the brain networks of all groups exhibited small-world properties, the topological architecture of the networks was significantly altered in children with ASD compared with DCD and TD. ASD children showed increased normalized path length and higher values of clustering coefficient. Also, paralimbic regions exhibited nodal clustering coefficient alterations in singular disorders. These changes were disorder-specific, and included alterations in clustering coefficient in the isthmus of the right cingulate gyrus and the pars orbitalis of the right inferior frontal gyrus in ASD children, and DCD-related increases in the lateral orbitofrontal cortex. Children meeting criteria for both DCD and ASD exhibited topological changes that were more widespread from those seen in children with only DCD, i.e. children with DCD+ASD showed alterations of clustering coefficient in (para)limbic regions, primary areas, and association areas. The DCD+ASD group showed changes in clustering coefficient in the left association cortex relative to the ASD group. Finally, the DCD+ASD group shared ASD-specific abnormalities in the pars orbitalis of right inferior frontal gyrus, which was hypothesized to reflect atypical emotional-cognitive processing. Our results provide evidence that DCD and ASD are neurodevelopmental disorders with a low degree of overlap in abnormalities in connectivity. The co-occurrence of DCD+ASD was also associated with a distinct topological pattern, highlighting the unique neural signature of comorbid neurodevelopmental disorders.

    View details for DOI 10.1111/desc.12424

    View details for Web of Science ID 000379952100006

    View details for PubMedID 27147441

  • Structural brain connectivity and the sit-to-stand-to-sit performance in individuals with non-specific low back pain: A diffusion MRI based network analysis Brain Connectivity Pijnenburg, M., Hosseini, S., Brumagne, S., Janssens, L., Goossens, N., Caeyenberghs, K. 2016: In Press
  • Estimating individual contribution from group-based structural correlation networks NEUROIMAGE Saggar, M., Hosseini, S. M., Bruno, J. L., Quintin, E., Raman, M. M., Kesler, S. R., Reiss, A. L. 2015; 120: 274-284


    Coordinated variations in brain morphology (e.g., cortical thickness) across individuals have been widely used to infer large-scale population brain networks. These structural correlation networks (SCNs) have been shown to reflect synchronized maturational changes in connected brain regions. Further, evidence suggests that SCNs, to some extent, reflect both anatomical and functional connectivity and hence provide a complementary measure of brain connectivity in addition to diffusion weighted networks and resting-state functional networks. Although widely used to study between-group differences in network properties, SCNs are inferred only at the group-level using brain morphology data from a set of participants, thereby not providing any knowledge regarding how the observed differences in SCNs are associated with individual behavioral, cognitive and disorder states. In the present study, we introduce two novel distance-based approaches to extract information regarding individual differences from the group-level SCNs. We applied the proposed approaches to a moderately large dataset (n=100) consisting of individuals with fragile X syndrome (FXS; n=50) and age-matched typically developing individuals (TD; n=50). We tested the stability of proposed approaches using permutation analysis. Lastly, to test the efficacy of our method, individual contributions extracted from the group-level SCNs were examined for associations with intelligence scores and genetic data. The extracted individual contributions were stable and were significantly related to both genetic and intelligence estimates, in both typically developing individuals and participants with FXS. We anticipate that the approaches developed in this work could be used as a putative biomarker for altered connectivity in individuals with neurodevelopmental disorders.

    View details for DOI 10.1016/j.neuroimage.2015.07.006

    View details for Web of Science ID 000362025000024

  • Dynamics of the connectome in Huntington's disease: A longitudinal diffusion MRI study NEUROIMAGE-CLINICAL Odish, O. F., Caeyenberghs, K., Hosseini, H., van den Bogaard, S. J., Roos, R. A., Leemans, A. 2015; 9: 32-43


    To longitudinally investigate the connectome in different stages of Huntington's disease (HD) by applying graph theoretical analysis to diffusion MRI data.We constructed weighted structural networks and calculated their topological properties. Twenty-two premanifest (preHD), 10 early manifest HD and 24 healthy controls completed baseline and 2 year follow-up scans. We stratified the preHD group based on their predicted years to disease onset into a far (preHD-A) and near (preHD-B) to disease onset group. We collected clinical and behavioural measures per assessment time point.We found a significant reduction over time in nodal betweenness centrality both in the early manifest HD and preHD-B groups as compared to the preHD-A and control groups, suggesting a decrease of importance of specific nodes to overall network organization in these groups (FDR adjusted ps < 0.05). Additionally, we found a significant longitudinal decrease of the clustering coefficient in preHD when compared to healthy controls (FDR adjusted p < 0.05), which can be interpreted as a reduced capacity for internodal information processing at the local level. Furthermore, we demonstrated dynamic changes to hub-status loss and gain both in preHD and early manifest HD. Finally, we found significant cross-sectional as well as longitudinal relationships between graph metrics and clinical and neurocognitive measures.This study demonstrates divergent longitudinal changes to the connectome in (pre) HD compared to healthy controls. This provides novel insights into structural correlates associated with clinical and cognitive functions in HD and possible compensatory mechanisms at play in preHD.

    View details for DOI 10.1016/j.nicl.2015.07.003

    View details for Web of Science ID 000373188400005

    View details for PubMedID 26288754

  • Altered Resting State Functional Connectivity in Young Survivors of Acute Lymphoblastic Leukemia PEDIATRIC BLOOD & CANCER Kesler, S. R., Gugel, M., Pritchard-Berman, M., Lee, C., Kutner, E., Hosseini, S. M., Dahl, G., Lacayo, N. 2014; 61 (7): 1295-1299


    Chemotherapy treatment for pediatric acute lymphoblastic leukemia (ALL) has been associated with long-term cognitive impairments in some patients. However, the neurobiologic mechanisms underlying these impairments, particularly in young survivors, are not well understood. This study aimed to examine intrinsic functional brain connectivity in pediatric ALL and its relationship with cognitive status.We obtained resting state functional magnetic resonance imaging (rsfMRI) and cognitive testing data from 15 ALL survivors age 8-15 years and 14 matched healthy children. The ALL group had a history of intrathecal chemotherapy treatment but were off-therapy for at least 6 months at the time of enrollment. We used seed-based analyses to compare intrinsic functional brain network connectivity between the groups. We also explored correlations between connectivity and cognitive performance, demographic, medical, and treatment variables.We demonstrated significantly reduced connectivity between bilateral hippocampus, left inferior occipital, left lingual gyrus, bilateral calcarine sulcus, and right amygdala in the ALL group compared to controls. The ALL group also showed regions of functional hyperconnectivity including right lingual gyrus, precuneus, bilateral superior occipital lobe, and right inferior occipital lobe. Functional hypoconnectivity was associated with reduced cognitive function as well as younger age at diagnosis in the ALL group.This is the first study to demonstrate that intrinsic functional brain connectivity is disrupted in pediatric ALL following chemotherapy treatment. These results help explain cognitive dysfunction even when objective test performance is seemingly normal. Children diagnosed at a younger age may show increased vulnerability to altered functional brain connectivity.

    View details for DOI 10.1002/pbc.24976

    View details for Web of Science ID 000335198400031

    View details for PubMedID 24619953

  • 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


    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

  • 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)


    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

  • 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)


    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

  • 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
  • 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

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