http://repub.eur.nl/res/aut/22381/ List of Publications en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl http://repub.eur.nl/res/pub/38376/ 2013-02-01T00:00:00Z Multimodal fusion is an effective approach to better understand brain diseases. However, most such instances have been limited to pair-wise fusion; because there are often more than two imaging modalities available per subject, there is a need for approaches that can combine multiple datasets optimally. In this paper, we extended our previous two-way fusion model called "multimodal CCA + joint ICA", to three or N-way fusion, that enables robust identification of correspondence among N data types and allows one to investigate the important question of whether certain disease risk factors are shared or distinct across multiple modalities. We compared "mCCA + jICA" with its alternatives in a 3-way fusion simulation and verified its advantages in both decomposition accuracy and modal linkage detection. We also applied it to real functional Magnetic Resonance Imaging (fMRI)-Diffusion Tensor Imaging (DTI) and structural MRI fusion to elucidate the abnormal architecture underlying schizophrenia (n = 97) relative to healthy controls (n = 116). Both modality-common and modality-unique abnormal regions were identified in schizophrenia. Specifically, the visual cortex in fMRI, the anterior thalamic radiation (ATR) and forceps minor in DTI, and the parietal lobule, cuneus and thalamus in sMRI were linked and discriminated between patients and controls. One fMRI component with regions of activity in motor cortex and superior temporal gyrus individually discriminated schizophrenia from controls. Finally, three components showed significant correlation with duration of illness (DOI), suggesting that lower gray matter volumes in parietal, frontal, and temporal lobes and cerebellum are associated with increased DOI, along with white matter disruption in ATR and cortico-spinal tracts. Findings suggest that the identified fractional anisotropy changes may relate to the corresponding functional/structural changes in the brain that are thought to play a role in the clinical expression of schizophrenia. The proposed "mCCA + jICA" method showed promise for elucidating the joint or coupled neuronal abnormalities underlying mental illnesses and improves our understanding of the disease process. http://repub.eur.nl/res/pub/33784/ 2011-03-30T00:00:00Z Background: The effect of antipsychotics on the blood oxygen level dependent signal in schizophrenia is poorly understood. The purpose of the present investigation is to examine the effect of antipsychotic medication on independent neural networks during a motor task in a large, multi-site functional magnetic resonance imaging investigation. Methods: Seventy-nine medicated patients with schizophrenia and 114 comparison subjects from the Mind Clinical Imaging Consortium database completed a paced, auditory motor task during functional magnetic resonance imaging (fMRI). Independent component analysis identified temporally cohesive but spatially distributed neural networks. The independent component analysis time course was regressed with a model time course of the experimental design. The resulting beta weights were evaluated for group comparisons and correlations with chlorpromazine equivalents. Results: Group differences between patients and comparison subjects were evident in the cortical and subcortical motor networks, default mode networks, and attentional networks. The chlorpromazine equivalents correlated with the unimotor/bitemporal (rho=-0.32, P=0.0039), motor/caudate (rho=-0.22, P=0.046), posterior default mode (rho=0.26, P=0.020), and anterior default mode networks (rho=0.24, P=0.03). Patients on typical antipsychotics also had less positive modulation of the motor/caudate network relative to patients on atypical antipsychotics (t77=2.01, P=0.048). Conclusion: The results suggest that antipsychotic dose diminishes neural activation in motor (cortical and subcortical) and default mode networks in patients with schizophrenia. The higher potency, typical antipsychotics also diminish positive modulation in subcortical motor networks. Antipsychotics may be a potential confound limiting interpretation of fMRI studies on the disease process in medicated patients with schizophrenia. http://repub.eur.nl/res/pub/23038/ 2011-03-01T00:00:00Z Children and adolescents who develop schizophrenia tend to have greater symptom severity than adults who develop the illness. Since the brain continues to mature into early adulthood, developmental differences in brain structure and function may provide clues to the underlying neurobiology of schizophrenia. With an emerging body of evidence supporting disrupted connectivity contributing to the underlying pathophysiology of schizophrenia, it was our goal to assess differences in functional connectivity in children and adolescents who develop schizophrenia. Participants included a total of 28 children and adolescents (14 patients with schizophrenia and 14 age- and gender-matched controls). All subjects underwent a functional magnetic resonance imaging scan involving a modified Sternberg Item Recognition Paradigm with 3 working memory (WkM) loads. Patients had poorer performance at all 3 WkM loads without a load by diagnosis interaction. Functional imaging results demonstrated 3 specific brain networks disrupted in children and adolescents with schizophrenia. These networks include 1) the anterior cingulate and the temporal lobes, bilaterally; 2) the cerebellum with subcortical regions; and 3) the occipital lobe and the cerebellum. Patients with early-onset schizophrenia demonstrate abnormal functional connectivity in networks involving limbic, temporal lobe, cerebellum, and early visual processing streams. http://repub.eur.nl/res/pub/19205/ 2010-05-01T00:00:00Z Collection of multiple-task brain imaging data from the same subject has now become common practice in medical imaging studies. In this paper, we propose a simple yet effective model, "CCA + ICA", as a powerful tool for multi-task data fusion. This joint blind source separation (BSS) model takes advantage of two multivariate methods: canonical correlation analysis and independent component analysis, to achieve both high estimation accuracy and to provide the correct connection between two datasets in which sources can have either common or distinct between-dataset correlation. In both simulated and real fMRI applications, we compare the proposed scheme with other joint BSS models and examine the different modeling assumptions. The contrast images of two tasks: sensorimotor (SM) and Sternberg working memory (SB), derived from a general linear model (GLM), were chosen to contribute real multi-task fMRI data, both of which were collected from 50 schizophrenia patients and 50 healthy controls. When examining the relationship with duration of illness, CCA + ICA revealed a significant negative correlation with temporal lobe activation. Furthermore, CCA + ICA located sensorimotor cortex as the group-discriminative regions for both tasks and identified the superior temporal gyrus in SM and prefrontal cortex in SB as task-specific group-discriminative brain networks. In summary, we compared the new approach to some competitive methods with different assumptions, and found consistent results regarding each of their hypotheses on connecting the two tasks. Such an approach fills a gap in existing multivariate methods for identifying biomarkers from brain imaging data.