Four-way multimodal fusion of 7 T imaging data using an mCCA+jICA model in first-episode schizophrenia.
ABSTRACT: Acquisition of multimodal brain imaging data for the same subject has become more common leading to a growing interest in determining the intermodal relationships between imaging modalities to further elucidate the pathophysiology of schizophrenia. Multimodal data have previously been individually analyzed and subsequently integrated; however, these analysis techniques lack the ability to examine true modality inter-relationships. The utilization of a multiset canonical correlation and joint independent component analysis (mCCA + jICA) model for data fusion allows shared or distinct abnormalities between modalities to be examined. In this study, first-episode schizophrenia patients (nSZ =19) and matched controls (nHC =21) completed a resting-state functional magnetic resonance imaging (fMRI) scan at 7 T. Grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), and amplitude of low frequency fluctuation (ALFF) maps were used as features in a mCCA + jICA model. Results of the mCCA + jICA model indicated three joint group-discriminating components (GM-CSF, WM-ALFF, GM-ALFF) and two modality-unique group-discriminating components (GM, WM). The joint component findings are highlighted by GM basal ganglia, somatosensory, parietal lobe, and thalamus abnormalities associated with ventricular CSF volume; WM occipital and frontal lobe abnormalities associated with temporal lobe function; and GM frontal, temporal, parietal, and occipital lobe abnormalities associated with caudate function. These results support and extend major findings throughout the literature using independent single modality analyses. The multimodal fusion of 7 T data in this study provides a more comprehensive illustration of the relationships between underlying neuronal abnormalities associated with schizophrenia than examination of imaging data independently.
Project description:Highlights • Covariant abnormalities of GM and WM occurred in SCD and MCI.• GM-WM covariant abnormalities were correlated with cognitive performance in SCD.• Multimodal fusion highlighted the interaction of sMRI and DTI in SCD and MCI. <h4>Background</h4> Previous multimodal neuroimaging studies analyzed each dataset independently in subjective cognitive decline (SCD) and mild cognitive impairment (MCI), missing the cross-information. Multi-modal fusion analysis can provide more integral and comprehensive information regarding the brain. There has been a paucity of research on fusion analysis of sMRI and DTI in SCD and MCI. <h4>Materials and Methods</h4> In the present study, we conducted fusion analysis of structural MRI and DTI by applying multimodal canonical correlation analysis with joint independent component analysis (mCCA-jICA) to capture the cross-information of gray matter (GM) and white matter (WM) in 62 SCD patients, 99 MCI patients, and 70 healthy controls (HCs). We further analyzed correlations between the mixing coefficients of mCCA-jICA and neuropsychological scores among the three groups. <h4>Results</h4> A set of joint-discriminative independent components of GM and fractional anisotropy (FA) exhibited significant links between SCD and HCs, as well as between MCI and HCs. The covariant abnormalities primarily involved the frontal lobe/middle temporal gyrus/calcarine sulcus-anterior thalamic radiation/superior longitudinal fasciculus in SCD, and middle temporal gyrus/ fusiform gyrus/caudate necleus-forceps minor/anterior thalamic radiation in MCI. There was no significant difference between SCD and MCI groups. <h4>Conclusions</h4> The covariant GM-WM abnormalities in SCD and MCI were found in specific brain regions involved in cognitive processing, which confirms the simultaneous GM and WM changes underlying cognitive decline. These findings suggest that multimodal fusion analysis allows for a more comprehensive understanding of the association among different types of brain tissues and its crucial role in the neuropathological mechanism of SCD and MCI.
Project description: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.
Project description:<b>Background:</b> Antipsychotic medications provide limited long-term benefit to ~30% of schizophrenia patients. Multimodal magnetic resonance imaging (MRI) data have been used to investigate brain features between responders and nonresponders to antipsychotic treatment; however, these analytical techniques are unable to weigh the interrelationships between modalities. Here, we used multiset canonical correlation and joint independent component analysis (mCCA + jICA) to fuse MRI data to examine the shared and specific multimodal features between the patients and healthy controls (HCs) and between the responders and non-responders. <b>Method:</b> Resting-state functional and structural MRI data were collected from 55 patients with drug-naïve first-episode schizophrenia (FES) and demographically matched HCs. Based on the decrease in Positive and Negative Syndrome Scale scores from baseline to the 1-year follow-up, FES patients were divided into a responder group (RG) and a non-responder group (NRG). Gray matter volume (GMV), fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) maps were used as features in mCCA + jICA. <b>Results:</b> Between FES patients and HCs, there were three modality-specific discriminative independent components (ICs) showing the difference in mixing coefficients (GMV-IC7, GMV-IC8, and fALFF-IC5). The fusion analysis indicated one modality-shared IC (GMV-IC2 and ReHo-IC2) and three modality-specific ICs (GMV-IC1, GMV-IC3, and GMV-IC6) between the RG and NRG. The right postcentral gyrus showed a significant difference in GMV features between FES patients and HCs and modality-shared features (GMV and ReHo) between responders and nonresponders. The modality-shared component findings were highlighted by GMV, mainly in the bilateral temporal gyrus and the right cerebellum associated with ReHo in the right postcentral gyrus. <b>Conclusions:</b> This study suggests that joint anatomical and functional features of the cortices may reflect an early pathophysiological mechanism that is related to a 1-year treatment response.
Project description:Neurological soft signs (NSS) comprise a broad range of subtle neurological deficits and are considered to represent external markers of sensorimotor dysfunction frequently found in mental disorders of presumed neurodevelopmental origin. Although NSS frequently occur in schizophrenia spectrum disorders (SSD), specific patterns of co-altered brain structure and function underlying NSS in SSD have not been investigated so far. It is unclear whether gray matter volume (GMV) alterations or aberrant brain activity or a combination of both, are associated with NSS in SSD. Here, 37 right-handed SSD patients and 37 matched healthy controls underwent motor assessment and magnetic resonance imaging (MRI) at 3 T. NSS were examined on the Heidelberg NSS scale. We used a multivariate data fusion technique for multimodal MRI data-multiset canonical correlation and joint independent component analysis (mCCA + jICA)-to investigate co-altered patterns of GMV and intrinsic neural fluctuations (INF) in SSD patients exhibiting NSS. The mCCA?+?jICA model indicated two joint group-discriminating components (temporoparietal/cortical sensorimotor and frontocerebellar/frontoparietal networks) and one modality-specific group-discriminating component (p <?.05, FDR corrected). NSS motor score was associated with joint frontocerebellar/frontoparietal networks in SSD patients. This study highlights complex neural pathomechanisms underlying NSS in SSD suggesting aberrant structure and function, predominantly in cortical and cerebellar systems that critically subserve sensorimotor dynamics and psychomotor organization.
Project description:To evaluate the distribution of white matter (WM) disease in frontotemporal lobar degeneration (FTLD) and Alzheimer disease (AD) and to evaluate the relative usefulness of WM and gray matter (GM) for distinguishing these conditions in vivo.Patients were classified as having FTLD (n = 50) or AD (n = 42) using autopsy-validated CSF values of total-tau:?-amyloid (t-tau:A?(1-42)) ratios. Patients underwent WM diffusion tensor imaging (DTI) and volumetric MRI of GM. We employed tract-specific analyses of WM fractional anisotropy (FA) and whole-brain GM density analyses. Individual patient classification was performed using receiver operator characteristic (ROC) curves with FA, GM, and a combination of the 2 modalities.Regional FA and GM were significantly reduced in FTLD and AD relative to healthy seniors. Direct comparisons revealed significantly reduced FA in the corpus callosum in FTLD relative to AD. GM analyses revealed reductions in anterior temporal cortex for FTLD relative to AD, and in posterior cingulate and precuneus for AD relative to FTLD. ROC curves revealed that a multimodal combination of WM and GM provide optimal classification (area under the curve = 0.938), with 87% sensitivity and 83% specificity.FTLD and AD have significant WM and GM defects. A combination of DTI and volumetric MRI modalities provides a quantitative method for distinguishing FTLD and AD in vivo.
Project description:This study aimed to investigate abnormalities in the gray matter and white matter (GM and WM, respectively) that are shared between schizophrenia (SZ) and bipolar disorder (BD). We used 3T-magnetic resonance imaging to examine patients with SZ, BD, or healthy control (HC) subjects (aged 20-50 years, N = 65 in each group). We generated modulated GM maps through voxel-based morphometry (VBM) for T1-weighted images and skeletonized fractional anisotropy, mean diffusion, and radial diffusivity maps through tract-based special statistics (TBSS) methods for diffusion tensor imaging (DTI) data. These data were analyzed using a generalized linear model with pairwise comparisons between groups with a family-wise error corrected P < 0.017. The VBM analysis revealed widespread decreases in GM volume in SZ compared to HC, but patients with BD showed GM volume deficits limited to the right thalamus and left insular lobe. The TBSS analysis showed alterations of DTI parameters in widespread WM tracts both in SZ and BD patients compared to HC. The two disorders had WM alterations in the corpus callosum, superior longitudinal fasciculus, internal capsule, external capsule, posterior thalamic radiation, and fornix. However, we observed no differences in GM volume or WM integrity between SZ and BD. The study results suggest that GM volume deficits in the thalamus and insular lobe along with widespread disruptions of WM integrity might be the common neural mechanisms underlying the pathologies of SZ and BD.
Project description:<h4>Objective</h4>To characterize and follow the diffuse gray and white matter (GM/WM) metabolic abnormalities in early relapsing-remitting multiple sclerosis using proton magnetic resonance spectroscopic imaging ((1)H-MRSI).<h4>Methods</h4>Eighteen recently diagnosed, mildly disabled patients (mean baseline time from diagnosis 32 months, mean Expanded Disability Status Scale [EDSS] score 1.3), all on immunomodulatory medication, were scanned semiannually for 3 years with T1-weighted and T2-weighted MRI and 3D (1)H-MRSI at 3 T. Ten sex- and age-matched controls were followed annually. Global absolute concentrations of N-acetylaspartate (NAA), choline (Cho), creatine (Cr), and myo-inositol (mI) were obtained for all GM and WM in the 360 cm(3) (1)H-MRSI volume of interest.<h4>Results</h4>Patients' average WM Cr, Cho, and mI concentrations (over all time points), 5.3 ± 0.4, 1.6 ± 0.1, and 5.1 ± 0.7 mM, were 8%, 12%, and 11% higher than controls' (p ≤ 0.01), while their WM NAA, 7.4 ± 0.7 mM, was 6% lower (p = 0.07). There were increases with time of patients' WM Cr: 0.1 mM/year, Cho: 0.02 mM/year, and NAA: 0.1 mM/year (all p < 0.05). None of the patients' metabolic concentrations correlated with their EDSS score, relapse rate, GM/WM/CSF fractions, or lesion volume.<h4>Conclusions</h4>Diffuse WM glial abnormalities were larger in magnitude than the axonal abnormalities and increased over time independently of conventional clinical or imaging metrics and despite immunomodulatory treatment. In contrast, the axonal abnormalities showed partial recovery, suggesting that patients' lower WM NAA levels represented a dysfunction, which may abate with treatment. Absence of detectable diffuse changes in GM suggests that injury there is minimal, focal, or heterogeneous between cortex and deep GM nuclei.
Project description:The aims of this study were to determine which cognitive control functions are most sensitive to cross-sectional age differences and to identify neural features in different neuroimaging modalities that associated cognitive control function across the adult lifespan. We employed a joint independent component analysis (jICA) approach to obtain common networks among three different brain-imaging modalities (i.e., structural MRI, resting-state functional MRI, and diffusion tensor imaging) in relation to the cognitive control function. We differentiated three distinct cognitive constructs: one common (across inhibition, shifting, and updating) and two specific (shifting, updating) factors. These common/specific constructs were transformed from three original performance indexes: (a) stop-signal reaction time, (b) switch-cost, and (c) performance sensitivity collected from 156 individuals aged 20 to 78 years old. The current results show that the cross-sectional age difference is associated with a wide spread of brain degeneration that is not limited to the frontal region. Crucially, these findings suggest there are some common and distinct joined multimodal components that correlate with the psychological constructs of common and discrete cognitive control functions, respectively. To support current findings, other fusion ICA models were also analyzed including, parallel ICA (para-ICA) and multiset canonical correlation analysis with jICA (mCCA + jICA). Dynamic interactions among these brain features across different brain modalities could serve as possible developmental mechanisms associated with these age effects.
Project description:<h4>Objective</h4>To use multimodal neuroimaging to evaluate the influence of heterogeneous underlying pathology in corticobasal syndrome (CBS) on the neuroanatomical distribution of disease.<h4>Methods</h4>We performed a retrospective evaluation of 35 patients with CBS with T1-weighted MRI, diffusion tensor imaging, and neuropathologic, genetic, or CSF evidence of underlying pathology. Patients were assigned to 2 groups: those with evidence of Alzheimer pathology (CBS-AD) and those without Alzheimer pathology (CBS-non-AD). Group comparisons of CBS-AD and CBS-non-AD assessed clinical features, gray matter (GM) cortical thickness, and white matter (WM) fractional anisotropy.<h4>Results</h4>CBS-AD was found in 34% (n = 12) and CBS-non-AD in 66% (n = 23) of CBS patients. Clinical evaluations revealed that CBS-non-AD had a higher frequency of asymmetric rigidity compared to CBS-AD, but groups otherwise did not differ in dementia severity, impairments in cognition, or rates of extrapyramidal symptoms. We found frontoparietal GM and WM disease in each group compared to healthy, demographically comparable controls, as well as multimodal neuroimaging evidence of a double dissociation: CBS-non-AD had WM disease in the corpus callosum, corticospinal tract, and superior longitudinal fasciculus relative to CBS-AD, and CBS-AD had reduced temporoparietal GM relative to CBS-non-AD, including the precuneus and posterior cingulate.<h4>Conclusions</h4>Patients with CBS have a pathology-mediated dissociation of GM and WM disease. Multimodality neuroimaging may be useful for improving in vivo pathologic diagnosis of CBS.
Project description:<h4>Objective</h4>Investigate global and regional grey and white matter volumes in patients with Chronic Fatigue Syndrome (CFS) using magnetic resonance imaging (MRI) and recent voxel-based morphometry (VBM) methods.<h4>Methods</h4>Forty-two patients with CFS and thirty healthy volunteers were scanned on a 3-Tesla MRI scanner. Anatomical MRI scans were segmented, normalized and submitted to a VBM analysis using randomisation methods. Group differences were identified in overall segment volumes and voxel-wise in spatially normalized grey matter (GM) and white matter (WM) segments.<h4>Results</h4>Accounting for total intracranial volume, patients had larger GM volume and lower WM volume. The voxel-wise analysis showed increased GM volume in several structures including the amygdala and insula in the patient group. Reductions in WM volume in the patient group were seen primarily in the midbrain, pons and right temporal lobe.<h4>Conclusion</h4>Elevated GM volume in CFS is seen in areas related to processing of interoceptive signals and stress. Reduced WM volume in the patient group partially supports earlier findings of WM abnormalities in regions of the midbrain and brainstem.