Personode: A Toolbox for ICA Map Classification and Individualized ROI Definition

fMRI-based data-driven brain parcellation using independent component analysis

BACKGROUND

Studies using functional magnetic resonance imaging (fMRI) broadly require a method of parcellating the brain into regions of interest (ROIs). Parcellations can be based on standardized brain anatomy, such as the Montreal Neurological Institute's (MNI) 152 atlas, or an individual's functional activity patterns, such as the Personode software.

NEW METHOD

This work outlines and tests the independent component analysis (ICA)-based parcellation algorithm (IPA) when applied to a hypertension study (n = 48) that uses the independent components (ICs) output from group ICA (gICA) to build ROIs which are ideally spatially consistent and functionally homogeneous. After regression of ICs to all subjects, the IPA builds individualized parcellations while simultaneously obtaining a gICA-derived parcellation.

RESULTS

ROI spatial consistency quantified by dice similarity coefficients (DSCs) show individualized parcellations exhibit mean DSCs of 0.69 ± 0.14. Functional homogeneity, calculated as mean Pearson correlation value of all voxels comprising a ROI, shows individualized parcellations with a mean of 0.30 ± 0.14 and gICA-derived parcellations' mean of 0.38 ± 0.15.

COMPARISON WITH EXISTING METHOD(S)

Individualized Personode parcellations show decreased mean DSCs (0.43 ± 0.11) with the individualized parcellations, gICA-derived parcellations, and the MNI atlas having decreased homogeneity values of 0.28 ± 0.14, 0.31 ± 0.15, and 0.20 ± 0.11 respectively.

CONCLUSIONS

Results show that the IPA can more reliably define a ROI and does so with higher functional homogeneity. Given these findings, the IPA shows promise as a novel parcellation technique that could aid the analysis of fMRI data.

Dysconnectivity of the cerebellum and somatomotor network correlates with the severity of alogia in chronic schizophrenia

Recent fMRI resting-state findings show aberrant functional connectivity within somatomotor network (SMN) in schizophrenia. Moreover, functional connectivity aberrations of the motor system are often reported to be related to the severity of psychotic symptoms. Thus, it is important to validate those findings and confirm their relationship with psychopathology. Therefore, we decided to take an entirely data-driven approach in our fMRI resting-state study of 30 chronic schizophrenia outpatients and 30 matched control subjects. We used independent component analysis (ICA), dual regression, and seed-based connectivity analysis. We found reduced functional connectivity within SMN in schizophrenia patients compared to controls and SMN hypoconnectivity with the cerebellum in schizophrenia patients. The latter was strongly correlated with the severity of alogia, one of the main psychotic symptoms, i.e. poverty of speech and reduction in spontaneous speech,. Our results are consistent with the recent knowledge about the role of the cerebellum in cognitive functioning and its abnormalities in psychiatric disorders, e.g. schizophrenia. In conclusion, the presented results, for the first time clearly showed the involvement of the cerebellum hypoconnectivity with SMN in the persistence and severity of alogia symptoms in schizophrenia.

Vision- and touch-dependent brain correlates of body-related mental processing

In humans, the nature of sensory input influences body-related mental processing. For instance, behavioral differences (e.g., response time) can be found between mental spatial transformations (e.g., mental rotation) of viewed and touched body parts. It can thus be hypothesized that distinct brain activation patterns are associated with such sensory-dependent body-related mental processing. However, direct evidence that the neural correlates of body-related mental processing can be modulated by the nature of the sensory stimuli is still missing. We thus analyzed event-related functional magnetic resonance imaging (fMRI) data from thirty-one healthy participants performing mental rotation of visually- (images) and haptically-presented (plastic) hands. We also dissociated the neural activity related to rotation or task-related performance using models that either regressed out or included the variance associated with response time. Haptically-mediated mental rotation recruited mostly the sensorimotor brain network. Visually-mediated mental rotation led to parieto-occipital activations. In addition, faster mental rotation was associated with sensorimotor activity, while slower mental rotation was associated with parieto-occipital activations. The fMRI results indicated that changing the type of sensory inputs modulates the neural correlates of body-related mental processing. These findings suggest that distinct sensorimotor brain dynamics can be exploited to execute similar tasks depending on the available sensory input. The present study can contribute to a better evaluation of body-related mental processing in experimental and clinical settings.

Construction of embedded fMRI resting-state functional connectivity networks using manifold learning

We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional magnetic resonance imaging (rsfMRI) data acquired from patients with schizophrenia and healthy controls based on linear and nonlinear manifold learning algorithms, namely, Multidimensional Scaling, Isometric Feature Mapping, Diffusion Maps, Locally Linear Embedding and kernel PCA. Furthermore, based on key global graph-theoretic properties of the embedded FCN, we compare their classification potential using machine learning. We also assess the performance of two metrics that are widely used for the construction of FCN from fMRI, namely the Euclidean distance and the cross correlation metric. We show that diffusion maps with the cross correlation metric outperform the other combinations.

Alteration of brain resting-state networks and functional connectivity in prelingual deafness

BACKGROUND AND PURPOSE

Early hearing loss causes several changes in the brain structure and function at multiple levels; these changes can be observed through neuroimaging. These changes are directly associated with sensory loss (hearing) and the acquisition of alternative communication strategies. Such plasticity changes in the brain might establish a different connectivity pattern with resting-state networks (RSNs) and other brain regions. We performed resting-state functional magnetic resonance imaging (rsfMRI) to evaluate these intrinsic modifications.

METHODS

We used two methods to characterize the functional connectivity (FC) of RSN components in 20 prelingual deaf adults and 20 demographic-matched hearing adults. rsfMRI data were analyzed using independent component analysis (ICA) and region-of-interest seed-to-voxel correlation analysis.

RESULTS

In ICA, we identified altered FC of RSNs in the deaf group. RSNs with altered FC were observed in higher visual, auditory, default mode, salience, and sensorimotor networks. The findings of seed-to-voxel correlation analysis suggested increased temporal coherence with other neural networks in the deaf group compared with the hearing control group.

CONCLUSION

These findings suggest a highly diverse resting-state connectivity pattern in prelingual deaf adults resulting from compensatory cross-modal plasticity that includes both auditory and nonauditory regions.