NEW APPROACHES FOR ESTIMATING HEMISPHERIC LATERALIZATION FROM RESTING STATE FMRI DATA WITH RELATIONSHIP TO AGE, GENDER AND MENTAL DISORDERS

Lateralization is specialization of the brain hemispheres in certain tasks, such as language, mathematics, cognition and motor skills. It is one of the most queried topics related to the human brain. After the invention of modern medical imaging techniques including functional magnetic resonance imaging (fMRI), scientific research about the human brain, including lateralization, gained huge momentum. There have been a remarkable numbers of studies about lateralization and most of these studies focused on investigating which part of the brain dominates in which tasks. However, there have been very few lateralization studies on brain intrinsic activity, i.e., resting state activity where subjects are asked to stay awake while resting without performing any specific tasks. Independent component analysis (ICA), a data-driven blind source separation method, has become one of the conventional data analysis tools for brain imaging data. ICA can separate the brain imaging data into functional regions that are temporally coherent, and functional network connectivity (FNC) of these regions can be computed. FNC is a measure that captures the temporal covariance of the brain networks. In this dissertation, we focus on the lateralization during the resting state and assess hemispheric differences during the resting state. The lateralization of the resting state networks and their association with age and gender is presented using a large resting state fMRI dataset. A novel approach for generating hemisphere specific time-courses and computing FNC inside the hemispheres and between hemispheres is proposed and the relationship of these FNC values with age, gender and mental illness, schizophrenia is reported. Finally, a new framework to estimate power spectral density of 4D brain imaging data and a dimension reduction method to reduce dimensionality from 4D frequency domain to 2D frequency domain has been proposed. This framework helps us to reveal spatiotemporal organization differences between hemispheres. In summary, our work has made several contributions to advance lateralization analysis and has improved our understanding of various aspects of hemispheric differences during the resting stat

Left lateralized cerebral glucose metabolism declines in amyloid-β positive persons with mild cognitive impairment

Background: Previous publications indicate that Alzheimer\u27s Disease (AD) related cortical atrophy may develop in asymmetric patterns, with accentuation of the left hemisphere. Since fluorodeoxyglucose positron emission tomography (FDG PET) measurements of the regional cerebral metabolic rate of glucose (rCMRgl) provide a sensitive and specific marker of neurodegenerative disease progression, we sought to investigate the longitudinal pattern of rCMRgl in amyloid-positive persons with mild cognitive impairment (MCI) and dementia, hypothesizing asymmetric declines of cerebral glucose metabolism. Methods: Using florbetapir PET and cerebrospinal fluid (CSF) measures to define amyloid-β (Aβ) positivity, 40 Aβ negative (Aβ-) cognitively unimpaired controls (CU; 76 ± 5y), 76 Aβ positive (Aβ+) persons with MCI (76 ± 7y) and 51 Aβ+persons with probable AD dementia (75 ± 7y) from the AD Neuroimaging Initiative (ADNI) were included in this study with baseline and 2-year follow-up FDG PET scans. The degree of lateralization of longitudinal rCMRgl declines in subjects with Aβ+MCI and AD in comparison with Aβ- CU were statistically quantified via bootstrapped lateralization indices [(LI); range−1 (right) to 1 (left)]. Results: Compared to Aβ- CU, Aβ+MCI patients showed marked left hemispheric lateralization (LI: 0.78). In contrast, modest right hemispheric lateralization (LI: −0.33) of rCMRgl declines was found in Aβ+persons with probable AD dementia. Additional comparisons of Aβ+groups (i.e. MCI and probable AD dementia) consequently indicated right hemispheric lateralization (LI: −0.79) of stronger rCMRgl declines in dementia stages of AD. For all comparisons, voxel-based analyses confirmed significant (pFWE\u3c0.05) declines of rCMRgl within AD-typical brain regions. Analyses of cognitive data yielded predominant decline of memory functions in both MCI and dementia stages of AD. Conclusions: These data indicate that in early stages, AD may be characterized by a more lateralized pattern of left hemispheric rCMRgl declines. However, metabolic differences between hemispheres appear to diminish with further progression of the disease

Sex differences in brain homotopic co-activations: a meta-analytic study

An element of great interest in functional connectivity is ‘homotopic connectivity’ (HC), namely the connectivity between two mirrored areas of the two hemispheres, mainly mediated by the fibers of the corpus callosum. Despite a long tradition of studying sexual dimorphism in the human brain, to our knowledge only one study has addressed the influence of sex on HC. We investigated the issue of homotopic co-activations in women and men using a coordinate-based meta-analytic method and data from the BrainMap database. A first unexpected observation was that the database was affected by a sex bias: women-only groups are investigated less often than men-only ones, and they are more often studied in certain domains such as emotion compared to men, and less in cognition. Implementing a series of sampling procedures to equalize the size and proportion of the datasets, our results indicated that females exhibit stronger interhemispheric co-activation than males, suggesting that the female brain is less lateralized and more integrated than that of males. In addition, males appear to show less intense but more extensive co-activation than females. Some local differences also appeared. In particular, it appears that primary motor and perceptual areas are more co-activated in males, in contrast to the opposite trend in the rest of the brain. This argues for a multidimensional view of sex brain differences and suggests that the issue should be approached with more complex models than previously thought. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00429-022-02572-0

Variability and reliability of effective connectivity within the core default mode network: A multi-site longitudinal spectral DCM study

Dynamic causal modelling (DCM) for resting state fMRI - namely spectral DCM - is a recently developed and widely adopted method for inferring effective connectivity in intrinsic brain networks. Most applications of spectral DCM have focused on group-averaged connectivity within large-scale intrinsic brain networks; however, the consistency of subject- and session-specific estimates of effective connectivity has not been evaluated. Establishing reliability (within subjects) is crucial for its clinical use; e.g., as a neurophysiological phenotype of disease progression. Effective connectivity during rest is likely to vary due to changes in cognitive, and physiological states. Quantifying these variations may help understand functional brain architectures - and inform clinical applications. In the present study, we investigated the consistency of effective connectivity within and between subjects, as well as potential sources of variability (e.g., hemispheric asymmetry). We also addressed the effects on consistency of standard data processing procedures. DCM analyses were applied to four longitudinal resting state fMRI datasets. Our sample comprised 17 subjects with 589 resting state fMRI sessions in total. These data allowed us to quantify the robustness of connectivity estimates for each subject, and to generalise our conclusions beyond specific data features. We found that subjects showed systematic and reliable patterns of hemispheric asymmetry. When asymmetry was taken into account, subjects showed very similar connectivity patterns. We also found that various processing procedures (e.g. global signal regression and ROI size) had little effect on inference and the reliability of connectivity estimates for the majority of subjects. Finally, Bayesian model reduction significantly increased the consistency of connectivity patterns

Identifying reproducible resting state networks and functional connectivity alterations following chronic restraint stress in anaesthetized rats

BackgroundResting-state functional MRI (rs-fMRI) in rodent models have the potential to bridge invasive experiments and observational human studies, increasing our understanding of functional alterations in the brains of patients with depression. A major limitation in current rodent rs-fMRI studies is that there has been no consensus on healthy baseline resting-state networks (RSNs) that are reproducible in rodents. Therefore, the present study aimed to construct reproducible RSNs in a large dataset of healthy rats and then evaluate functional connectivity changes within and between these RSNs following a chronic restraint stress (CRS) model within the same animals.MethodsA combined MRI dataset of 109 Sprague Dawley rats at baseline and after two weeks of CRS, collected during four separate experiments conducted by our lab in 2019 and 2020, was re-analysed. The mICA and gRAICAR toolbox were first applied to detect optimal and reproducible ICA components and then a hierarchical clustering algorithm (FSLNets) was applied to construct reproducible RSNs. Ridge-regularized partial correlation (FSLNets) was used to evaluate the changes in the direct connection between and within identified networks in the same animals following CRS.ResultsFour large-scale networks in anesthetised rats were identified: the DMN-like, spatial attention-limbic, corpus striatum, and autonomic network, which are homologous across species. CRS decreased the anticorrelation between DMN-like and autonomic network. CRS decreased the correlation between amygdala and a functional complex (nucleus accumbens and ventral pallidum) in the right hemisphere within the corpus striatum network. However, a high individual variability in the functional connectivity before and after CRS within RSNs was observed.ConclusionThe functional connectivity changes detected in rodents following CRS differ from reported functional connectivity alterations in patients with depression. A simple interpretation of this difference is that the rodent response to CRS does not reflect the complexity of depression as it is experienced by humans. Nonetheless, the high inter-subject variability of functional connectivity within networks suggests that rats demonstrate different neural phenotypes, like humans. Therefore, future efforts in classifying neural phenotypes in rodents might improve the sensitivity and translational impact of models used to address aetiology and treatment of psychiatric conditions including depression

Differential patterns of age-related cortical and subcortical functional connectivity in 6-to-10 year old children: A connectome-wide association study

Introduction: Typical brain development is characterized by specific patterns of maturation of functional networks. Cortico-cortical connectivity generally increases, whereas subcortico-cortical connections often decrease. Little is known about connectivity changes amongst different subcortical regions in typical development. Methods: This study examined age- and gender-related differences in functional connectivity between and within cortical and subcortical regions using two different approaches. The participants included 411 six- to ten-year-old typically developing children sampled from the population-based Generation R study. Functional connectomes were defined in native space using regions of interest from subject-specific FreeSurfer segmentations. Connections were defined as: (a) the correlation between regional mean time-series; and (b) the focal maximum of voxel-wise correlations within FreeSurfer regions. The association of age and gender with each functional connection was determined using linear regression. The preprocessing included the exclusion of children with excessive head motion and scrubbing to reduce the influence of minor head motion during scanning. Results: Cortico-cortical associations echoed previous findings that connectivity shifts from short to long-range with age. Subcortico-cortical associations with age were primarily negative in the focal network approach but were both positive and negative in the mean time-series network approach. Between subcortical regions, age-related associations were negative in both network approaches. Few connections had significant associations with gender. Conclusions: The present study replicates previously reported age-related patterns of connectivity in a relatively narrow age-range of children. In addition, we extended these findings by demonstrating decreased connectivity within the subcortex with increasing age. Lastly, we show the utility of a more focal approach that challenges the spatial assumptions made by the traditional mean time series approach

Advanced Application of Diffusion Kurtosis Imaging

Diffusion tensor imaging (DTI) has become a standard procedure in clinical routine as well as research as it enables the reconstruction and visualization of fiber tracts in the human brain. Due to the simplified assumption the tensor model – a Gaussian distribution of the diffusion – it typically fails to provide neither accurate spatial mapping nor quantification of crossing or kissing fibers. A clinically feasible development might be diffusion kurtosis imaging (DKI), an extension of DTI also integrating non-Gaussian distribution diffusion processes and thereby shall overcome some of its limitations. The potential DKI will be evaluated in case of the detection of the interhemispheric asymmetry of the white matter in healthy volunteers (n = 20), as well as the analysis of tumor-related impairments of fiber tracts and their correlation with neurological deficits in patients (n = 13) diagnosed with glioma. In order to analyze interhemispheric asymmetry across the whole brain, especially of nine large fiber tracts, tract-based spatial statistics (TBSS) analysis was performed using DTI- and DKI-based parameters, a laterality index was calculated for asymmetries and DTI- and DKI-based results were compared. With regard to fractional anisotropy as marker of integrity, asymmetry was found for all nine fiber tracts based on DTI and seven tracts based on DKI. For mean diffusivity, asymmetries were found for three (DTI) and two (DKI) fiber tracts. Regarding mean kurtosis, asymmetry was found in one tract. The interhemispheric asymmetry thereby varied in anatomical location as well as in cluster size. Only small parts of the tracts were affected. A comparison of DTI and DKI showed significantly higher fractional anisotropy and mean diffusivity based on DKI compared to DTI. Gender and handedness did not seem to have any influence. For the assessment of tumor-related changes of fiber tracts in patients diagnosed with glioma, especially in relation to pre-existing and postoperative neurological deficits (hemiparesis, aphasia), templates for the corticospinal tract and the arcuate fasciculus were created based on DTI- and DKI-derived parameters, respectively. The corticospinal tract and the arcuate fasciculus were reconstructed for each patient and the associated parametric maps were projected onto the templates. Based on this, alterations along the tracts could be identified and quantified. Alterations were found on fiber tracts regardless of the spatial proximity to the lesion. There was a correlation between alterations based on fractional anisotropy, mean diffusivity and mean kurtosis. Increased mean diffusivity was associated with alteration in mean kurtosis, a decreased fractional anisotropy was found concurrent with a likewise decreased mean kurtosis. In the case of pre-existing neurological deficits (hemiparesis, aphasia) with regard to the changes along the fiber tracts (corticospinal tract, left arcuate fasciculus), most often increased mean diffusivity and altered mean kurtosis was found. Applying this pattern for prediction of corresponding postoperative neurological deficits a sensitivity of 75.0% and a specificity of 87.5% was achieved. DKI seems to more precisely estimated and depict the underlying microstructure in comparison to DTI. Thereby, in pathological cases especially the mean kurtosis seems to be of special interest. A combination of DTI- and DKI based parameters, particularly with regard to its clinical usability and value, offers great potential in clinical routine