Leftward Lateralization of Auditory Cortex Underlies Holistic Sound Perception in Williams Syndrome

BACKGROUND: Individuals with the rare genetic disorder Williams-Beuren syndrome (WS) are known for their characteristic auditory phenotype including strong affinity to music and sounds. In this work we attempted to pinpoint a neural substrate for the characteristic musicality in WS individuals by studying the structure-function relationship of their auditory cortex. Since WS subjects had only minor musical training due to psychomotor constraints we hypothesized that any changes compared to the control group would reflect the contribution of genetic factors to auditory processing and musicality. METHODOLOGY/PRINCIPAL FINDINGS: Using psychoacoustics, magnetoencephalography and magnetic resonance imaging, we show that WS individuals exhibit extreme and almost exclusive holistic sound perception, which stands in marked contrast to the even distribution of this trait in the general population. Functionally, this was reflected by increased amplitudes of left auditory evoked fields. On the structural level, volume of the left auditory cortex was 2.2-fold increased in WS subjects as compared to control subjects. Equivalent volumes of the auditory cortex have been previously reported for professional musicians. CONCLUSIONS/SIGNIFICANCE: There has been an ongoing debate in the neuroscience community as to whether increased gray matter of the auditory cortex in musicians is attributable to the amount of training or innate disposition. In this study musical education of WS subjects was negligible and control subjects were carefully matched for this parameter. Therefore our results not only unravel the neural substrate for this particular auditory phenotype, but in addition propose WS as a unique genetic model for training-independent auditory system properties

Adolescent brain maturation and cortical folding: evidence for reductions in gyrification

Evidence from anatomical and functional imaging studies have highlighted major modifications of cortical circuits during adolescence. These include reductions of gray matter (GM), increases in the myelination of cortico-cortical connections and changes in the architecture of large-scale cortical networks. It is currently unclear, however, how the ongoing developmental processes impact upon the folding of the cerebral cortex and how changes in gyrification relate to maturation of GM/WM-volume, thickness and surface area. In the current study, we acquired high-resolution (3 Tesla) magnetic resonance imaging (MRI) data from 79 healthy subjects (34 males and 45 females) between the ages of 12 and 23 years and performed whole brain analysis of cortical folding patterns with the gyrification index (GI). In addition to GI-values, we obtained estimates of cortical thickness, surface area, GM and white matter (WM) volume which permitted correlations with changes in gyrification. Our data show pronounced and widespread reductions in GI-values during adolescence in several cortical regions which include precentral, temporal and frontal areas. Decreases in gyrification overlap only partially with changes in the thickness, volume and surface of GM and were characterized overall by a linear developmental trajectory. Our data suggest that the observed reductions in GI-values represent an additional, important modification of the cerebral cortex during late brain maturation which may be related to cognitive development

Brain morphological and functional correlates of genetic, psychological, prenatal and prodromal risk for major mental disorders and their behavioural links

Cross-sectional mri-studies comparing psychiatric patients with healthy individuals have shown that patients show brain morphometric as well as functional changes. However, it is unclear whether these are pathological factors or whether these neurobiological changes are simply a risk factor for mental disorders, a consequence of therapy, only occur in certain subgroups. Therefore, the influence of a broad spectrum of different risk factors for mental disorders on brain morphometry as well as function was investigated in the present study: polygenic risk scores for psychiatric disorders, temporal perspective, shortened prenatal development as well as an extremely high risk for the development of psychosis. It can be shown that these risk factors significantly influence brain structural parameters as well as brain function. Some of these changes also correlated with behavioural changes such as poorer cognitive performance. These behavioural correlates could be valuable diagnostic or prognostic markers and could also be important research targets for the development of new therapeutic approaches

Three shades of grey : detecting brain abnormalities in children with autism by using source-, voxel- and surface-based morphometry

Autistic spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social interactions, communication and stereotyped behavior. Recent evidence from neuroimaging supports the hypothesis that ASD deficits in adults may be related to abnormalities in a specific frontal - temporal network (Autism-specific Structural Network, ASN). To see whether these results extend to younger children and to better characterize these abnormalities, we applied three morphometric methods on brain grey matter of children with and without ASD. We selected 39 sMRI images of male children with ASD and 42 typically developing (TD) from the ABIDE database. We used Source -Based Morphometry (SoBM), a whole-brain multivariate approach to identify grey matter networks, Voxel-Based Morphometry (VBM), a voxel-wise comparison of the local grey matter concentration, and Surface-Based Morphometry (SuBM) for the estimation of the cortical parameters. SoBM showed a bilateral frontal - parietal - temporal network different between groups, including the inferior - middle temporal gyrus, the inferior parietal lobule and the postcentral gyrus; VBM returned differences only in the right temporal lobe; SuBM returned a thinning in the right inferior temporal lobe thinner in ASD, a higher gyrification in the right superior parietal lobule in TD and in the middle frontal gyrus in ASD

Doctor of Philosophy

dissertationNeurodegenerative diseases are an increasing health care problem in the United States. Quantitative neuroimaging provides a noninvasive method to illuminate individual variations in brain structure to better understand and diagnose these disorders. The overall objective of this research is to develop novel clinical tools that summarize and quantify changes in brain shape to not only help better understand age-appropriate changes but also, in the future, to dissociate structural changes associated with aging from those caused by dementing neurodegenerative disorders. Because the tools we will develop can be applied for individual assessment, achieving our goals could have a significant clinical impact. An accurate, practical objective summary measure of the brain pathology would augment current subjective visual interpretation of structural magnetic resonance images. Fractal dimension is a novel approach to image analysis that provides a quantitative measure of shape complexity describing the multiscale folding of the human cerebral cortex. Cerebral cortical folding reflects the complex underlying architectural features that evolve during brain development and degeneration including neuronal density, synaptic proliferation and loss, and gliosis. Building upon existing technology, we have developed innovative tools to compute global and local (voxel-wise and regional) cerebral cortical fractal dimensions and voxel-wise cortico-fractal surfaces from high-contrast MR images. Our previous research has shown that fractal dimension correlates with cognitive function and changes during the course of normal aging. We will now apply unbiased diffeomorphic atlasing methodology to dramatically improve the alignment of complex cortical surfaces. Our novel methods will create more accurate, detailed geometrically averaged images to take into account the intragroup differences and make statistical inferences about spatiotemporal changes in shape of the cerebral cortex across the adult human lifespan

Cortical complexity as a measure of age-related brain atrophy

The structure of the human brain changes in a variety of ways as we age. While a sizeable literature has examined age-related differences in cortical thickness, and to a lesser degree, gyrification, here we examined differences in cortical complexity, as indexed by fractal dimensionality in a sample of over 400 individuals across the adult lifespan. While prior studies have shown differences in fractal dimensionality between patient populations and age-matched, healthy controls, it is unclear how well this measure would relate to age-related cortical atrophy. Initially computing a single measure for the entire cortical ribbon, i.e., unparcellated gray matter, we found fractal dimensionality to be more sensitive to age-related differences than either cortical thickness or gyrification index. We additionally observed regional differences in age-related atrophy between the three measures, suggesting that they may index distinct differences in cortical structure. We also provide a freely available MATLAB toolbox for calculating fractal dimensionality

Assessment of the potentials and limitations of cortical-based analysis for the integration of structure and function in normal and pathological brains using MRI

The software package Brainvisa (www.brainvisa.tnfo) offers a wide range of possibilities for cortical analysis using its automatic sulci recognition feature. Automated sulci identification is an attractive feature as the manual labelling of the cortical sulci is often challenging even for the experienced neuro-radiologists. This can also be of interest in fMRI studies of individual subjects where activated regions of the cortex can simply be identified using sulcal labels without the need for normalization to an atlas. As it will be explained later in this thesis, normalization to atlas can especially be problematic for pathologic brains. In addition, Brainvisa allows for sulcal morphometry from structural MR images by estimating a wide range of sulcal properties such as size, coordinates, direction, and pattern. Morphometry of abnormal brains has gained huge interest and has been widely used in finding the biomarkers of several neurological diseases or psychiatric disorders. However mainly because of its complexity, only a limited use of sulcal morphometry has been reported so far. With a wide range of possibilities for sulcal morphometry offered by Brainvisa, it is possible to thoroughly investigate the sulcal changes due to the abnormality. However, as any other automated method, Brainvisa can be susceptible to limitations associated with image quality. Factors such as noise, spatial resolution, and so on, can have an impact on the detection of the cortical folds and estimation of their attributes. Hence the robustness of Brainvisa needs to be assessed. This can be done by estimating the reliability and reproducibility of results as well as exploring the changes in results caused by other factors. This thesis is an attempt to investigate the possible benefits of sulci identification and sulcal morphometry for functional and structural MRI studies as well as the limitations of Brainvisa. In addition, the possibility of improvement of activation localization with functional MRI studies is further investigated. This investigation was motivated by a review of other cortical-based analysis methods, namely the cortical surface-based methods, which are discussed in the literature review chapter of this thesis. The application of these approaches in functional MRI data analysis and their potential benefits is used in this investigation

Cortical gyrification morphology in individuals with ASD and ADHD across the lifespan: a systematic review and meta-analysis

Autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD) are common neurodevelopmental disorders (NDDs) that may impact brain maturation. A number of studies have examined cortical gyrification morphology in both NDDs. Here we review and when possible pool their results to better understand the shared and potentially disorder-specific gyrification features. We searched MEDLINE, PsycINFO, and EMBASE databases, and 24 and 10 studies met the criteria to be included in the systematic review and meta-analysis portions, respectively. Meta-analysis of local Gyrification Index (lGI) findings across ASD studies was conducted with SDM software adapted for surface-based morphometry studies. Meta-regressions were used to explore effects of age, sex, and sample size on gyrification differences. There were no significant differences in gyrification across groups. Qualitative synthesis of remaining ASD studies highlighted heterogeneity in findings. Large-scale ADHD studies reported no differences in gyrification between cases and controls suggesting that, similar to ASD, there is currently no evidence of differences in gyrification morphology compared with controls. Larger, longitudinal studies are needed to further clarify the effects of age, sex, and IQ on cortical gyrification in these NDDs.info:eu-repo/semantics/publishedVersio

Dimensions of psychosis: Elucidating the subclinical spectrum using neuroimaging markers

Psychosis unifies a collective of disorders characterised by symptom dimensions (Gaebel & Zielasek, 2015). Purposefully delimited clinical descriptors of schizophrenia spectrum and psychotic disorders (American Psychiatric Association, 2013) impose challenges on the identification of aetiological and clinically meaningful predictors. The disassembly of psychiatric diagnoses into their elementary symptom dimensions has helped formulate psychosis phenotypes fitted on a psychosis continuum (Verdoux & van Os, 2002). Aetiological models of psychosis may be studied through schizotypy and transient psychotic experiences (Barrantes-Vidal et al., 2015; Nelson, Fusar-Poli, & Yung, 2012), collectively termed subclinical psychosis phenotypes. The dimensional psychometric structures of these phenotypes varying in temporal stability (Linscott & van Os, 2013; Mason et al., 1995; Stefanis et al., 2002), and their implications might be further consolidated when paired with neuroimaging parameters (Siever & Davis, 2004). Three neuroimaging studies aimed to examine the relationship between subclinical psychotic phenotypes and neurobiology. Surface and volume-based morphometric (VBM) methods were implemented to examine the variety of cortical and subcortical signatures of different phenotype dimensions. Study 1 investigated whether cortical surface gyrification -a maker of genetic and developmental influences on cortical morphology (Docherty et al., 2015; Haukvik et al., 2012)- is associated with dimensional psychosis prone phenomena (Konings, Bak, Hanssen, van Os, & Krabbendam, 2006; Stefanis et al., 2002). Early cortical organisation contributes to cognitive capacities in later life (Gautam et al., 2015; Gregory et al., 2016; Papini et al., 2020). Given that cognitive deficits are present in psychosis prone and clinical samples to varying extents (Hou et al., 2016; Siddi et al., 2017), Study 1 also explored the mediating role of cognition (both as a general measure and intelligence quotient) as a psychosis endophenotype in the relationship between regional gyrification and PLE distress. Study 2 and Study 3 used VBM to investigate structural brain correlates for psychotic-like experiences (PLE) and trait psychosis phenotypes (schizotypy). Different PLE facets (quantity and distress severity) (Hanssen, Bak, et al., 2005; Ising et al., 2012) were used to estimate whole-brain grey matter volume, followed by interaction models in subsequent prefrontal regions of interest (Study 2). The medial temporal lobe includes the hippocampal subfields, which are regions of interest in psychosis pathophysiology (Lieberman et al., 2018; Mathew et al., 2014; Schobel et al., 2013). Based on a previous study in schizoytypy (Sahakyan et al., 2020), Study 3 examined the relationship between schizotypal trait dimensions (Mason et al., 1995) and PLE, and their interactions, and hippocampal subfields and the amygdala. The results of Study 1 showed that psychometrically assessed PLE were associated with reduced gyrification in parietal and temporal regions, indicating that psychosis proneness correlates with neurodevelopmental factors (Fonville et al., 2019; Liu et al., 2016). A lack of mediating pathways between regional gyrification and PLE suggested that cognition effects may emerge in larger samples (Mollon et al., 2016) and/or increasingly psychosis pone phenotypes. Elaborating on the distinction between PLE quantity versus distress, Study 2 showed that PLE load, but not distress severity, were associated with volume increases in prefrontal and occipitotemporal regions. At increased distress severity for perceptual abnormalities, PLE were associated with regional volume reductions of the superior frontal gyrus. Study 3 showed differential relationships between schizotypy dimensions and volumes of the MTL that are involved in the pathophysiology of schizophrenia. PLE per se did not associate with amygdala or hippocampal subfield volumes, but a positive association between the hippocampal subiculum and PLE was moderated by positive schizotypy. Study 3 underscored the enhanced usefulness of schizotypy as an endophenotype in psychosis research when its multidimensional organisation (Grant, 2015; Vollema & van den Bosch, 1995) is respected. The results support the use of psychosis symptom dimensions, showing different (positive and negative) neuroanatomical associations. While case-control studies in schizophrenia show consistent volume reductions of the prefrontal and temporal cortices (Haijma et al., 2013; Honea, Crow, Passingham, & Mackay, 2005), these findings contribute to more heterogeneous volumetric relationships in nonclinical individuals. Reduced regional cortical gyrification proposes a continuous distribution of neurodevelopmental impacts. Distress severity and schizotypy occasioned modulatory effects in prefrontal and hippocampal subfield volumes, respectively. Collectively, these three cross-sectional studies extend previous research suggesting that dimensional phenotypes show neuroanatomical variation supportive of a psychosis continuum possibly characterised by an underlying non-linearity (Bartholomeusz et al., 2017; Binbay et al., 2012; Johns & van Os, 2001)

Multimodal neuroimaging signatures of early cART-treated paediatric HIV - Distinguishing perinatally HIV-infected 7-year-old children from uninfected controls

Introduction: HIV-related brain alterations can be identified using neuroimaging modalities such as proton magnetic resonance spectroscopy (1H-MRS), structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and functional MRI (fMRI). However, few studies have combined multiple MRI measures/features to identify a multivariate neuroimaging signature that typifies HIV infection. Elastic net (EN) regularisation uses penalised regression to perform variable selection, shrinking the weighting of unimportant variables to zero. We chose to use the embedded feature selection of EN logistic regression to identify a set of neuroimaging features characteristic of paediatric HIV infection. We aimed to determine 1) the most useful features across MRI modalities to separate HIV+ children from HIV- controls and 2) whether better classification performance is obtained by combining multimodal MRI features rather than using features from a single modality. Methods: The study sample comprised 72 HIV+ 7-year-old children from the Children with HIV Early Antiretroviral Therapy (CHER) trial in Cape Town, who initiated combination antiretroviral therapy (cART) in infancy and had their viral loads suppressed from a young age, and 55 HIV- control children. Neuroimaging features were extracted to generate 7 MRI-derived sets. For sMRI, 42 regional brain volumes (1st set), mean cortical thickness and gyrification in 68 brain regions (2nd and 3rd set) were used. For DTI data: radial (RD), axial (AD), mean (MD) diffusivities, and fractional anisotropy (FA) in each of 20 atlas regions were extracted for a total of 80 DTI features (4th set). For 1H-MRS, concentrations of 14 metabolites and their ratios to creatine in the basal ganglia, peritrigonal white matter, and midfrontal gray matter voxels (5th, 6th and 7th set) were considered. A logistic EN regression model with repeated 10-fold cross validation (CV) was implemented in R, initially on each feature set separately. Sex, age and total intracranial volume (TIV) were included as confounders with no shrinkage penalty. For each model, the classification performance for HIV+ vs HIV- was assessed by computing accuracy, specificity, sensitivity, and mean area under the receiver operator characteristic curve (AUC) across 10 CV folds and 100 iterations. To combine feature sets, the best performing set was concatenated with each of the other sets and further EN regressions were run. The combination giving the largest AUC was combined with each of the remaining sets until there was no further increase in AUC. Two concatenation techniques were explored: nested and non-nested modelling. All models were assessed for their goodness of fit using χ 2 likelihood ratio tests for non-nested models and Akaike information criterion (AIC) for nested models. To identify features most useful in distinguishing HIV infection, the EN model was retrained on all the data, to find features with non-zero weights. Finally, multivariate imputation using chained equations (MICE) was explored to investigate the effect of increased sample size on classification and feature selection. Results: The best performing modality in the single modality analysis was sMRI volume