Mother-child similarity in brain morphology: A comparison of structural characteristics of the brain\u27s reading network

Background: Substantial evidence acknowledges the complex gene-environment interplay impacting brain development and learning. Intergenerational neuroimaging allows the assessment of familial transfer effects on brain structure, function and behavior by investigating neural similarity in caregiver-child dyads. Methods: Neural similarity in the human reading network was assessed through well-used measures of brain structure (i.e., surface area (SA), gyrification (lG), sulcal morphology, gray matter volume (GMV) and cortical thickness (CT)) in 69 mother-child dyads (children\u27s age~11 y). Regions of interest for the reading network included left-hemispheric inferior frontal gyrus, inferior parietal lobe and fusiform gyrus. Mother-child similarity was quantified by correlation coefficients and familial specificity was tested by comparison to random adult-child dyads. Sulcal morphology analyses focused on occipitotemporal sulcus interruptions and similarity was assessed by chi-square goodness of fit. Results: Significant structural brain similarity was observed for mother-child dyads in the reading network for lG, SA and GMV (r = 0.349/0.534/0.542, respectively), but not CT. Sulcal morphology associations were non-significant. Structural brain similarity in lG, SA and GMV were specific to mother-child pairs. Furthermore, structural brain similarity for SA and GMV was higher compared to CT. Conclusion: Intergenerational neuroimaging techniques promise to enhance our knowledge of familial transfer effects on brain development and disorders

Developing multidimensional metrics for evaluating paediatric neurodevelopmental disorders

Healthy brain functioning depends on efficient communication of information between brain regions, forming complex networks. By quantifying synchronisation between brain regions, a functionally connected brain network can be articulated. In neurodevelopmental disorders, where diagnosis is based on measures of behaviour and tasks, a measure of the underlying biological mechanisms holds promise as a potential clinical tool. Graph theory provides a tool for investigating the neural correlates of neuropsychiatric disorders, where there is disruption of efficient communication within and between brain networks. This research aimed to use recent conceptualisation of graph theory, along with measures of behaviour and cognitive functioning, to increase understanding of the neurobiological risk factors of atypical development. Using magnetoencephalography to investigate frequency-specific temporal dynamics at rest, the research aimed to identify potential biological markers derived from sensor-level whole-brain functional connectivity. Whilst graph theory has proved valuable for insight into network efficiency, its application is hampered by two limitations. First, its measures have hardly been validated in MEG studies, and second, graph measures have been shown to depend on methodological assumptions that restrict direct network comparisons. The first experimental study (Chapter 3) addressed the first limitation by examining the reproducibility of graph-based functional connectivity and network parameters in healthy adult volunteers. Subsequent chapters addressed the second limitation through adapted minimum spanning tree (a network analysis approach that allows for unbiased group comparisons) along with graph network tools that had been shown in Chapter 3 to be highly reproducible. Network topologies were modelled in healthy development (Chapter 4), and atypical neurodevelopment (Chapters 5 and 6). The results provided support to the proposition that measures of network organisation, derived from sensor-space MEG data, offer insights helping to unravel the biological basis of typical brain maturation and neurodevelopmental conditions, with the possibility of future clinical utility

Cortical Thickness and Gyrification in Children with Developmental Dyslexia

Prior research has demonstrated a pattern of atypical neural structure and function within regions of the left hemisphere reading network in individuals with dyslexia compared to controls. However, studies of pediatric dyslexia are sparse, demonstrate variability in dyslexia classification, and yield inconsistent associations between cortical metrics and reading ability. This study investigated cortical metrics in typically developing readers (n=39) and children with dyslexia (n=37) as determined by deficient single word reading ability. Whole-brain vertex-wise analyses, performed using FreeSurfer, evaluated cortical thickness and local gyrification between reading groups, controlling for age. Following multiple comparison correction, readers with dyslexia demonstrated reduced cortical thickness within previously identified critical reading areas including: bilateral inferior-temporal, inferior-frontal, and occipito-parietal regions, along with left anterior cingulate cortex. In readers with dyslexia, thinner cortex was accompanied by increased gyrification in the cuneus and left inferior temporal cortex. The convergence of thinner and more gyrified cortex within the left inferior temporal region in children with dyslexia may reflect its early temporal role in processing word forms, and highlights the importance of the ventral stream for successful decoding. Reading fluency scores demonstrated a positive association with cortical thickness in right inferior frontal and bilateral inferior temporal cortices, while reading comprehension was significantly correlated with thickness across all regions.Psychology, Department o

Structural gray matter features and behavioral preliterate skills predict future literacy – A machine learning approach

When children learn to read, their neural system undergoes major changes to become responsive to print. There seem to be nuanced interindividual differences in the neurostructural anatomy of regions that later become integral parts of the reading network. These differences might affect literacy acquisition and, in some cases, might result in developmental disorders like dyslexia. Consequently, the main objective of this longitudinal study was to investigate those interindividual differences in gray matter morphology that might facilitate or hamper future reading acquisition. We used a machine learning approach to examine to what extent gray matter macrostructural features and cognitive-linguistic skills measured before formal literacy teaching could predict literacy 2 years later. Forty-two native German-speaking children underwent T1-weighted magnetic resonance imaging and psychometric testing at the end of kindergarten. They were tested again 2 years later to assess their literacy skills. A leave-one-out cross-validated machine-learning regression approach was applied to identify the best predictors of future literacy based on cognitive-linguistic preliterate behavioral skills and cortical measures in a priori selected areas of the future reading network. With surprisingly high accuracy, future literacy was predicted, predominantly based on gray matter volume in the left occipito-temporal cortex and local gyrification in the left insular, inferior frontal, and supramarginal gyri. Furthermore, phonological awareness significantly predicted future literacy. In sum, the results indicate that the brain morphology of the large-scale reading network at a preliterate age can predict how well children learn to read

Neural Correlates in Learning Disabilities

In recent years, researchers have done significant advances on the study of learning disabilities in particular in terms of comprehension of cognitive and anatomical mechanisms. The understanding of neural mechanism of learning disabilities is useful for their management and cognitive treatment. The advent of functional neuroimaging methods has also identified anatomical networks and neurological learning systems that have contributed to knowledge of neurobiology of learning deficits. On the other side, neuropsychological assessment, with comprehensive test or specific cognitive tasks, has proved to be useful to analyze specific cognitive deficits to find potential targets of intervention for cognitive compensation. In this chapter the author summarizes major scientific advances in particular in the study of neuroanatomical mechanism based on structural and functional neuroimaging of children with learning disorders, developmental disorders, and language impairment, in particular with dyslexia which is one of the most common learning disabilities

White matter connectome associations with reading functions in children

This thesis investigated associations between the white matter connectome and reading in children with a wide range of reading abilities. It is well established that the connectome supports the interplay among brain regions and connections within an integrated system. In this dissertation, I examine the hypothesis that it could therefore represent multiple mapping processes among reading components and further explain variations in reading performance. Such associations between the organization of the connectome and reading skills have not been well explored. This thesis aimed to address this issue by considering both the relationship between connectome measures and standardized reading performance out of scanner, and neural activations during in-scanner reading tasks. Chapter 2 examined the rich-club organization of the white matter connectome and its association with sight word reading, phonemic decoding, reading comprehension, and rapid automatized naming in children. I found that feeder connections that link hub and reading network regions were associated with word-level reading skills. Chapter 3 further examined how the left thalamus influences reading skills by coordinating information flow between the reading network and hub regions. Results showed that the efficiency metrics and routing cost of the left thalamus within a subnetwork, which contains the reading network and hub regions, were associated with rapid automatized naming and phonemic decoding scores, respectively. Chapter 4 applied network control theory to investigate if the white matter connectome could explain the dynamics of functional activation. Specifically, I examined if control energy, which reflects the level of cognitive demands from a task, showed differences across different conditions of an in-scanner rhyming task. I found that conditions requiring more effort were associated with higher control energy within reading network areas. In addition, the control energy of the superior temporal gyrus and fusiform gyrus showed dissociations regarding different modalities of stimulus presentation. Moreover, children with better word-level reading scores required lower control energy. Chapter 5 summarizes the findings and discusses their implications to the connectome-reading relationship

Convergent and diver gent brain structural and functional abnormalities associated with developmental dyslexia

Brain abnormalities in the reading network have been repeatedly reported in individuals with developmental dyslexia (DD); however, it is still not totally understood where the structural and functional abnormalities are consistent/inconsistent across languages. In the current multimodal meta-analysis, we found convergent structural and functional alterations in the left superior temporal gyrus across languages, suggesting a neural signature of DD. We found greater reduction in grey matter volume and brain activation in the left inferior frontal gyrus in morpho-syllabic languages (e.g. Chinese) than in alphabetic languages, and greater reduction in brain activation in the left middle temporal gyrus and fusiform gyrus in alphabetic languages than in morpho-syllabic languages. These language differences are explained as consequences of being DD while learning a specific language. In addition, we also found brain regions that showed increased grey matter volume and brain activation, presumably suggesting compensations and brain regions that showed inconsistent alterations in brain structure and function. Our study provides important insights about the etiology of DD from a cross-linguistic perspective with considerations of consistency/inconsistency between structural and functional alterations