Development Of Human Brain Network Architecture Underlying Executive Function

The transition from late childhood to adulthood is characterized by refinements in brain structure and function that support the dynamic control of attention and goal-directed behavior. One broad domain of cognition that undergoes particularly protracted development is executive function, which encompasses diverse cognitive processes including working memory, inhibitory control, and task switching. Delineating how white matter architecture develops to support specialized brain circuits underlying individual differences in executive function is critical for understanding sources of risk-taking behavior and mortality during adolescence. Moreover, neuropsychiatric disorders are increasingly understood as disorders of brain development, are marked by failures of executive function, and are linked to the disruption of evolving brain connectivity. Network theory provides a parsimonious framework for modeling how anatomical white matter pathways support synchronized fluctuations in neural activity. However, only sparse data exists regarding how the maturation of white matter architecture during human brain development supports coordinated fluctuations in neural activity underlying higher-order cognitive ability. To address this gap, we capitalize on multi-modal neuroimaging and cognitive phenotyping data collected as part of the Philadelphia Neurodevelopmental Cohort (PNC), a large community-based study of brain development. First, diffusion tractography methods were applied to characterize how the development of structural brain network topology supports domain-specific improvements in cognitive ability (n=882, ages 8-22 years old). Second, structural connectivity and task-based functional connectivity approaches were integrated to describe how the development of anatomical constraints on functional communication support individual differences in executive function (n=727, ages 8-23 years old). Finally, the systematic impact of head motion artifact on measures of structural connectivity were characterized (n=949, ages 8-22 years old), providing important guidelines for studying the development of structural brain network architecture. Together, this body of work expands our understanding of how developing white matter connectivity in youth supports the emergence of functionally specialized circuits underlying executive processing. As diverse types of psychopathology are increasingly linked to atypical brain maturation, these findings could collectively lead to earlier diagnosis and personalized interventions for individuals at risk for developing mental disorders

Structural brain networks from diffusion MRI: methods and application

Structural brain networks can be constructed at a macroscopic scale using diffusion magnetic resonance imaging (dMRI) and whole-brain tractography. Under this approach, grey matter regions, such as Brodmann areas, form the nodes of a network and tractography is used to construct a set of white matter fibre tracts which form the connections. Graph-theoretic measures may then be used to characterise patterns of connectivity. In this study, we measured the test-retest properties of such networks by varying several factors affecting network construction using ten healthy volunteers who underwent a dMRI protocol at 1.5 T on two separate occasions. High resolution T1-weighted brains were parcellated into regions-of-interest and network connections were identified using dMRI and two alternative tractography algorithms, two alternative seeding strategies, constraints on anatomical plausibility and three alternative network weightings. Test-retest performance was found to improve when: 1) seeding from white matter, rather than grey; and 2) using probabilistic tractography, rather than deterministic. In terms of network weighting, a measure of streamline density produced better test-retest performance than tract-averaged diffusion anisotropy, although it remains unclear which is most representative of the underlying axonal connections. These findings were then used to inform network construction for two further cohorts: a casecontrol analysis of 30 patients with amyotrophic lateral sclerosis (ALS) compared with 30 age-matched healthy controls; and a cross-sectional analysis of 80 healthy volunteers aged 25– 64 years. In both cases, networks were constructed using a weighting reflecting tract-averaged fractional anisotropy (FA). A mass-univariate statistical technique called network-based statistics, identified an impaired motor-frontal-subcortical subnetwork (10 nodes and 12 bidirectional connections), consistent with upper motor neuron pathology, in the ALS group compared with the controls. Reduced FA for three of the impaired network connections, which involved fibres of the cortico-spinal tract, were significantly correlated with the rate of disease progression. Cross-sectional analysis of the 80 healthy volunteers was intended to provide supporting evidence for the widely reported age-related decline in white matter integrity. However, no meaningful relationships were found between increasing age and impaired connectivity based on global, lobar and nodal network properties – findings which were confirmed with a conventional voxel-based analysis of the dMRI data. In conclusion, whilst current acquisition protocols and methods can produce networks capable of characterising the genuine between-subject differences in connectivity, it is challenging to measure subtle white matter changes, for example, due to normal ageing. We conclude that future work should be undertaken to address these concerns

Diffusion and Perfusion MRI in Paediatric Posterior Fossa Tumours

Brain tumours in children frequently occur in the posterior fossa. Most undergo surgical resection, after which up to 25% develop cerebellar mutism syndrome (CMS), characterised by mutism, emotional lability and cerebellar motor signs; these typically improve over several months. This thesis examines the application of diffusion (dMRI) and arterial spin labelling (ASL) perfusion MRI in children with posterior fossa tumours. dMRI enables non-invasive in vivo investigation of brain microstructure and connectivity by a computational process known as tractography. The results of a unique survey of British neurosurgeons’ attitudes towards tractography are presented, demonstrating its widespread adoption and numerous limitations. State-of-the-art modelling of dMRI data combined with tractography is used to probe the anatomy of cerebellofrontal tracts in healthy children, revealing the first evidence of a topographic organization of projections to the frontal cortex at the superior cerebellar peduncle. Retrospective review of a large institutional series shows that CMS remains the most common complication of posterior fossa tumour resection, and that surgical approach does not influence surgical morbidity in this cohort. A prospective case-control study of children with posterior fossa tumours treated at Great Ormond Street Hospital is reported, in which children underwent longitudinal MR imaging at three timepoints. A region-of-interest based approach did not reveal any differences in dMRI metrics with respect to CMS status. However, the candidate also conducted an analysis of a separate retrospective cohort of medulloblastoma patients at Stanford University using an automated tractography pipeline. This demonstrated, in unprecedented spatiotemporal detail, a fine-grained evolution of changes in cerebellar white matter tracts in children with CMS. ASL studies in the prospective cohort showed that following tumour resection, increases in cortical cerebral blood flow were seen alongside reductions in blood arrival time, and these effects were modulated by clinical features of hydrocephalus and CMS. The results contained in this thesis are discussed in the context of the current understanding of CMS, and the novel anatomical insights presented provide a foundation for future research into the condition

White matter connectivity, cognition, symptoms and genetic risk factors in Schizophrenia

Schizophrenia is a highly heritable complex neuropsychiatric disorder with a lifetime prevalence of around 1%. It is often characterised by impaired white matter structural dysconnectivity. In vivo and post-mortem alterations in white matter microstructure have been reported, along with differences in the topology of the structural connectome; overall these suggest a reduced communication between distal brain regions. Schizophrenia is characterised by persistent cognitive impairments that predate the occurrence of symptoms and have been shown to have a neural foundation reflecting aberrant brain connectivity. So far, 179 independent genome-wide significant single nucleotide polymorphisms (SNPs) have been associated with a diagnosis of schizophrenia. The high heritability and polygenicity of schizophrenia, white matter parameters and cognitive functions provides a great opportunity to investigate the potential relationships between them due to the genetic overlap shared among these factors. This work investigates the psychopathology of schizophrenia from a neurobiological, psychological and genetic perspective. The datasets used here include data from the Scottish Family Mental Health (SFMH) study, the Lothian Birth Cohort 1936 (LBC1936) and UK Biobank. The main goal of this thesis was to study white matter microstructure in schizophrenia using diffusion MRI (dMRI) data. Our first aim was to examine whether processing speed mediated the association between white matter structure and general intelligence in patients diagnosed with schizophrenia in the SFMH study. Secondly, we investigated specific networks from the structural connectome and their topological properties in both healthy controls and patients diagnosed with schizophrenia in the SFMH study. These networks were studied alongside cognition, clinical symptoms and polygenic risk factor for schizophrenia (szPGRS). The third aim of this thesis was to study the effects of szPGRS on the longitudinal trajectories of white matter connectivity (measured using tractography and graph theory metrics) in the LBC1936 over a period of three-years. Finally, we derived the salience network which has been previously associated with schizophrenia and examined the effect of szPGRS on the grey matter nodes associated with this network and their connecting white matter tracts in UK Biobank. With regards to the first aim, we found that processing speed significantly mediates the association between a general factor of white matter structure and general intelligence in schizophrenia. These results suggest that, as in healthy controls, processing speed acts as a key cognitive resource facilitating higher order cognition by allowing multiple cognitive processes to be simultaneously available. Secondly, we found that several graph theory metrics were significantly impaired in patients diagnosed with schizophrenia compared with healthy controls. Moreover, these metrics were significantly associated with intelligence. There was a strong tendency towards significance for a correlation between intelligence and szPGRS that was significantly mediated by graph theory metrics in both healthy controls and schizophrenia patients of the SFMH study. These results are consistent with the hypothesis that intelligence deficits are associated with a genetic risk for schizophrenia, which is mediated via the disruption of distributed brain networks. In the LBC1936 we found that higher szPGRS showed significant associations with longitudinal increases in MD in several white matter tracts. Significant declines over time were observed in graph theory metrics. Overall these findings suggest that szPGRS confer risk for ageing-related degradation of some aspects of structural connectivity. Moreover, we found significant associations between higher szPGRS and decreases in cortical thickness, in particular, in a latent factor for cortical thickness of the salience network. Taken together, our findings suggest that white matter connectivity plays a significant role in the disorder and its psychopathology. The computation of the structural connectome has improved our understanding of the topological characteristics of the brain’s networks in schizophrenia and how it relates to the microstructural level. In particular, the data suggests that white matter structure provides a neuroanatomical substrate for cognition and that structural connectivity mediates the relationship between szPGRS and intelligence. Additionally, these results suggest that szPGRS may have a role in age-related changes in brain structural connectivity, even among individuals who are not diagnosed with schizophrenia. Further work will be required to validate these results and will hopefully examine additional risk factors and biomarkers, with the ultimate aims of improving scientific knowledge about schizophrenia and conceivably of improving clinical practice

주의력 결핍/과잉행동장애의 신경 아형과 임상적 연관성

학위논문(석사) -- 서울대학교대학원 : 자연과학대학 뇌인지과학과, 2023. 2. 차지욱.Attention-deficit/hyperactivity disorder (ADHD) is one of childhoods most common neurodevelopmental disorders, typically characterized by inattention, impulsivity, and hyperactivity. Despite previous studies exploring brain abnormalities in ADHD, these studies have frequently compared ADHD to a control group, potentially overlooking the heterogeneity within ADHD. Given the challenge posed by the varying symptoms of ADHD in making accurate diagnoses and providing effective treatments, it is essential to understand the heterogeneity in ADHD. To this end, this study uncovered the heterogeneity of the structural brain in ADHD using unsupervised clustering modeling. The clustering model revealed two distinct groups of ADHD. Then, this study investigated the relationship between the identified ADHD subgroups and clinical characteristics in prepubertal children (ages 9-10 years old; the Adolescent Brain Cognitive Development study). Both subgroups showed higher levels of ADHD symptoms compared to non-ADHD individuals, but ADHD-2 had higher internalizing mood and genome-polygenic scores (GPSs) for bipolar disorder, BMI, and risk tolerance. The brain profiles of each subgroup showed that ADHD-1 had reduced cortical measures with only a few regions, while ADHD-2 had overall brain volume reductions and decreased surface area. Additionally, the longitudinal analysis revealed different developmental patterns, with ADHD-1 showing reductions in cortical and subcortical volume and ADHD-2 showing reduced cortical thickness. The findings suggest the possibility of different brain pathologies within ADHD and the need for further understanding to inform diagnostic strategies. In conclusion, this study sheds light on the heterogeneity of ADHD and the underlying brain differences between subgroups, providing insights for improved diagnostic and therapeutic approaches in the future.주의력 결핍/과잉행동 장애 (ADHD)는 아동기 가장 흔한 신경 발달 장애 중 하나로, 주의력 결핍, 충동, 과잉 행동을 특징으로 한다. ADHD 뇌에서의 구조적, 기능적 이상성은 대조군과 비교하여 발견되어 왔다. 그러나 이러한 접근은 ADHD내에서의 개인 변동성과 이질성을 반영하는데 어려움이 있다. 이를 해결하기 위해 본 연구에서는 감독되지 않은 클러스터링 모델을 사용하여 ADHD 뇌에서의 이질성을 분리하고, 분리된 하위 그룹이 서로 다른 임상적 특성과 관련되는지를 조사하고자 했다. 연구 결과, 클러스터링 모델은 두 개의 ADHD 하위 그룹을 밝혀냈다. 두 개의 ADHD 하위 그룹은 대조군과 비교하여 높은 ADHD 증상 수준을 보였지만, 양극성 장애, BMI, 위험 감수의 유전 점수와 내재화 기분 증상에 대해서는 ADHD-2 하위 그룹에서만 유의미한 높은 점수를 보였다. 각 하위 그룹의 뇌 프로파일에서는, ADHD-1은 일부 영역에서만 피질 측정치가 감소한 반면, ADHD-2는 전반적인 뇌 부피 및 표면적의 감소를 보였다. 종단 연구 결과에서는 ADHD-1은 피질 및 피질하 부피의 감소, ADHD-2 는 피질 두께의 감소를 주요 특징으로 하는 등 뇌 발달 과정에서의 패턴 차이를 보였다. 종합하면, 본 연구는 ADHD 뇌의 이질성과 하위 집단 간의 임상적 지표 및 뇌에서의 차이를 조명하여, 향후 진단 및 치료 접근법에 대한 통찰력을 제공한다.1. INTRODUCTION 1 1.1. Background 1 1.1.1. Attention-deficit/hyperactivity disorder (ADHD) 1 1.1.1.1. ADHD in childhood 1 1.1.1.2. Structural brain abnormalities in ADHD 2 1.1.1.3. Genetic influences on ADHD 4 1.1.2. Heterogeneity in ADHD 5 1.2. Purpose of Research 6 2. Materials and Methods 7 2.1. Participants 7 2.2. ADHD 8 2.2.1. ADHD assessment 8 2.2.2. Comorbid disorders 9 2.2.3. Medication treatment 11 2.3. Neuropsychological measures 12 2.3.1. Cognitive measures 12 2.3.2. Behavioral measures 13 2.4. Missing data imputation 14 2.5. MRI data acquisition and processing 15 2.5.1. Structural magnetic resonance imaging (sMRI) 15 2.5.2. Diffusion magnetic resonance imaging (dMRI) 16 2.5.3. Quality assessment and control 16 2.6. Genetic data acquisition and processing 17 2.6.1. Genotype data 17 2.6.2. Genetic relatedness inference 18 2.6.3. Genome-wide polygenic scores (GPSs) 18 2.7. Dissecting the heterogeneity of the brain structure in ADHD 19 2.7.1. Dimensionality reduction 19 2.7.2. Agglomerative hierarchical clustering analysis 20 2.8. Relation to ADHD subgroups and neuropsychological measures 20 3. Results 22 3.1. Demographic characteristics 22 3.2. Dissecting the heterogeneity of the ADHD brain 24 3.3. Relation to ADHD subgroups and demographic, cognitive and behavioral measures 26 3.4. Relation to ADHD subgroups and GPS measures 31 3.5. Relation to ADHD subgroups and brain measures 34 3.6. Developmental changes of each ADHD subgroup 38 4. DISCUSSION 42 4.1. Summary 42 4.2. Implication and perspective 43 4.3. Limitations and future research direction 45 4.4. Conclusion 47 CONTRIBUTION 48 BIBLIOGRAPHY 49 국문초록 61 ACKNOWLEDGMENT 62석