Personalized connectome fingerprints: Their importance in cognition from childhood to adult years

Structural neural network architecture patterns in the human brain could be related to individual differences in phenotype, behavior, genetic determinants, and clinical outcomes from neuropsychiatric disorders. Recent studies have indicated that a personalized neural (brain) fingerprint can be identified from structural brain connectomes. However, the accuracy, reproducibility and translational potential of personalized fingerprints in terms of cognition is not yet fully determined. In this study, we introduce a dynamic connectome modeling approach to identify a critical set of white matter subnetworks that can be used as a personalized fingerprint. Several individual variable assessments were performed that demonstrate the accuracy and practicality of personalized fingerprint, specifically predicting the identity and IQ of middle age adults, and the developmental quotient in toddlers. Our findings suggest the fingerprint found by our dynamic modeling approach is sufficient for differentiation between individuals, and is also capable of predicting general intellectual ability across human development. © 2020 The AuthorsSignificance Statement We demonstrate that white matter connections obtained from high resolution medical imaging data form a personalized fingerprint is capable of estimating individual identity and neurodevelopmental variables across human life-span. This important finding provides strong evidence to support the concept of neurological identity and function through human brain connectome mapping

Structural and functional neural correlates of a mind-body connection.

The human brain underlies the complex cognitive processes of the mind, however, this is dependent upon the physiological processes of the body in order to receive adequate energy, oxygen, and blood flow. Therefore, physical measurements such as body mass index (BMI) and indices of cognitive functioning, such as intelligence, may be related via common neural features. Current analyses assessed morphometric differences in cortical and subcortical grey matter regions, white matter structural integrity, and resting-state functional activation in order to determine what combinations of neural variables predict BMI and intelligence (Wechsler Abbreviated Scale of Intelligence; WASI) with the best degree of accuracy. Data for eighty-five subjects was obtained from the Nathan Kline Institute, in connection with the 1000 Functional Connectomes neuroimaging database. Behavioral results indicated a negative correlation between BMI and WASI scores. Neural analyses revealed that increased BMI predicted changes in a frontolimbic network comprised of the anterior cingulate cortex, amygdala, and uncinate fasciculus, as well as increased cortical surface area of the left fusiform gyrus. These results indicate a relationship of BMI with emotional decision-making and visual recognition processes. Whereas, increased WASI scores predicted increased thickness and volume of prefrontal and parietal cortices, which reflect brain regions involved in the fronto-parietal attentional network. As well, increased WASI scores also related to a functional network that included increased activation of the postcentral gyrus and posterior hippocampal complex, regions involved with attention and memory. Taken together, these results indicate that BMI and intelligence are behaviorally anticorrelated, yet mediated by separate neuroanatomical substrates that associate with a variety of cognitive functioning measures

TheRelationship between brain network organization and variability in episodic memory outcomes and abilities:

Thesis advisor: Maureen RitcheyThesis advisor: Elizabeth KensingerOur brains afford us the remarkable ability to remember past events from our lives, to travel back in time in our minds' eye and relive our memories anew. What are the brain processes that support this ability? In this thesis I investigated this question across three experiments. In Chapter 1, I examined how the brain regions previously linked to episodic cognition (i.e., the hippocampus, parahippocampal cortex, retrosplenial cortex, posterior cingulate cortex, precuneus, angular gyrus, and medial prefrontal cortex) support recollection by building a model that incorporates both region-specific and network-level contributions. I found that these brain regions form ventral and dorsal subnetworks and that their contributions to recollection outcomes are largely explained by subnetwork-level rather than region-specific engagement. In Chapter 2, I used an openly available MRI dataset to test whether individual differences in functional connectivity were related to individual differences in memory ability, finding that network connectivity outside of the classic episodic networks supports individual differences in our ability to remember. In Chapter 3, I tested a neuroscience inspired hypothesis that individuals would have different capacities to bind their memories around social-emotional and visual-spatial content, ultimately finding inconclusive evidence for or against my hypothesis. Together, these results help to solidify our understanding of the brain as an interconnected network of brain regions and shed new light on how these networks support individual differences in memory.Thesis (PhD) — Boston College, 2023.Submitted to: Boston College. Graduate School of Arts and Sciences.Discipline: Psychology

Image acquisition and quality assurance in the Boston Adolescent Neuroimaging of Depression and Anxiety study

The Connectomes Related to Human Diseases (CRHD) initiative was developed with the Human Connectome Project (HCP) to provide high-resolution, open-access, multi-modal MRI data to better understand the neural correlates of human disease. Here, we present an introduction to a CRHD project, the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) study, which is collecting multimodal neuroimaging, clinical, and neuropsychological data from 225 adolescents (ages 14–17), 150 of whom are expected to have a diagnosis of depression and/or anxiety. Our transdiagnostic recruitment approach samples the full spectrum of depressed/anxious symptoms and their comorbidity, consistent with NIMH Research Domain Criteria (RDoC). We focused on an age range that is critical for brain development and for the onset of mental illness. This project sought to harmonize imaging sequences, hardware, and functional tasks with other HCP studies, although some changes were made to canonical HCP methods to accommodate our study population and questions. We present a thorough overview of our imaging sequences, hardware, and scanning protocol. We detail similarities and dif-ferences between this study and other HCP studies. We evaluate structural-, diffusion-, and functional-image-quality measures that may be influenced by clinical factors (e.g., disorder, symptomatology). Signal-to-noise and motion estimates from the first 140 adolescents suggest minimal influence of clinical factors on image quality. We anticipate enrollment of an additional 85 participants, most of whom are expected to have a diagnosis of anxiety and/or depression. Clinical and neuropsychological data from the first 140 participants are currently freely available through the National Institute of Mental Health Data Archive (NDA)

Towards an Ontology of Ongoing Thought

Functional outcomes of ongoing thought show both costs and benefits. Yet, the reason for its heterogeneity remains unclear. The executive failure and representational accounts stemmed from different psychological research approaches to understand ongoing thought. The executive failure account examines why changes in ongoing thought happen, while the representational account seeks to explain how humans generate ongoing thought. The attentional system and the default mode network are the common neural processes of both theoretical accounts, but interacting in a contradicting manner. The two accounts can be seen as competing theories of ongoing thought. However, in the family resemblance view (Seli et al., 2018), the two theoretical accounts potentially serve as two component processes of one phenomenon. One possible solution to this conflict could be that under different global neural configurations, the two networks support different cognitive functions. The thesis sets out to present evidence supporting of the family resemblance view and to begin research on the ontology of the component processes in ongoing thought. Neural cognitive hierarchy is the potential explanation of the heterogeneity. The current thesis adopts sparse canonical correlation analysis to incorporate the neural and behavioural aspects of ongoing thought. The data suggests ongoing thought is a collective phenomenon with many types of experience driven by the connectivity patterns in the default mode network. Each type of experience associated with their unique functional outcomes and neural hierarchies at the whole-brain level. Cognitive flexibility and the balance of segregation and integration between the transmodal systems and the rest of the cortex determines the immersive details. The current findings suggested the importance of whole-brain neural hierarchies to ongoing thought. The confirmation of these trait level findings at a state level are necessary to gain more insights into the architecture of the component processes

Alterations in Brain Network Topology and Structural-Functional Connectome Coupling Relate to Cognitive Impairment

According to the network-based neurodegeneration hypothesis, neurodegenerative diseases target specific large-scale neural networks, such as the default mode network, and may propagate along the structural and functional connections within and between these brain networks. Cognitive impairment no dementia (CIND) represents an early prodromal stage but few studies have examined brain topological changes within and between brain structural and functional networks. To this end, we studied the structural networks [diffusion magnetic resonance imaging (MRI)] and functional networks (task-free functional MRI) in CIND (61 mild, 56 moderate) and healthy older adults (97 controls). Structurally, compared with controls, moderate CIND had lower global efficiency, and lower nodal centrality and nodal efficiency in the thalamus, somatomotor network, and higher-order cognitive networks. Mild CIND only had higher nodal degree centrality in dorsal parietal regions. Functional differences were more subtle, with both CIND groups showing lower nodal centrality and efficiency in temporal and somatomotor regions. Importantly, CIND generally had higher structural-functional connectome correlation than controls. The higher structural-functional topological similarity was undesirable as higher correlation was associated with poorer verbal memory, executive function, and visuoconstruction. Our findings highlighted the distinct and progressive changes in brain structural-functional networks at the prodromal stage of neurodegenerative diseases

Brain Structural Maturation and Cognitive Abilities in Early Life

The first two years of life mark the most dynamic period of postnatal brain maturation, during which time cortical expansion and myelination reach peak developmental rates. Cortical morphology and white matter (WM) microstructure have been linked to cognition in older adults and children, yet we know remarkably little about how the brain matures to support emergent cognition. This is a critical gap in knowledge, as the first years of life mark a sensitive period in child development when atypical brain and behavioral phenotypes may become apparent. In this report, we examined cortical thickness (CT), surface area (SA), and WM fiber integrity in 450 typically-developing children at birth, age 1, and age 2 in association with assessments of motor, language, and general cognitive abilities at ages 1 and 2. Results revealed that generally thicker, larger cortices and more mature WM tract properties in early life related to better performance on cognitive tasks, suggesting that increased synaptogenesis, elaborations in dendritic arborization, and myelination may confer benefits for infant cognitive development. We found several expected brain-cognition relationships, with CT in regions associated with motor planning and execution and regions associated with language processing and production related to motor and language scores, respectively. Results between cognition and WM integrity were less specific, with tract properties across many fibers spanning the brain relating to cognition across domains. This finding, along with the fact that the majority of significant WM results were of a predictive nature, prompted further study into the organization of WM at birth and future outcomes. Using a deep learning approach, we successfully predicted 2-year cognitive outcomes using WM connectivity patterns at birth. Taken together, these results suggest that cortical structure and the organization and microstructural integrity of WM pathways at birth serve as a foundation upon which subsequent fine-tuning of brain structure takes place to support emergent cognition in infancy and toddlerhood. These findings offer novel insight into how prenatal and postnatal brain structural maturation support infant and toddler cognitive abilities and fills important gaps in our current understanding of the neurobiology of emergent language, motor, and cognitive abilities in early life.Doctor of Philosoph