Hypertension is associated with reduced hippocampal connectivity and impaired memory

Background: The objective was a large-scale analysis of the relation between hypertension, memory problems, and brain function. Methods: The study design was to measure the association between a history of hypertension, and the functional connectivity between 94 brain regions, and prospective and numeric memory, in 19,507 participants from the UK Biobank, with cross-validation in 1,002 participants in the Human Connectome Project, and 13,441 individuals in the second release of the UK Biobank. A history of hypertension was measured by whether individuals were admitted to hospital for the treatment of hypertension, with the control group admissions for other reasons. Findings: A history of hypertension was associated with reduced functional connectivity of the hippocampus, and with reduced prospective memory score (FDR correction p<0.01). The reduced functional connectivity mediated the association between the hypertension history and the prospective memory score. A graded linear relation between both the hippocampal functional connectivity and memory impairment, was found across a wide range of blood pressure (r=-0.04). In 502,537 participants from the UK Biobank, a history of hypertension was associated with impaired prospective memory (p = 9.1 × 10−41, Cohen's d=-0.08) and numeric memory (p = 4.7 × 10−24, Cohen's d=-0.10). The association between hypertension, functional connectivity, and impaired memory was cross-validated with 1,002 participants from the Human Connectome Project; and for functional connectivity in 13,441 individuals in the second release of the UK Biobank imaging dataset. Interpretation: The reduced functional connectivity of the hippocampus, and the memory impairments, both related to hypertension across a wide range of blood pressure, are important for clinical practice

Preliminary prediction of individual response to electroconvulsive therapy using whole-brain functional magnetic resonance imaging data.

Electroconvulsive therapy (ECT) works rapidly and has been widely used to treat depressive disorders (DEP). However, identifying biomarkers predictive of response to ECT remains a priority to individually tailor treatment and understand treatment mechanisms. This study used a connectome-based predictive modeling (CPM) approach in 122 patients with DEP to determine if pre-ECT whole-brain functional connectivity (FC) predicts depressive rating changes and remission status after ECT (47 of 122 total subjects or 38.5% of sample), and whether pre-ECT and longitudinal changes (pre/post-ECT) in regional brain network biomarkers are associated with treatment-related changes in depression ratings. Results show the networks with the best predictive performance of ECT response were negative (anti-correlated) FC networks, which predict the post-ECT depression severity (continuous measure) with a 76.23% accuracy for remission prediction. FC networks with the greatest predictive power were concentrated in the prefrontal and temporal cortices and subcortical nuclei, and include the inferior frontal (IFG), superior frontal (SFG), superior temporal (STG), inferior temporal gyri (ITG), basal ganglia (BG), and thalamus (Tha). Several of these brain regions were also identified as nodes in the FC networks that show significant change pre-/post-ECT, but these networks were not related to treatment response. This study design has limitations regarding the longitudinal design and the absence of a control group that limit the causal inference regarding mechanism of post-treatment status. Though predictive biomarkers remained below the threshold of those recommended for potential translation, the analysis methods and results demonstrate the promise and generalizability of biomarkers for advancing personalized treatment strategies

Robust prediction of individual creative ability from brain functional connectivity

People’s ability to think creatively is a primary means of technological and cultural progress, yet the neural architecture of the highly creative brain remains largely undefined. Here, we employed a recently developed method in functional brain imaging analysis—connectome-based predictive modeling—to identify a brain network associated with high-creative ability, using functional magnetic resonance imaging (fMRI) data acquired from 163 participants engaged in a classic divergent thinking task. At the behavioral level, we found a strong correlation between creative thinking ability and self-reported creative behavior and accomplishment in the arts and sciences (r = 0.54). At the neural level, we found a pattern of functional brain connectivity related to high-creative thinking ability consisting of frontal and parietal regions within default, salience, and executive brain systems. In a leave-one-out cross-validation analysis, we show that this neural model can reliably predict the creative quality of ideas generated by novel participants within the sample. Furthermore, in a series of external validation analyses using data from two independent task fMRI samples and a large task-free resting-state fMRI sample, we demonstrate robust prediction of individual creative thinking ability from the same pattern of brain connectivity. The findings thus reveal a whole-brain network associated with high-creative ability comprised of cortical hubs within default, salience, and executive systems—intrinsic functional networks that tend to work in opposition—suggesting that highly creative people are characterized by the ability to simultaneously engage these large-scale brain networks

Brain Training and Meditation’s Effects on Memory in Subjects with Vascular Cognitive Impairment

Vascular Dementia (VaD) is an important public health concern, which causes significant morbidity and mortality amongst populations around the world. With the increases in average age of individuals and prevalence of cardiovascular risk factors, the incidence of vascular cognitive impairment (VCI) and VaD are on the rise. Most of this increase will come from cerebral small vessel disease (CSVD) as treatment for large vessel disease improves. Yet, very few interventions are recommended for CSVD beyond control of risk factors. In this thesis, we propose a non-pharmacological intervention, which we believe may address executive dysfunction in VCI due to CSVD. CSVD impairs functional frontal-subcortical connectivity and results in cognitive and functional impairments. Given the plasticity in these circuits, despite old age, cognitive training may be a good candidate for improving cognition in CSVD. However, previous studies have suffered from heterogeneity of pathologies in VCI by including both large and small vessel disease. Furthermore, they have often not considered the effects of anxiety and depression, which we aim to exclude from the study. Finally, these studies do not use validated composite scores as a primary endpoint and currently do not use any biomarkers to follow the progress of subjects. In this study, we aim to partially address these shortcomings and offer a more rigorous approach to cognitive training

Individual-based morphological brain network organization and its association with autistic symptoms in young children with autism spectrum disorder

Individual-based morphological brain networks built from T1-weighted magnetic resonance imaging (MRI) reflect synchronous maturation intensities between anatomical regions at the individual level. Autism spectrum disorder (ASD) is a socio-cognitive and neurodevelopmental disorder with high neuroanatomical heterogeneity, but the specific patterns of morphological networks in ASD remain largely unexplored at the individual level. In this study, individual-based morphological networks were constructed by using high-resolution structural MRI data from 40 young children with ASD (age range: 2-8 years) and 38 age-, gender-, and handedness-matched typically developing children (TDC). Measurements were recorded as threefold. Results showed that compared with TDC, young children with ASD exhibited lower values of small-worldness (i.e., sigma) of individual-level morphological brain networks, increased morphological connectivity in cortico-striatum-thalamic-cortical (CSTC) circuitry, and decreased morphological connectivity in the cortico-cortical network. In addition, morphological connectivity abnormalities can predict the severity of social communication deficits in young children with ASD, thus confirming an associational impact at the behavioral level. These findings suggest that the morphological brain network in the autistic developmental brain is inefficient in segregating and distributing information. The results also highlight the crucial role of abnormal morphological connectivity patterns in the socio-cognitive deficits of ASD and support the possible use of the aberrant developmental patterns of morphological brain networks in revealing new clinically-relevant biomarkers for ASD.China Postdoctoral Science Foundation, Grant/Award Number: 2019M660236; National Natural Science Foundation of China, Grant/Award Numbers: 61901129, 62036003, 81871432, U1808204; The Basque Foundation for Science and from Ministerio de Economia, Industria y Competitividad (Spain) and FEDER, Grant/Award Number: DPI2016-79874-R; the Fundamental Research Funds for the Central Universities, Grant/Award Numbers: 2672018ZYGX2018J079, ZYGX2019Z017; the Sichuan Science and Technology Program, Grant/Award Number: 2019YJ018

Beyond fingerprinting: Choosing predictive connectomes over reliable connectomes

Recent years have seen a surge of research on variability in functional brain connectivity within and between individuals, with encouraging progress toward understanding the consequences of this variability for cognition and behavior. At the same time, well-founded concerns over rigor and reproducibility in psychology and neuroscience have led many to question whether functional connectivity is sufficiently reliable, and call for methods to improve its reliability. The thesis of this opinion piece is that when studying variability in functional connectivity—both across individuals and within individuals over time—we should use behavior prediction as our benchmark rather than optimize reliability for its own sake. We discuss theoretical and empirical evidence to compel this perspective, both when the goal is to study stable, trait-level differences between people, as well as when the goal is to study state-related changes within individuals. We hope that this piece will be useful to the neuroimaging community as we continue efforts to characterize inter- and intra-subject variability in brain function and build predictive models with an eye toward eventual real-world applications

The human mediodorsal thalamus: Organization, connectivity, and function

The human mediodorsal thalamic nucleus (MD) is crucial for higher cognitive functions, while the fine anatomical organization of the MD and the function of each subregion remain elusive. In this study, using high-resolution data provided by the Human Connectome Project, an anatomical connectivity-based method was adopted to unveil the topographic organization of the MD. Four fine-grained subregions were identified in each hemisphere, including the medial (MDm), central (MDc), dorsal (MDd), and lateral (MDl), which recapitulated previous cytoarchitectonic boundaries from histological studies. The subsequent connectivity analysis of the subregions also demonstrated distinct anatomical and functional connectivity patterns, especially with the prefrontal cortex. To further evaluate the function of MD subregions, partial least squares analysis was performed to examine the relationship between different prefrontal-subregion connectivity and behavioral measures in 1012 subjects. The results showed subregion-specific involvement in a range of cognitive functions. Specifically, the MDm predominantly subserved emotional-cognition domains, while the MDl was involved in multiple cognitive functions especially cognitive flexibility and inhibition. The MDc and MDd were correlated with fluid intelligence, processing speed, and emotional cognition. In conclusion, our work provides new insights into the anatomical and functional organization of the MD and highlights the various roles of the prefrontal-thalamic circuitry in human cognition

Connectome-Based Predictive Modeling of Individual Anxiety

Anxiety-related illnesses are highly prevalent in human society. Being able to identify neurobiological markers signaling high trait anxiety could aid the assessment of individuals with high risk for mental illness. Here, we applied connectome-based predictive modeling (CPM) to whole-brain resting-state functional connectivity (rsFC) data to predict the degree of trait anxiety in 76 healthy participants. Using a computational "lesion" approach in CPM, we then examined the weights of the identified main brain areas as well as their connectivity. Results showed that the CPM successfully predicted individual anxiety based on whole-brain rsFC, especially the rsFC between limbic areas and prefrontal cortex. The prediction power of the model significantly decreased from simulated lesions of limbic areas, lesions of the connectivity within limbic areas, and lesions of the connectivity between limbic areas and prefrontal cortex. Importantly, this neural model generalized to an independent large sample (n = 501). These findings highlight important roles of the limbic system and prefrontal cortex in anxiety prediction. Our work provides evidence for the usefulness of connectome-based modeling in predicting individual personality differences and indicates its potential for identifying personality factors at risk for psychopathology

The role of cortical morphometry of functional networks in predicting age-related cognition in older adults

Over the next three decades, the 65-and-over population is projected to nearly double, increasing from 8.5% to 16.7% of the world’s total population (He, Goodkind, and Kowal, 2016). Alarmingly, despite longer life expectancies, health is not necessarily improving (He, Goodkind, and Kowal, 2016). While all organs are affected by aging, decline in the brain’s ability to function (cognitive aging) is one of the most impactful consequences of aging on day-to-day activities and one of the most common complaints of older adults (Blazer et al., 2015). In fact, in a recent survey, almost half of individuals aged 65 or older report changes in mental ability (AARP Brain Health Survey, Fall 2015). While almost all older adults acknowledge the importance of brain health, only half actually engage in activities found to be beneficial for brain health (AARP Brain Health Survey, Fall 2015). Thus, understanding individual variability in the older adult brain, both in terms of structure and function, and its relationship with cognition and age is essential (Hedden and Gabrieli 2004). Despite the well-established widespread relationships of age and cognition with cortical structure, the nature and organization of this relationship remains underspecified. In this thesis, I investigate the nature of the relationships between cortical morphometry, cognition, and age in older adults through a contemporary neuroscience lens of the brain as a system of functional networks. In chapter one, I employ a widely-used functional network architecture as the organizing principle of the cortex to investigate how the cortical morphometry of individual networks predicts cognition and mediates the age-cognition relationship in older adults (using both cortical thickness and surface area—phenotypes both implicated in relationships with cognition but not tested in the same sample of older adults). I use a machine learning and cross-validation prediction framework to compare the predictive ability of cortical morphometry of individual functional networks to age-related cognitive abilities (declarative memory and executive function). In a second set of analyses, I apply a novel inferential test to exploratory, whole brain analyses. Specifically, I examine the number of significant point-by-point regional associations within functional networks, providing a test of the spatial extent of each functional network’s relationship with age-related cognitive abilities (compared to chance). Ultimately, making impactful theoretical and practical contributions to the field requires assessing the reproducibility and generalizability of conclusions derived from data-driven techniques. Thus, in chapter 2, I test if regions robustly associated with cognitive ability (executive function) discovered in chapter 1 and regions associated with cognitive task performance discovered in a previous study (Sun et al., 2016) predict well-established cognitive reference abilities in an independent sample of older adults. General patterns of functional connectivity (i.e., group-average functional networks) across a population(s), such as the one used in Chapter 1, provide a picture of the common functional architecture and distinct functional networks across the cortex of healthy adults (i.e., Yeo et al., 2011). These group-based networks of the functional connectome were used to assess the importance of cortical structure of functional networks in Chapter 1. However, this ignores individual differences in the integrity of these functional networks and how these individual differences relate to individual differences in cortical structure. If functional connectivity causes (or is caused by) differences in mechanisms marked by cortical structure or vice versa (e.g., individual variability in older adults’ cortical thickness may be indexing the number of synapses or intracortical myelin important for connectivity between regions as is theorized in previous studies; see Fjell et al., 2015), one would expect the two to be related and share overlapping variance in their relationship with age and cognition. Thus, in chapter 3, I examine whether individual differences in functional connectivity mediates the relationship of cortical structure with age and cognitive ability (as the relationship of structure with cognition emerges as a result of the functional system measured by functional connectivity)