Research Focused on the
Large-Scale Functional Organization of the Human Brain
Holmes Laboratory researchers are focused on discovering the fundamental organization of large-scale human brain networks. A core motivation that drives this work is the search for specific network-level signatures, or “fingerprints”, that co-vary with heritable behavioral variation in the general population and mark vulnerability for psychiatric illness onset.
The human brain is composed of a complex web of interdigitated functional networks. Substantial progress has been made delineating aspects of this intricate architecture, yet there are many gaps in our understanding of how the brain’s functional properties influence behaviors across health and disease. Recent technological advances allow us to study network function in a rapid and non-invasive manner. Leveraging associated large-scale data-collection initiatives, researchers in our lab work to characterize the organization of functional brain networks across childhood and adolescence through adulthood, developing analytic approaches that map cortical network topographies within groups and down to the level of a single person. Information gained through these approaches is applied to the study of individual differences and risk for the development of psychiatric illness.
The ability to pursue goals through an ever shifting and uncertain environment is core to human survival. To accomplish this, the brain must integrate internal and external stimuli across multiple time scales. Prior work based on descriptions of discrete moments during reward anticipation and consumption has greatly advanced understanding of the cortico-striatal circuits that support motivated behavior. However, relatively little is known about the dynamic neural mechanisms that underlie sustained goal pursuit in humans. In order to inform our understanding of how motivated behavior is directed and maintained over time, researchers in our lab are studying the processes through which dynamic goal monitoring is instantiated in the brain.
Human thought and behavior emerge from the integration and coordination of distributed brain networks. Traditional static descriptions of network coupling have provided foundational discoveries, characterizing core aspects of brain function. However, the brain is a dynamic system and recent work has demonstrated time-varying fluctuations in network coherence. A current line of work in our lab explores the dynamic aspects of network function. The study of dynamic flexibility in the coordination of neural systems has the potential to improve our understanding of how the brain adaptively supports state dependent shifts in behavior.
Identical experiences can produce drastically different behavioral and emotional responses across time, space and person. While these responses arise in a seemingly effortless manner, the ease with which they are experienced masks the intricate neural circuitry supporting their expression and regulation. A fundamental question facing researchers is how such marked behavioral variability is instantiated in the brain. Additionally, although individual differences in brain structure and function are readily observable in the general population, open questions surround both their origins and functional significance. While some variance may reflect the gradual accumulation of a lifetime of experience, other differences are highly heritable, suggesting a strong genetic component. Ongoing work in our lab seeks to establish reliable links between genetic variation, system-level brain function, and behavior in the general population.
Constructing an integrated picture of brain functioning requires understanding how genetic and molecular factors propagate upwards, through cells, circuits, and networks to influence behavior and cognition. Leveraging human and non-human primate transcriptional atlases, our laboratory has pioneered the characterization of cross-scale interactions that link genetic, molecular, cellular, and macroscale levels of brain functioning, establishing techniques to map spatial profiles of gene expression (mRNA transcription) among anatomical regions and functional networks. In doing so, discovering synchronized genetic signatures that recapitulate the distributed spatial topography of large-scale networks, nominating hundreds of potential cellular and molecular mechanisms supporting brain functioning and influencing risk for schizophrenia and depression.
Population-level variability in cognition and behavior likely reflect shifts in the network architecture of individuals, particularly in the higher order association regions. Mapping functional network topography down to the level of a single person is critical for clinical intervention and the study of individual differences. Ongoing work in our lab explores functional and structural network organization at the individual level. Here, our research group has established a comprehensive mapping of heritability across the collective set of functional connections in the human brain, providing evidence that genetic factors influence the size and spatial organization of individual-specific network topographies. The capacity to identify the unique functional architecture of a subject’s brain is a critical step towards personalized medicine as well as understanding the neural basis of variations in human cognition and behavior. It is our hope that the knowledge gained through this approach can provide novel biological targets for therapeutic interventions and predictive markers of clinical course.
The brain’s exquisite complexity allows for the staggering diversity of human behavior. However, the evolutionary forces that enabled complex cognitive functions have also pushed our capabilities to their limits, increasing vulnerability for psychiatric illness. Using our understanding of the genetic and brain bases of individual differences in behavior as a foundation, we are exploring links between disturbances in network organization and clinical presentation in patient populations. Recent work from our group focused on the manner in which functional connectivity and network coherence is altered in the presence of affective and psychotic illnesses, emphasizing brain networks believed to support core cognitive control functions. Ongoing projects seek to characterize the discrete and transdiagnostic features of schizophrenia, bipolar disorder, anxiety, depression and how these differ from healthy functioning. Through this approach we hope to facilitate earlier and more accurate diagnoses, improve treatment outcomes, and enable the detection of illness risk in healthy populations
One essential aspect of cognition is the ability to flexibly modify behaviors based on negative outcomes and unexpected changes in the environment. These core cognitive control processes are supported through the function of a distributed network of interconnected regions that encompass the anterior cingulate and the dorsolateral prefrontal cortex. Patients with depression display deficits in cognitive control, including difficulty coping with change and abnormal reactions to errors and negative feedback. Prior work from our lab established a rapid downward spiral in performance following error commission, an inability to account for shifting task incentives, and dysregulated fronto-cingulate interactions in patients with depression. Ongoing studies are exploring the diagnostic specificity of cognitive control impairments across psychiatric populations. Additional lines of research examine relations between impaired cognitive control system functioning and illness risk.
Psychiatric diagnoses describe clusters of frequently co-occurring symptoms that recur across multiple diagnostically distinct illnesses. The issues inherent in this complexity are particularly evident when considering the gaps in our knowledge of illness etiology. For instance, impaired frontoparietal network integrity has been observed in bipolar disorder and schizophrenia. However, it is not yet clear if this reflects a specific vulnerability factor, or a general marker, for psychiatric illnesses with an affective component. In responding to these challenges, our lab is seeking to characterize the shared and unique features of network function that track symptom severity in unipolar depression, bipolar depression, and schizophrenia. The information gained through this research will be applied to the study neurogenetic factors that influence clinical course and risk for illness onset.
Over one third of adults will experience an anxiety or mood disorder in their lifetime, making these illnesses a leading cause of global disease burden. Chronic or severe anxiety and negative affect predict subsequent clinical diagnoses, but many people with such symptoms never develop full-blown anxiety or mood disorders. This suggests that diagnosable illnesses lie at the extreme end of a continuum encompassing health and disease. Despite this possibility, research on the neuroanatomical basis of negative affect has most often focused on animal models or clinically ill patients. While this approach has been highly productive, the importance of establishing the predictive utility of brain phenotypes during the prodromal stages preceding illness onset has been overlooked. Current work in our lab studies relations linking brain structure and function with illness risk in healthy young adults. Through this work we seek to establish which neurobiological markers of affective illness reflect current episode severity, the cumulative effects of a chronic course, or premorbid trait vulnerabilities.