What Underlying Brain Dysconnectivity Causes Mental Illness?

Paradigm-shifting research on adolescents is a game-changer in mental health.

Posted Feb 07, 2018

I can see the beauty of glass objects fully at the moment when they slip from my hand. 
― Andrew Solomon

Neuroscience is making great strides toward understanding mental illness and well-being from the perspective of brain networks by studying the “connectome”―following on the term "genome"―the characteristic pattern of structural and functional interconnections among different brain regions. Beyond understanding what individual brain areas do, understanding the connectome means gleaning a whole-brain perspective, grasping systemic patterns of brain function which ultimately give rise to mind, personality, health, pathology and social relations.

The depth of complexity we are just beginning to grasp is opening up avenues for diagnosis, therapeutics and human enhancement which are transforming the human experience as well as informing machine-learning and artificial intelligence. Conversely, it is only through advances in mathematical modeling, computational techniques and machine-learning, and neuroimaging and genetics, that we are able meaningfully to capture the intricacies of brain function and how brain function relates to mental life and actual behavior. By understanding the connectome in health, and identifying aberrant patterns of connectivity as markers of illness (“dysconnectivity”), we have passed the threshold of a profound paradigm shift in how we understand ourselves.

Laying the foundations

In 2012, Buckholtz and Meyer-Lindenberg, pulling together many prior research threads, suggested a “transdiagnostic” model of mental illness based on the possibility that genetic and environmental risk factors could lead to brain dysconnectivity and corresponding cognitive and emotional problems. Developing a proven transdiagnostic model based on connectome and genetic studies correlating with clinical symptoms would provide a robust and novel platform for understanding mental illness, allowing for early detection and prevention, accurate diagnosis, and more effective treatment.

In 2015, Smith and colleagues published key research showing that participants from the Human Connectome Project could be sorted along a “positive-negative” continuum by correlating a pattern of dysconnectivity (a “CCA mode”) they derived from data on lifestyle, demographics and psychological function, and neuroimaging results. In 2017, Kaufmann and colleagues showed that not only does the human connectome move toward a stable pattern during adolescent brain development, but that delays in this process are associated with poorer mental health. Adolescence is a key developmental stage during which the brain is highly plastic, undergoing rapid and dramatic changes in connectivity. Adolescence is a period of vulnerability for mental illness, when environmental factors interact with genetic and neurodevelopmental factors to lead to health or illness in adulthood. Understanding what is happening in the adolescent brain is an important first step for developing effective approaches to mitigate risk and secure future well-being.

Mapping disconnectivity

Building on this prior work, Alnæs and colleagues (2018) in Norway, working with the Philadelphia Neurodevelopment study team, looked more deeply into the relationship between dysconnectivity and mental illness in adolescents. Using fMRI (functional magnetic resonance imaging) brain scans from 748 young people ranging in age from 8.7 to 22.6 years, with clinical data from the majority of participants, they sought to find out whether there was a specific pattern of dysconnectivity associated with mental illness, and how that varied with age and development.

In addition to functional neuroimaging data, they looked at cognitive performance using computerized tests measuring executive control, working memory, episodic memory, verbal and non-verbal reasoning, and social cognition. They looked at clinical factors including mood and anxiety symptoms, eating issues, behavioral problems, and psychotic symptoms. From these data, they derived a general measure of psychopathology while also looking at specific symptom categories. They genotyped participants, including genome-wide data in their analysis.

Their findings have profound implications. First, they identified seven clusters of clinical symptoms, including: 1) attention problems; 2) anxiety; 3) norm-violating behaviors; 4) active (“positive”) psychotic and prodromal (predisposition to psychosis) symptoms; 5) negative psychotic symptoms, depression and suicide; 6) mania; and 7) obsessive-compulsive symptoms. These seven symptom domains correlated closely with the general psychopathology factor. They found that changes in brain structure and function correlated accurately with age in years, important for developing accurate models which, for example, could guide future clinical application. Furthermore, genetics accounted for close to 20 percent of variations in cognition and psychopathology.

Most significantly, of the 20 clusters of connectivity identified, only one, LICA-09 (“linked independent component analysis”―see at the end of this post for a detailed analysis) was highly correlated with both the general psychopathology factor and the general cognitive factor. LICA-09 can be thought of as a consistent “brainprint” (like a fingerprint), which is a marker for mental illness and cognitive problems. Differences in LICA-09, furthermore, correlated with better cognitive performance and more severe psychopathology, and were consistent across younger and older participants, showing stability over developmental time.

Alnæs et al., 2018
LICA-09 Brain Maps
Source: Alnæs et al., 2018

LICA-09 consists of two affected white-matter pathways (the brain’s “wiring”, which appears lighter because of a fatty covering which allows electrical signals to travel faster). They are named the “uncinate fasciculus” and the “inferior fronto-occipital fasciculus” ("fasciculus" means "bundle"). The affected white matter tracts in LICA-09 connect frontal areas of the brain (typically involved with higher executive control) with other important regions scattered throughout the brain. When functioning properly, these tracts are involved with effective integration of emotional states with cognition and behavior, but with LICA-09 dysconnectivity, abnormalities in structure and function appear to result in a broad array of cognitive and clinical problems. The uncinate fasciculus and inferior fronto-occipital fasciculus also have been shown in prior research to be highly heritable, passing from one generation to the next. In addition, they mature more slowly compared with other white matter tracts, pointing to the importance of these pathways in brain development, and possibility of being exposed to more environmental influences over longer periods of active change.

Opening doors

This study represents a major achievement in understanding. Researchers have clearly shown a shared pattern of dysconnectivity which follows a developmental trajectory, and is correlated with genetic changes, cognitive impairment, and clinical issues. The LICA-09 pattern of dysconnectivity cuts across multiple clinical domains, and may be useful as a screening tool to identify at-risk adolescents.

Furthermore, future research will aim to refine our understanding of how different patterns of altered activity in LICA-09 lead to different clinical conditions, and could be used to diagnose different clinical conditions before they fully develop, permitting early intervention especially as environmental factors are identified which can tip the balance one way or the other. Finally, greater understanding of what exactly is off in different patterns of LICA-09 dysconnectivity could drive the development of specific therapeutic approaches which target particular problems in the two key white matter tracts involved.

By Grant H. Brenner, MD

LICA Subtypes and Relevant Correlations

Alnæs et al., 2018
Source: Alnæs et al., 2018

References

Buckholtz JW, Meyer-Lindenberg A. Psychopathology and the human connectome: toward a transdiagnostic model of risk for mental illness. Neuron. 2012 Jun 21;74(6):990-1004. doi: 10.1016/j.neuron.2012.06.002.

Smith SM, Nichols TE, Vidaurre D, Winkler AM, Behrens TE, Glasser MF, Ugurbil K, Barch DM, Van Essen DC, Miller KL. A positive-negative mode of population covariation links brain connectivity, demographics and behavior. 

Nat Neurosci. 2015 Nov;18(11):1565-7. doi: 10.1038/nn.4125. Epub 2015 Sep 28.

Kaufmann T, Alnæs D, Doan NT, Brandt CL, Andreassen OA, Westlye LT. Delayed stabilization and individualization in connectome development are related to psychiatric disorders.  Nat Neurosci. 2017 Apr;20(4):513-515. doi: 10.1038/nn.4511. Epub 2017 Feb 20.

Alnæs D, Kaufmann T, Doan NT, Córdova-Palomera A,  Wang Y, Bettella F, Moberget T, Andreassen OA, Westlye, LT. Association of Heritable Cognitive Ability and Psychopathology With White Matter Properties in Children and Adolescents. JAMA Psychiatry. doi:10.1001/jamapsychiatry.2017.4277 Published online January 24, 2018.