D. Reese McKay

Behavioral interpretations of intrinsic connectivity networks

An increasingly large number of neuroimaging studies have investigated functionally connected networks during rest, providing insight into human brain architecture. Assessment of the functional qualities of resting state networks has been limited by the task-independent state, which results in an inability to relate these networks to specific mental functions. However, it was recently demonstrated that similar brain networks can be extracted from resting state data and data extracted from thousands of task-based neuroimaging experiments archived in the BrainMap database. Here, we present a full functional explication of these intrinsic connectivity networks at a standard low order decomposition using a neuroinformatics approach based on the BrainMap behavioral taxonomy as well as a stratified, data-driven ordering of cognitive processes. Our results serve as a resource for functional interpretations of brain networks in resting state studies and future investigations into mental operations and the tasks that drive them.

The BrainMap strategy for standardization, sharing, and meta-analysis of neuroimaging data.

BackgroundNeuroimaging researchers have developed rigorous community data and metadata standards that encourage meta-analysis as a method for establishing robust and meaningful convergence of knowledge of human brain structure and function. Capitalizing on these standards, the BrainMap project offers databases, software applications, and other associated tools for supporting and promoting quantitative coordinate-based meta-analysis of the structural and functional neuroimaging literature.FindingsIn this report, we describe recent technical updates to the project and provide an educational description for performing meta-analyses in the BrainMap environment.ConclusionsThe BrainMap project will continue to evolve in response to the meta-analytic needs of biomedical researchers in the structural and functional neuroimaging communities. Future work on the BrainMap project regarding software and hardware advances are also discussed.

Arguments for the sake of endophenotypes: Examining common misconceptions about the use of endophenotypes in psychiatric genetics

Endophenotypes are measurable biomarkers that are correlated with an illness, at least in part, because of shared underlying genetic influences. Endophenotypes may improve our power to detect genes influencing risk of illness by being genetically simpler, closer to the level of gene action, and with larger genetic effect sizes or by providing added statistical power through their ability to quantitatively rank people within diagnostic categories. Furthermore, they also provide insight into the mechanisms underlying illness and will be valuable in developing biologically‐based nosologies, through efforts such as RDoC, that seek to explain both the heterogeneity within current diagnostic categories and the overlapping clinical features between them. While neuroimaging, electrophysiological, and cognitive measures are currently most used in psychiatric genetic studies, researchers currently are attempting to identify candidate endophenotypes that are less genetically complex and potentially closer to the level of gene action, such as transcriptomic and proteomic phenotypes. Sifting through tens of thousands of such measures requires automated, high‐throughput ways of assessing, and ranking potential endophenotypes, such as the Endophenotype Ranking Value. However, despite the potential utility of endophenotypes for gene characterization and discovery, there is considerable resistance to endophenotypic approaches in psychiatry. In this review, we address and clarify some of the common issues associated with the usage of endophenotypes in the psychiatric genetics community.

Long-term neural and physiological phenotyping of a single human

Psychiatric disorders are characterized by major fluctuations in psychological function over the course of weeks and months, but the dynamic characteristics of brain function over this timescale in healthy individuals are unknown. Here, as a proof of concept to address this question, we present the MyConnectome project. An intensive phenome-wide assessment of a single human was performed over a period of 18 months, including functional and structural brain connectivity using magnetic resonance imaging, psychological function and physical health, gene expression and metabolomics. A reproducible analysis workflow is provided, along with open access to the data and an online browser for results. We demonstrate dynamic changes in brain connectivity over the timescales of days to months, and relations between brain connectivity, gene expression and metabolites. This resource can serve as a testbed to study the joint dynamics of human brain and metabolic function over time, an approach that is critical for the development of precision medicine strategies for brain disorders.

ICA model order selection of task co-activation networks

Independent component analysis (ICA) has become a widely used method for extracting functional networks in the brain during rest and task. Historically, preferred ICA dimensionality has widely varied within the neuroimaging community, but typically varies between 20 and 100 components. This can be problematic when comparing results across multiple studies because of the impact ICA dimensionality has on the topology of its resultant components. Recent studies have demonstrated that ICA can be applied to peak activation coordinates archived in a large neuroimaging database (i.e., BrainMap Database) to yield whole-brain task-based co-activation networks. A strength of applying ICA to BrainMap data is that the vast amount of metadata in BrainMap can be used to quantitatively assess tasks and cognitive processes contributing to each component. In this study, we investigated the effect of model order on the distribution of functional properties across networks as a method for identifying the most informative decompositions of BrainMap-based ICA components. Our findings suggest dimensionality of 20 for low model order ICA to examine large-scale brain networks, and dimensionality of 70 to provide insight into how large-scale networks fractionate into sub-networks. We also provide a functional and organizational assessment of visual, motor, emotion, and interoceptive task co-activation networks as they fractionate from low to high model-orders.

Genetic basis of neurocognitive decline and reduced white-matter integrity in normal human brain aging

Significance Identification of genes associated with brain aging should improve our understanding of the biological processes that govern normal age-related decline. In randomly selected pedigrees, we documented profound aging effects from young adulthood to old age (18–83 years) on neurocognitive ability and diffusion-based white-matter measures. Despite significant phenotypic correlation between white-matter integrity and tests of processing speed, working memory, declarative memory, and intelligence, no evidence for shared genetic determination was observed. Applying a gene-by-environment interaction analysis where age is an environmental factor, we demonstrate a heritable basis for neurocognitive deterioration with age. In contrast, increasing white-matter incoherence with age appears to be nongenetic. Identifying brain-aging traits is a critical first step in delineating the biological mechanisms of successful aging. Identification of genes associated with brain aging should markedly improve our understanding of the biological processes that govern normal age-related decline. However, challenges to identifying genes that facilitate successful brain aging are considerable, including a lack of established phenotypes and difficulties in modeling the effects of aging per se, rather than genes that influence the underlying trait. In a large cohort of randomly selected pedigrees (n = 1,129 subjects), we documented profound aging effects from young adulthood to old age (18–83 y) on neurocognitive ability and diffusion-based white-matter measures. Despite significant phenotypic correlation between white-matter integrity and tests of processing speed, working memory, declarative memory, and intelligence, no evidence for pleiotropy between these classes of phenotypes was observed. Applying an advanced quantitative gene-by-environment interaction analysis where age is treated as an environmental factor, we demonstrate a heritable basis for neurocognitive deterioration as a function of age. Furthermore, by decomposing gene-by-aging (G × A) interactions, we infer that different genes influence some neurocognitive traits as a function of age, whereas other neurocognitive traits are influenced by the same genes, but to differential levels, from young adulthood to old age. In contrast, increasing white-matter incoherence with age appears to be nongenetic. These results clearly demonstrate that traits sensitive to the genetic influences on brain aging can be identified, a critical first step in delineating the biological mechanisms of successful aging.

Influence of age, sex and genetic factors on the human brain

We report effects of age, age2, sex and additive genetic factors on variability in gray matter thickness, surface area and white matter integrity in 1,010 subjects from the Genetics of Brain Structure and Function Study. Age was more strongly associated with gray matter thickness and fractional anisotropy of water diffusion in white matter tracts, while sex was more strongly associated with gray matter surface area. Widespread heritability of neuroanatomic traits was observed, suggesting that brain structure is under strong genetic control. Furthermore, our findings indicate that neuroimaging-based measurements of cerebral variability are sensitive to genetic mediation. Fundamental studies of genetic influence on the brain will help inform gene discovery initiatives in both clinical and normative samples.

Ventral Anterior Cingulate Connectivity Distinguished Nonpsychotic Bipolar Illness from Psychotic Bipolar Disorder and Schizophrenia

Bipolar illness is a debilitating neuropsychiatric disorder associated with alterations in the ventral anterior cingulate cortex (vACC), a brain region thought to regulate emotional behavior. Although recent data-driven functional connectivity studies provide evidence consistent with this possibility, the role of vACC in bipolar illness and its pattern of whole brain connectivity remain unknown. Furthermore, no study has established whether vACC exhibits differential whole brain connectivity in bipolar patients with and without co-occurring psychosis and whether this pattern resembles that found in schizophrenia. We conducted a human resting-state functional connectivity investigation focused on the vACC seed in 73 remitted bipolar I disorder patients (33 with psychosis history), 56 demographically matched healthy comparison subjects, and 73 demographically matched patients with chronic schizophrenia. Psychosis history within the bipolar disorder group corresponded with significant between-group connectivity alterations along the dorsal medial prefrontal surface when using the vACC seed. Patients with psychosis history showed reduced connectivity (Cohens d = -0.69), whereas those without psychosis history showed increased vACC coupling (Cohens d = 0.8) relative to controls. The vACC connectivity observed in chronic schizophrenia patients was not significantly different from that seen in bipolar patients with psychosis history but was significantly reduced compared with that in bipolar patients without psychosis history. These robust findings reveal complex vACC connectivity alterations in bipolar illness, which suggest differences depending on co-occurrence of lifetime psychosis. The similarities in vACC connectivity patterns in schizophrenia and psychotic bipolar disorder patients may suggest the existence of common mechanisms underlying psychotic symptoms in the two disorders.

Reduced White Matter Integrity in Sibling Pairs Discordant for Bipolar Disorder

OBJECTIVE Several lines of evidence indicate that white matter integrity is compromised in bipolar disorder, but the nature, extent, and biological causes remain elusive. To determine the extent to which white matter deficits in bipolar disorder are familial, the authors investigated white matter integrity in a large sample of bipolar patients, unaffected siblings, and healthy comparison subjects. METHOD The authors collected diffusion imaging data for 64 adult bipolar patients, 60 unaffected siblings (including 54 discordant sibling pairs), and 46 demographically matched comparison subjects. Fractional anisotropy was compared between the groups using voxel-wise tract-based spatial statistics and by extracting mean fractional anisotropy from 10 regions of interest. Additionally, intraclass correlation coefficients were calculated between the sibling pairs as an index of familiality. RESULTS Widespread fractional anisotropy reductions in bipolar patients (>40,000 voxels) and more subtle reductions in their siblings, mainly restricted to the corpus callosum, posterior thalamic radiations, and left superior longitudinal fasciculus (>2,000 voxels) were observed. Similarly, region-of-interest analysis revealed significant reductions in most white matter regions in patients. In siblings, fractional anisotropy in the posterior thalamic radiation and the forceps was nominally reduced. Significant between-sibling correlations were found for mean fractional anisotropy across the tract-based spatial statistic skeleton, within significant clusters, and within nearly all regions of interest. CONCLUSIONS These findings emphasize the relevance of white matter to neuropathology and familiality of bipolar disorder and encourage further use of white matter integrity markers as endophenotypes in genetic studies.

Multi-region hemispheric specialization differentiates human from nonhuman primate brain function

The human behavioral repertoire greatly exceeds that of nonhuman primates. Anatomical specializations of the human brain include an enlarged neocortex and prefrontal cortex (Semendeferi et al. in Am J Phys Anthropol 114:224–241, 2001), but regional enlargements alone cannot account for these vast functional differences. Hemispheric specialization has long believed to be a major contributing factor to such distinctive human characteristics as motor dominance, attentional control and language. Yet structural cerebral asymmetries, documented in both humans and some nonhuman primate species, are relatively minor compared to behavioral lateralization. Identifying the mechanisms that underlie these functional differences remains a goal of considerable interest. Here, we investigate the intrinsic connectivity networks in four primate species (humans, chimpanzees, baboons, and capuchin monkeys) using resting-state fMRI to evaluate the intra- and inter- hemispheric coherences of spontaneous BOLD fluctuation. All three nonhuman primate species displayed lateralized functional networks that were strikingly similar to those observed in humans. However, only humans had multi-region lateralized networks, which provide fronto-parietal connectivity. Our results indicate that this pattern of within-hemisphere connectivity distinguishes humans from nonhuman primates.