Defining Individual-Specific Functional Neuroanatomy for Precision Psychiatry

Gratton, Caterina; Kraus, Brian; Greene, Deanna J.; Gordon, Evan M.; Laumann, Timothy O.; Nelson, Steven M.; Dosenbach, Nico U.F.; Petersen, Steven E.

doi:10.1016/j.biopsych.2019.10.026

Cited by 151 publications

(144 citation statements)

References 138 publications

Supporting

Mentioning

140

Contrasting

Order By: Relevance

“…While the field of precision fMRI has recently emerged in human brain research 4,7,12,33,34 , rodent data are still analyzed at the group level 35,36 . A major challenge in precision fMRI is the need of large amount of data per subject ranging from 50 to 100 minutes based on the studied brain structure 37 . Such amount of data has yet to be achieved in anesthetized mice in which acquisition is predominantly between several minutes and up to 40 minutes per session 38,39 and a substantive repeated-measurement design is not feasible due to the difficulties that arise from repeated ventilation and catheterization and the potential impact of prolonged anesthesia.…”

Section: Discussionmentioning

confidence: 99%

Individual variability in functional connectivity architecture of the mouse brain

Bergmann

Gofman

Kavushansky

et al. 2020

Preprint

View full text Add to dashboard Cite

The functional organization of brain networks can be estimated using fMRI by examining the coherence of spontaneous fluctuations in the fMRI signal, a method known as resting-state functional connectivity MRI. Previous studies in humans reported that such functional networks are dominated by stable group and individual factors, demonstrating that fMRI is suited to measuring subject-specific characteristics, and suggesting the utility of such precision fMRI approach in personalized medicine. However, mechanistic investigations to the sources of individual variability in health and disease are limited in humans and thus require animal models.Here, we used repeated-measurement resting-state fMRI in awake mice to quantify the contribution of individual variation to the functional architecture of the mouse cortex. Comparing the organization of functional networks across the group, we found dominant common organizational principles. The data also revealed stable individual features, which create a unique fingerprint that allow identification of individual mice from the group. Examining the distribution of individual variation across the mouse cortex, we found it is homogeneously distributed in both sensory and association networks. Finally, connectome-based predictive modeling of motor behavior in the rotarod task revealed that individual variation in functional connectivity explained behavioral variability. Collectively, these results show that mouse functional networks are characterized by individual variations suggesting that individual variation characterizes the mammalian cortex in general, and not only the primate cortex. These findings lay the foundation for future mechanistic investigations of individual brain organization and pre-clinical studies of brain disorders in the context of personalized medicine.

show abstract

Section: Discussionmentioning

confidence: 99%

Individual variability in functional connectivity architecture of the mouse brain

Bergmann

Gofman

Kavushansky

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…This work provides the basis for further elaboration of multi-dimensional heritability techniques, such as genetic correlation, that could reveal patterns of shared genetic variance with psychological phenotypes 13,41 . Individualized network parcellations also hold promise for understanding psychiatric disorders 16 , which are often heritable 60 . Identifying shared genetic substrates between individualized features of network organization and psychiatric illness will be important as individualized approaches become increasingly adopted in clinical neuroscientific research.…”

Section: Heritability Of Individualized Network Topography Across Cortexmentioning

confidence: 99%

“…The use of population-average network topographies has accelerated psychological and neuroscientific discovery, however there is growing recognition that the human brain is characterized by striking functional variability across individuals [9][10][11][12][13][14][15] . As individualized approaches become increasingly popular for the study of human behavior and psychopathology 13,[16][17][18] , there is growing need to quantify the heritable bases of population-level variability in functional network size and topography. Despite the fact that individual differences result from the convergence of both genetic and environmental influences, the extent to which the size and spatial patterning of cortical networks may reflect heritable features of brain function has not yet been systematically investigated.…”

Section: Introductionmentioning

confidence: 99%

Heritability of individualized cortical network topography

Anderson

Kong

et al. 2020

Preprint

View full text Add to dashboard Cite

Human cortex is patterned by a complex and interdigitated web of large-scale functional networks. Recent methodological breakthroughs reveal variation in the size, shape, and spatial topography of cortical networks across individuals. While spatial network organization emerges across development, is stable over time, and predictive of behavior, it is not yet clear to what extent genetic factors underlie inter-individual differences in network topography. Here, leveraging a novel non-linear multi-dimensional estimation of heritability, we provide evidence that individual variability in the size and topographic organization of cortical networks are under genetic control. Using twin and family data from the Human Connectome Project (n=1,023), we find increased variability and reduced heritability in the size of heteromodal association networks (h2: M=0.33, SD=0.071), relative to unimodal sensory/motor cortex (h2: M=0.44, SD=0.051). We then demonstrate that the spatial layout of cortical networks is influenced by genetics, using our multi-dimensional estimation of heritability (h2-multi; M=0.14, SD=0.015). However, topographic heritability did not differ between heteromodal and unimodal networks. Genetic factors had a regionally variable influence on brain organization, such that the heritability of network topography was greatest in prefrontal, precuneus, and posterior parietal cortex. Taken together, these data are consistent with relaxed genetic control of association cortices relative to primary sensory/motor regions, and have implications for understanding population-level variability in brain functioning, guiding both individualized prediction and the interpretation of analyses that integrate genetics and neuroimaging.

show abstract

“…Using alternative methods, others have obtained similar estimates of intra-and inter-subject variation in rsfMRI data (Betzel et al, 2019;Mueller et al, 2013). While these studies have produced important insights, such large amounts of neuroimaging data (5-84 hr of data per individual, see (Gratton et al, 2019) for a review) may be infeasible for clinical samples.…”

Section: Introductionmentioning

confidence: 97%

Functional connectome fingerprinting accuracy in youths and adults is similar when examined on the same day and 1.5‐years apart

Jalbrzikowski

Liu

Foran

et al. 2020

Human Brain Mapping

View full text Add to dashboard Cite

Pioneering studies have shown that individual correlation measures from resting‐state functional magnetic resonance imaging studies can identify another scan from that same individual. This method is known as “connectotyping” or functional connectome “fingerprinting.” We analyzed a unique dataset of 12–30 years old (N = 140) individuals who had two distinct resting state scans on the same day and again 12–18 months later to assess the sensitivity and specificity of fingerprinting accuracy across different time scales (same day, ~1.5 years apart) and developmental periods (youths, adults). Sensitivity and specificity to identify one's own scan was high (average AUC = 0.94), although it was significantly higher in the same day (average AUC = 0.97) than 1.5‐years later (average AUC = 0.91). Accuracy in youths (average AUC = 0.93) was not significantly different from adults (average AUC = 0.96). Multiple statistical methods revealed select connections from the Frontoparietal, Default, and Dorsal Attention networks enhanced the ability to identify an individual. Identification of these features generalized across datasets and improved fingerprinting accuracy in a longitudinal replication data set (N = 208). These results provide a framework for understanding the sensitivity and specificity of fingerprinting accuracy in adolescents and adults at multiple time scales. Importantly, distinct features of one's “fingerprint” contribute to one's uniqueness, suggesting that cognitive and default networks play a primary role in the individualization of one's connectome.

show abstract

Defining Individual-Specific Functional Neuroanatomy for Precision Psychiatry

Cited by 151 publications

References 138 publications

Individual variability in functional connectivity architecture of the mouse brain

Individual variability in functional connectivity architecture of the mouse brain

Heritability of individualized cortical network topography

Functional connectome fingerprinting accuracy in youths and adults is similar when examined on the same day and 1.5‐years apart

Contact Info

Product

Resources

About