Xilin Shen

We depart from the feed-forward approach of brain-behavior modeling, which identifies the functional brain connectivity networks associated with performance on external tests, and instead introduce a feedback approach that reveals the brain systems those external tests reflect. In fMRI data from n = 302 demographically and clinically diverse participants, we a priori define connectivity networks for six cognitive constructs and employ kernel ridge regression to quantify each network's contribution to test performance. This approach provides a ranking of test scores according to the predictive power of each cognitive network, revealing which tests probe which brain networks. It further identifies combinations of measures that optimally probe predefined brain systems and evaluates how specific subtests influence composite scores, revealing when subset inclusion reinforces or weakens specific brain circuit and composite score relationships. This work opens an avenue of research by providing a framework for the development of test instruments guided by quantitative brain metrics.

The global burden of psychiatric disorders is rising ^1,2 and there is an urgent need for more effective systems of diagnosis, prediction, and treatment. While advances in neuroimaging have accelerated the discovery of brain networks involved across psychiatric conditions, whole-brain imaging techniques face limited clinical practicality due to high costs and restricted availability. The development of self-report psychometric tests targeting distinct functional networks in the brain may enable the affordable, rapid, and scalable measurement of psychiatric biomarkers without the need for imaging. Here, we show that individual self-report prompts (“items”) can be organized into distinct scales targeting functional brain networks, serving as biomarkers for brain connectivity across psychiatric conditions. We used the YaleNeuroConnect dataset ³ , comprised of a transdiagnostic cohort of participants (n=302) who completed a comprehensive psychometric battery with 318 total assessment items and underwent eight runs of resting and task-based functional magnetic resonance imaging (fMRI). Functional connectivity matrices were generated for each participant and partitioned into ten distinct canonical brain networks. Kernel ridge regression models were then trained on participant network connectivity and used to predict their scores on individual psychometric items. Overall, whole-brain functional connectivity significantly predicted 79% of psychometric items (FDR < 0.01) with every item being predicted by at least one functional network (FDR < 0.01). Each functional network was strongly linked to a unique group of psychometric items that could be aggregated into a single network score with a strong predictive basis in the brain. Next, a sequential feature selection paradigm was employed to identify combinations of items that optimally estimated participant connectivity across networks. Network-specific item weights extracted from these predictive models were able to significantly predict network connectivity and formed scales that recapitulated connectivity profiles across psychiatric diagnoses and general psychopathology. Using the functional network-basis for each item, we then developed and administered a brain-based scale, termed the “Functional Network Connectivity Index - Brief” (FNCI-B), to an external cohort (n=600) to evaluate the psychometric validity of this neurobiologically-driven development process. Administering the FNCI-B while varying properties such as item composition and order preserved critical psychometric properties such as factor structure. Together, these results indicate that brain-based psychometric scales can be developed with fMRI data to target specific functional networks – laying the foundation for the implementation of neural biomarkers in the clinic and advancing systems of diagnosis and treatment in psychiatry.

YaleNeuroConnect is a human functional MRI (fMRI) dataset collected at Yale University that includes functional MRI data (and the respective functional connectomes) obtained under resting-state and six task conditions. There are 302 diagnostically and demographically diverse subjects, each with extensive neuropsychological testing and symptom inventories obtained outside of the MRI. Prior studies have shown that stronger predictive models relating the brain to external measures can be built with connectivity data obtained during continuous performance tasks instead of the more common resting-state. The tasks here were selected to exercise the brain across various cognitive domains. For each subject, 48 minutes of fMRI data and high-resolution 3D brain volumes were obtained. The fMRI data, along with the deep phenotyping data in a diverse subject pool, allow studies of brain parcellation under different conditions, the relationship between cognitive and clinical measures, identification of circuits supporting external measures, and data for the development of brain-based tests. The transdiagnostic nature of the sample allows a sufficient range of symptom scores to test the principles of the Research Domain Criteria framework.

Individual differences in neuroimaging are of interest to clinical and cognitive neuroscientists based on their potential for guiding the personalized treatment of various heterogeneous neurological conditions and diseases. Despite many advantages, the prevailing modality in this field—blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI)—suffers from low spatiotemporal resolution and specificity as well as a propensity for noise and spurious signal corruption. To better understand individual differences in BOLD-fMRI data, we can use animal models where fMRI, alongside complementary but more invasive contrasts, can be accessed. Here, we apply simultaneous wide-field fluorescence calcium imaging and BOLD-fMRI in mice to interrogate individual differences using a connectome-based identification framework adopted from the human fMRI literature. This approach yields high spatiotemporal resolution cell-type specific signals (here, from glia, excitatory, as well as inhibitory interneurons) from the whole cortex. We found mouse multimodal connectome-based identification to be successful and explored various features of these data.

The mammalian brain is comprised of anatomically and functionally distinct regions. Substantial work over the past century has pursued construction of ever-more accurate maps of regional boundaries, using either expert judgement or data-driven clustering of functional, connectional, and/or architectonic properties. However, these approaches are often purely descriptive, have limited generalizability, and do not elucidate the underlying generative mechanisms that shape the regional organization of the brain. Here, we develop a novel approach that leverages a simple, hierarchical principle for generating a multiscale parcellation of any brain structure in any mammalian species using only its geometry. We show that this approach yields regions at any resolution scale that are more homogeneous than those defined in nearly all existing benchmark brain parcellations in use today across hundreds of anatomical, functional, cellular, and molecular brain properties measured in humans, macaques, marmosets, and mice. We additionally show how our method can be generalized to previously unstudied mammalian species for which no parcellations exist. Finally, we demonstrate how our approach captures the essence of a simple, hierarchical reaction-diffusion mechanism, in which the geometry of a brain structure shapes the spatial expression of putative patterning molecules linked to the formation of distinct regions through development. Our findings point to a highly conserved and universal influence of geometry on the regional organization of the mammalian brain.

Repeated mild head injuries due to sports, or domestic violence and military service are increasingly linked to debilitating symptoms in the long term. Although symptoms may take decades to manifest, potentially treatable neurobiological alterations must begin shortly after injury. Better means to diagnose and treat traumatic brain injuries requires an improved understanding of the mechanisms underlying progression and means through which they can be measured. Here, we employ a repetitive mild traumatic brain injury (rmTBI) and chronic variable stress mouse model to investigate emergent structural and functional brain abnormalities. Brain imaging is achieved with [ ¹⁸ F]SynVesT-1 positron emission tomography, with the synaptic vesicle glycoprotein 2A ligand marking synapse density and BOLD (blood-oxygen-level-dependent) functional magnetic resonance imaging (fMRI). Animals were scanned six weeks after concluding rmTBI/Stress procedures. Injured mice showed widespread decreases in synaptic density coupled with an i ncrease in local BOLD-fMRI synchrony detected as regional homogeneity. Injury-affected regions with higher synapse density showed a greater increase in fMRI regional homogeneity. Taken together, these observations may reflect compensatory mechanisms following injury. Multimodal studies are needed to provide deeper insights into these observations.

Background: Substantial strides have been made in developing connectome-based predictive models that establish connections between external measures of cognition and/or symptoms obtained through testing performed in a clinical setting, and the human functional connectome. Often referred to as brain-behavior modeling such models offer insights into the functional brain organization supporting the test scores of these external measures ^1–7 . Here, we depart from the conventional feed-forward approach and introduce a feed-back approach that provides new insight into the systems the external measures are reflecting and provides a framework for developing new test instruments that better target specific brain systems. Methods: In fMRI data from 227 demographically and clinically diverse subjects (healthy participants and patients), we a priori define connectivity networks for the six cognitive constructs and employ kernel ridge regression in a predictive modeling framework to quantify each network’s contribution to performance across a spectrum of standardized tests. Results: This approach provides a ranking of test scores according to the predictive power of each cognitive network, allowing one to choose the best test to probe a specific brain network. It yields a brain-driven process for forming new tests through selection of combinations of measures that probe the same brain systems. These new composite tests yield better external measures, as reflected by higher predictive power in brain-behavior modeling. We also evaluate the inclusion of specific subtests within a composite score, revealing instances where composite scores are reinforced or weakened by subtest inclusion regarding the specificity of the brain network they interrogate. Conclusions: The brain-behavior modeling problem can be reconfigured to provide a biologically driven approach to the selection of external measures directed at specific brain systems. It opens new avenues of research by providing a framework for the development of measures, both cognitive and clinical, guided by quantitative brain metrics.

Up to 50% of patients with severe congenital heart disease (CHD) develop life-altering neurodevelopmental disability (NDD). It has been presumed that NDD arises in CHD cases because of hypoxia before, during, or after cardiac surgery. Recent studies detected an enrichment in de novo mutations in CHD and NDD, as well as significant overlap between CHD and NDD candidate genes. However, there is limited evidence demonstrating that genes causing CHD can produce NDD independent of hypoxia. A patient with hypoplastic left heart syndrome and gross motor delay presented with a de novo mutation in SMC5. Modeling mutation of smc5 in Xenopus tropicalis embryos resulted in reduced heart size, decreased brain length, and disrupted pax6 patterning. To evaluate the cardiac development, we induced the conditional knockout (cKO) of Smc5 in mouse cardiomyocytes, which led to the depletion of mature cardiomyocytes and abnormal contractility. To test a role for Smc5 specifically in the brain, we induced cKO in the mouse central nervous system, which resulted in decreased brain volume, and diminished connectivity between areas related to motor function but did not affect vascular or brain ventricular volume. We propose that genetic factors, rather than hypoxia alone, can contribute when NDD and CHD cases occur concurrently.