Evelyn M. R. Lake

Understanding the principles governing large-scale functional organization of the brain remains a central challenge in systems neuroscience. Despite convergent findings, substantial variability across analytical approaches suggests that functional networks may not admit a unique partitioning. Here, we propose that this variability reflects an intrinsic property of the connectome itself: its organization may be fundamentally multi-modal rather than singular. To test this hypothesis, we employ a Bayesian generative modeling framework based on stochastic block models, enabling principled comparison of competing organizational principles and characterization of the full posterior distribution over network partitions. Applying this framework to resting-state fMRI data in mice, we find that a non-degree-corrected hierarchical architecture provides the most parsimonious description of the functional connectome. Importantly, the inferred posterior landscape is not dominated by a single configuration, but instead comprises multiple distinct and co-dominant organizational schemes. At the mesoscale, these hierarchical communities are anatomically grounded yet systematically reorganize canonical resting-state networks: primary sensory systems remain cohesive, whereas higher-order association networks are fractionated into multiple interacting sub-circuits. This global structural variation is driven by structured variability at the community level, where integrative systems exhibit variable regional affiliations while sensory systems act as structurally stable anchors. Together, these findings suggest that the resting-state connectome is best described as a distribution over alternative, yet co-dominant, organizational configurations. This perspective reconciles inconsistencies across previous studies and supports a view of brain organization as inherently degenerate, providing a latent repertoire of network configurations that may underlie adaptive information routing and dynamic functional reconfiguration.

Sustained attention is an important neurobiological process impaired in multiple neurodevelopmental disorders, including attention-deficit/hyperactivity disorder (ADHD) and autism. Connectome-based predictive models (CPMs) of sustained attention function and autism symptoms generalize across samples, but whether their features are shared or phenotype-specific is unknown. We tested within-phenotype consistency and across-phenotype specificity in 5 published CPMs predicting autism symptoms or sustained attention performance in youth and adults, equating model size (top 1,000 positively and negatively predictive edges per model). No edge or network pair was shared by all 5 models. Same-phenotype models were nonetheless consistent: the whole-brain pattern of edge-behavior associations correlated in all 4 same-phenotype dataset pairs (r > 0.13, P < 0.005). Features were also largely phenotype-specific: only 1 of 6 cross-phenotype pairs correlated significantly (negatively), and 2 models built to predict autism symptoms vs. attention in the same dataset shared no more edges than chance. Finally, representational similarity analysis revealed that scan type (rest vs. task), participant age, and autism diagnosis did not explain similarity in edge-behavior associations across datasets. Together, these results suggest that feature sets are reproducible within, but distinct across, phenotypes, consistent with autism symptoms and sustained attention being related but separable. Highlights: We interrogated five previously published, transdiagnostic models of sustained attention and autistic phenotypes Functional connectome-based models shared edges and networks when phenotypes were similar Models predicting attention phenotypes shared more similar features with each other than models predicting autism symptoms and vice-versa Age, functional run type, and diagnosis did not affect model similarity

Understanding how brain regions influence one another over time is a central goal of neuroscience. However, existing approaches to effective connectivity often involve tradeoffs among mechanistic interpretability, computational scalability, and time-resolved estimation. Here, we introduce information channel capacity, a model-based measure of directed information transfer between brain regions, and combine it with a sliding-window framework to estimate time-varying directional interactions. We validate channel capacity across multimodal neuroimaging datasets in humans and rodents because this breadth is needed to test three complementary properties that no single dataset can establish alone: sensitivity to evoked information transfer, specificity against false-positive directional effects, and the ability to capture meaningful temporal variability in directed brain-network interactions. Human motor-task fMRI tests sensitivity, showing that channel capacity detects task-related increases in directed interactions and stronger directional effects during task than during rest in motor-related regions. Concurrent rat local field potential (LFP)-fMRI tests specificity, showing minimal spurious directional asymmetry relative to scan-to-scan variability and consistent temporal dynamics across neural and BOLD signals. Mouse Ca ²⁺ -fMRI tests temporal variability, showing that channel-capacity patterns identify reproducible connectivity states and transitions over time. Together, these results establish channel capacity as a physiologically grounded framework for measuring dynamic directional interactions across species and neuroimaging modalities.

Functional magnetic resonance imaging (fMRI) captures whole-brain activity fluctuations non-invasively in humans and animals. Beyond task/stimuli-locked responses, fMRI measures large-scale patterned activity during rest. Among these, quasi-periodic patterns (QPPs) represent recurring waves of activity that unfold over seconds and exhibit consistent spatiotemporal characteristics. Notably, certain fMRI-QPPs are well-preserved across species and altered in various neuropsychiatric and neurodegenerative diseases. Yet, our collective understanding of their neural underpinnings is limited given the indirect nature of blood-oxygen-level dependent (BOLD) fMRI signals. Simultaneous measures of local field potentials have provided some affirmation that fMRI-QPPs have neural origins, but these point-measurements are limited to a handful of sites. Here, we use a unique multimodal implementation of simultaneous wide-field calcium (WF-Ca ²⁺ ) imaging and fMRI to investigate the neural origins of fMRI-based QPPs. We uncover a robust time-locked correlation between QPPs detected by cortex-wide fluorescent WF-Ca ²⁺ imaging of neural activity and QPPs of BOLD-fMRI. These data validate the hypothesis that BOLD-fMRI QPPs derive from preceding slow waves of neural activity with regional and temporal precision.

Background: Despite high rates of autism spectrum disorder (ASD), understanding of pathophysiology is limited. The RAS-mitogen-activated protein kinase (RAS-MAPK) pathway plays a crucial role in ASD and is altered in children with Noonan syndrome (NS). Children with NS offer a unique model to disentangle genetic and neurological underpinnings of ASD. Methods: This study aimed to examine functional brain network anatomy underlying social impairments in children with NS ( n = 28, mean age = 8.24), and tested generalizability of models developed in a non-syndromic cohort enriched for ASD (Autism Brain Imaging Data Exchange (ABIDE), n = 352, mean age = 11.0). Connectome-based predictive modeling (CPM) was applied to fMRI data to predict the severity of autism symptoms, indexed by the Social Responsive Scale (SRS), in children with NS. Next, we tested if a model developed to predict autism symptoms in an autism-enriched sample of children without NS (ABIDE) could predict autism symptoms in children with NS. Results: Predicted SRS scores were significantly associated with observed SRS scores in NS (r _s = 0.43, p = 0.011). Application of the predictive model generated in the autism-enriched cohort (ABIDE) significantly predicted observed SRS scores in NS (r _s = 0.46, p = 0.018). Predictive brain networks in both NS and the non-syndromic cohorts included subcortical-cerebellar networks and visual processing networks. Limitations: The size of our NS cohort is small, given the rarity of NS. However, the significant cross-dataset comparison yielded in this study suggests that use of large publicly available datasets can be useful in contextualizing smaller and harder to collect datasets in rare genetic syndromes. Conclusions: The presence of shared brain networks suggests converging patterns of functional connectivity underlying autism symptoms across diagnoses. These findings point to potential overlap between non-syndromic autism and NS and highlight the value of human genetic models for studying ASD. Future work investigating RAS-MAPK pathway dysregulation may further elucidate its contribution to autism-related brain function.

Functional magnetic resonance imaging (fMRI) can be applied in mice and humans making it a key technology in translational neuroimaging research. Yet, most neuroimaging studies in rodents use anesthesia to limit subject motion and stress. This puts a hard boundary on the range of brain and behavioral states that can be studied in animals, and deviates from the near-universal practice of imaging people whilst awake. Recent years have seen a push towards the development of acclimation protocols for imaging mice without anesthesia, but the results have been mixed. In parallel, imaging mice without anesthesia has become routine for complementary neuroimaging methods including wide-field fluorescence calcium (WF-Ca ²⁺ ) imaging. We present the first longitudinal protocol for simultaneous WF-Ca ²⁺ and fMRI in awake mice. Our approach is comprised of a two-phase (biphasic) acclimation protocol that results in high-quality multimodal data, enabling direct comparison of neuronal mesoscopic and hemodynamic signals across brain states and time. Data from awake mice are compared with data from isoflurane-anesthetized imaging sessions to unlock state-dependent differences in functional connectivity, which show both convergent and divergent patterns between imaging modalities. Together, these results establish a framework for longitudinal awake multimodal imaging and uncover new insights into the neural and vascular functional organization of the mouse brain across states and time.

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.

Enlargement of the cerebrospinal fluid (CSF)-filled brain ventricles (ventriculomegaly) is a defining feature of congenital hydrocephalus (CH) and an under-recognized concomitant of autism. Here, we show that de novo mutations in the autism risk gene PTEN are among the most frequent monogenic causes of CH and primary ventriculomegaly. Mouse Pten-mutant ventriculomegaly results from aqueductal stenosis due to hyperproliferation of periventricular Nkx2.1⁺ neural progenitor cells (NPCs) and increased CSF production from hyperplastic choroid plexus. Pten-mutant ventriculomegalic cortices exhibit network dysfunction from increased activity of Nkx2.1⁺ NPC-derived inhibitory interneurons. Raptor deletion or postnatal everolimus treatment corrects ventriculomegaly, rescues cortical deficits and increases survival by antagonizing mTORC1-dependent Nkx2.1⁺ NPC pathology. Thus, PTEN mutations concurrently alter CSF dynamics and cortical networks by dysregulating Nkx2.1⁺ NPCs. These results implicate a nonsurgical treatment for CH, demonstrate a genetic association of ventriculomegaly and ASD, and help explain neurodevelopmental phenotypes refractory to CSF shunting in select individuals with CH.

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.