Biology subjects

Mandino, F.

Publications and source records attributed to Mandino, F..

16 recordsLinked to original sources

bioRxiv2026

Bayesian generative modeling reveals a multi-modal hierarchical architecture in the mouse functional connectome

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.

neuroscience↗

bioRxiv2026

Channel Capacity for Time-Resolved Effective Connectivity in Functional Neuroimaging

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 Ca2+-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.

neuroscience↗

bioRxiv2026

Harmonizing brain rhythms: cortex-wide neuronal dynamics underpin quasi-periodic patterns in resting-state fMRI

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-Ca2+) 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-Ca2+ 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.

neuroscience↗

bioRxiv2025

Longitudinal, simultaneous wide-field fluorescent Ca2+ imaging and fMRI in awake mice

Functional magnetic resonance imaging (fMRI) can be applied in mice and humans making it a key technology in translational neuroimaging research. Yet, most fMRI 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 performing fMRI in 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-Ca2+) imaging. Our group works with a unique co-implementation of WF-Ca2+ and fMRI which enables the collection of complementary measures of brain function, but, until now, has necessitated the use of anesthesia. We present the first longitudinal protocol for simultaneous WF-Ca2+ and fMRI in awake mice. Our approach is comprised of a two-phase (biphasic) acclimation protocol that results in high-quality multimodal data. Comparisons of cortex-wide neuronal activity (WF-Ca2+ imaging) and blood-oxygen-level dependent fMRI signals between isoflurane-anesthetized and awake mice reveal both convergent and divergent patterns in brain function between modalities, and across time.

neuroscience↗

bioRxiv2024

Repetitive mild closed-head injury induced synapse loss and increased local BOLD-fMRI signal homogeneity

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 closed-head injury (rmTBI) and chronic variable stress (CVS) mouse model to investigate emergent structural and functional brain abnormalities. Brain imaging is achieved with [18F]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 increase 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.

neuroscience↗

bioRxiv2024

Multimodal identification of the mouse brain using simultaneous Ca2+ imaging and fMRI

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 workhorse in this arena, BOLD (blood-oxygen-level-dependent) 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.

neuroscience↗

bioRxiv2023

Aberrant Connectivity Across the Lifespan in a Mouse Model of Alzheimer's disease and Rescue by mGlur5 Modulator Treatment

Amyloid accumulation in Alzheimers disease (AD) is associated with synaptic damage and altered connectivity in brain networks. While measures of amyloid accumulation and biochemical changes in mouse models have utility for translational studies of certain therapeutics, preclinical analysis of altered brain connectivity using clinically relevant fMRI measures has not been well developed for agents intended to improve neural networks. Here, we conduct a longitudinal study in a double knock-in mouse model for AD (AppNL-G-F/hMapt), monitoring brain connectivity by means of resting-state fMRI. While the 4-month-old AD mice are indistinguishable from wild-type controls (WT), decreased connectivity in the default-mode network is significant for the AD mice relative to WT mice by 6 months of age and is pronounced by 9 months of age. In a second cohort of 20-month-old mice with persistent functional connectivity deficits for AD relative to WT, we assess the impact of two-months of oral treatment with a silent allosteric modulator of mGluR5 (BMS-984923) known to rescue synaptic density. Functional connectivity deficits in the aged AD mice are reversed by the mGluR5-directed treatment. The longitudinal application of fMRI has enabled us to define the preclinical time trajectory of AD-related changes in functional connectivity, and to demonstrate a translatable metric for monitoring disease emergence, progression, and response to synapse-rescuing treatment.

neuroscience↗

bioRxiv2023

Dual impact of PTEN mutation on CSF dynamics and cortical networks via the dysregulation of neural precursors and their interneuron descendants

Expansion of the cerebrospinal fluid (CSF)-filled cerebral ventricles (ventriculomegaly) is the quintessential feature of congenital hydrocephalus (CH) but also seen in autism spectrum disorder (ASD) and several neuropsychiatric diseases. PTEN is frequently mutated in ASD; here, we show PTEN is a bona fide risk gene for the development of ventriculomegaly, including neurosurgically-treated CH. Pten-mutant hydrocephalus is associated with aqueductal stenosis due to the hyperproliferation of periventricular Nkx2.1+ neural precursors (NPCs) and CSF hypersecretion from inflammation-dependent choroid plexus hyperplasia. The hydrocephalic Pten-mutant cortex exhibits ASD-like network dysfunction due to impaired activity of Nkx2.1+ NPC-derived inhibitory interneurons. Raptor deletion or post-natal Everolimus corrects ventriculomegaly, rescues cortical deficits, and increases survival by antagonizing mTORC1-dependent Nkx2.1+ cell pathology. These results implicate a dual impact of PTEN mutation on CSF dynamics and cortical networks via the dysregulation of NPCs and their interneuron descendants. These data identify a non-surgical treatment target for hydrocephalus and have implications for other developmental brain disorders. HIGHLIGHTSO_LIPTEN de novo mutations are associated with cerebral ventriculomegaly in autism spectrum disorder (ASD) and congenital hydrocephalus (CH). C_LIO_LIPten-mutant hydrocephalus is associated with aqueductal stenosis due to the hyperproliferation of medial ganglionic eminence Nkx2.1+ neural precursors and CSF hypersecretion from inflammation-induced choroid plexus hyperplasia. C_LIO_LIThe hydrocephalic Pten-mutant cortex exhibits ASD-like network dysfunction due to impaired activity of Nkx2.1+ NPC-derived inhibitory interneurons. C_LIO_LImTORC1 inhibition via Raptor deletion or early post-natal treatment with rapamycin or everolimus increases survival and ameliorates Pten-mutant ventriculomegaly and cortical pathology. C_LI

neuroscience↗

bioRxiv2022

Functional network organization of the mouse cortex determined by wide-field fluorescence imaging shares some --but not all-- properties revealed with simultaneous fMRI-BOLD

Work in humans and animals shows that the brain can be decomposed into large-scale functional networks. Whereas most studies, especially in humans, use the blood-oxygenation-level-dependent (BOLD) signal, the relationship between BOLD and neuronal activity is complex and incompletely understood. This limits our ability to interpret and apply measures derived from fMRI-BOLD. Here, we employ wide-field Ca2+ imaging simultaneously recorded with fMRI-BOLD in highly-sampled mice expressing GCaMP6f in excitatory neurons. These unique data enabled us to characterize the similarities and differences between networks discoverable by each modality. Importantly, we applied a network partitioning approach that uses a mixed-membership algorithm, which allows brain regions to participate in multiple networks with varying strengths. This contrasts with assuming regions belong to only one network. Our findings demonstrate that (1) most BOLD networks are detected via Ca2+ signals. (2) There is considerable overlapping--as opposed to disjoint--network organization that is evident from both modalities. (3) Large-scale networks determined by Ca2+ signals at low temporal frequencies (0.01 - 0.5 Hz)--as opposed to higher frequencies (0.5 - 5 Hz)--are more similar to those determined by BOLD. (4) Despite many similarities, differences emerge across modes including the spatial distribution of membership diversity (the extent to which regions affiliate with multiple networks). In sum, Ca2+ imaging of excitatory neurons confirms that the mouse cortex is functionally organized into overlapping large-scale networks in a manner that reflects many, but not all, properties observable with simultaneous fMRI-BOLD; affirming the neural origins of patterns of brain organization that are evident in a clinically accessible neuroimaging modality.

neuroscience↗

bioRxiv2022

Identifying dynamic reproducible brain states using a predictive modelling approach

Conceptually brain states reflect some combination of the internal mental process of a person, and the influence of their external environment. Importantly, for neuroimaging, brain states may impact brain-behavior modeling of a persons traits, which should be independent of moment-to-moment changes in behavior. A common way to measure both brain states and traits is to use functional connectivity based on functional MRI data. Brain states can fluctuate in time periods shorter than a typical fMRI scan, and a family of methods called dynamic functional connectivity analyses, have been developed to capture these short time estimates of brain states. There has been a rise in the use of dynamic functional connectivity in order to find temporally specific spatial patterns of connectivity which reflect brain states, that can yield further insight into traits and behaviors. It has previously been shown that brain state can be manipulated through the use of continuous performance tasks that put the brain in a particular configuration while the task is performed. Here we focus on moment-to-moment changes in brain state and test the hypothesis that there are particular brain-states that maximize brain-trait modeling performance. We use a regression-based brain-behavior modelling framework, Connectome-based Predictive Modelling, allied to a resample aggregating approach, to identify behavior and trait related short time brain states, as represented by dynamic functional connectivity maps. We find that there is not a particular brain state that is optimal for trait-based prediction, and drawing data from across the scan is better. We also find that this not the case for in-magnet behavioral prediction where more isolated and temporally specific parts of the scan session are better for building predictive models of behavior. The resample aggregated dynamic functional connectivity models of behavior replicated within sample using unseen HCP data. The modelling framework also showed success in the estimating variance behavior in the ABCD dataset when using data from that dataset. The method detailed here may prove useful for both the study of behaviorally related brain states, and for short time predictive modelling.

neuroscience↗

bioRxiv2022

A preprocessing and functional connectivity analysis pipeline for mesoscale calcium imaging

To improve bench-to-bedside translation, it is integral that knowledge flow bidirectionally--from animal models to humans, and vice versa. This requires common analytical frameworks, as well as open software and data sharing practices. We share a new pipeline (and test dataset) for the preprocessing of wide-field optical fluorescence imaging data--an emerging mode applicable in animal models--as well as results from a functional connectivity and graph theory analysis inspired by recent work in the human neuroimaging field. The approach is demonstrated using a dataset comprised of two test-cases: (1) data from animals imaged during awake and anesthetized conditions with excitatory neurons labeled, and (2) data from awake animals with different genetically encoded fluorescent labels that target either excitatory neurons or inhibitory interneuron subtypes. Both seed-based connectivity and graph theory measures (global efficiency, transitivity, modularity, and characteristic path-length) are shown to be useful in quantifying differences between wakefulness states and cell populations. Wakefulness state and cell type show widespread effects on canonical network connectivity with variable frequency band dependence. Differences between excitatory neurons and inhibitory interneurons are observed, with somatostatin expressing inhibitory interneurons emerging as notably dissimilar from parvalbumin and vasoactive polypeptide expressing cells. In sum, we demonstrate that our pipeline can be used to examine brain state and cell-type differences in mesoscale imaging data, aiding translational neuroscience efforts. In line with open science practices, we freely release the pipeline and data to encourage other efforts in the community.

neuroscience↗

bioRxiv2020

Causal evidence of network communication in whole-brain dynamics through a multiplexed neural code

An important question in neuroscience is how local activity can be flexibly and selectively routed across the brain network. A proposed mechanism to flexibly route information is frequency division multiplexing: selective readout can be achieved by segregating the signal into non-overlapping frequency bands. Here, in wild-type mice and in a transgenic model (3xTgAD) of Alzheimers Disease (AD), we use optogenetic activation of the entorhinal cortex, concurrent whole-brain fMRI, and hidden Markov modeling. We demonstrate how inducing neuronal spiking with different theta frequencies causes spatially distinct states of brain network dynamics to emerge and to preferentially respond to one frequency, showing how selective information streams can arise from a single neuronal source of activity. This theta modulation mechanism, however, is impaired in the AD model. This work demonstrates that neuronal multiplexing is a sufficient mechanism to enable flexible brain network communication, and provides insight into the aberrant mechanisms underlying cognitive decline.

neuroscience↗

bioRxiv2020

Local and global dichotomic dysfunction in resting and evoked functional connectivity precedes tauopathy

Functional network activity alterations are one of the earliest hallmarks of Alzheimers disease (AD), detected prior to amyloidosis and tauopathy. Better understanding the neuronal underpinnings of such network alterations could offer mechanistic insight into AD progression. Here, we examined a mouse model (early-tauopathy 3xTgAD mice) recapitulating this early AD stage. We found resting functional connectivity loss within ventral networks, including the entorhinal cortex, aligning with the spatial distribution of tauopathy reported in humans. Unexpectedly, in contrast to decreased connectivity at rest, 3xTgAD mice show enhanced fMRI signal within several projection areas following optogenetic activation of the entorhinal cortex. We corroborate this finding by demonstrating neuronal facilitation within ventral networks and synaptic hyperexcitability in projection targets. 3xTgAD mice thus reveal a dichotomic hypo-connected resting/hyper-responsive active phenotype. The strong homotopy between the areas affected supports the translatability of this pathophysiological model to tau-related deficits in humans.

neuroscience↗

bioRxiv2020

Conjugates of neuroprotective chaperone L-PGDS provide MRI contrast for detection of amyloid β-rich regions in live Alzheimer's Disease mouse model brain

Endogenous brain proteins can recognize the toxic oligomers of amyloid-{beta} (A{beta}) peptides implicated in Alzheimers disease (AD) and interact with them to prevent their aggregation. Lipocalin-type Prostaglandin D Synthase (L-PGDS) is a major A{beta}-chaperone protein in the human cerebrospinal fluid. Here we demonstrate that L-PGDS detects amyloids in diseased mouse brain. Conjugation of L-PGDS with magnetic nanoparticles enhanced the contrast for magnetic resonance imaging. We conjugated the L-PGDS protein with ferritin nanocages to detect amyloids in the AD mouse model brain. We show here that the conjugates administered through intraventricular injections co-localize with amyloids in the mouse brain. These conjugates can target the brain regions through non-invasive intranasal administration, as shown in healthy mice. These conjugates can inhibit the aggregation of amyloids in vitro and show potential neuroprotective function by breaking down the mature amyloid fibrils.

biochemistry↗

bioRxiv2020

Gradients of functional connectivity in the mouse cortex reflect neocortical evolution

Understanding cortical organization is a fundamental goal of neuroscience that requires comparisons across species and modalities. Large-scale connectivity gradients have recently been introduced as a data-driven representation of the intrinsic organization of the cortex. We studied resting-state functional connectivity gradients in the mouse cortex and found robust spatial patterns across four data sets. The principal gradient of functional connectivity shows a striking overlap with an axis of neocortical evolution from two primordial origins. Additional gradients reflect sensory specialization and aspects of a sensory-to-transmodal hierarchy, and are associated with transcriptomic features. While some of these gradients strongly resemble observations in the human cortex, the overall pattern in the mouse cortex emphasizes the specialization of sensory areas over a global functional hierarchy. HighlightsO_LIThe principal gradient of functional connectivity in the mouse cortex recapitulates an axis of neocortical evolution from archicortex and paleocortex. C_LIO_LIAdditional gradients highlight sensory specialization and reflect aspects of a sensory-to-transmodal hierarchy. C_LIO_LIFunctional connectivity gradients partly align with gene expression patterns. C_LIO_LIMouse cortical gradients are stable across data sets. C_LI

neuroscience↗

bioRxiv2019

Common functional networks in the mouse brain revealed by multi-centre resting-state fMRI analysis

Preclinical applications of resting-state functional magnetic resonance imaging (rsfMRI) offer the possibility to non-invasively probe whole-brain network dynamics and to investigate the determinants of altered network signatures observed in human studies. Mouse rsfMRI has been increasingly adopted by numerous laboratories world-wide. Here we describe a multi-centre comparison of 17 mouse rsfMRI datasets via a common image processing and analysis pipeline. Despite prominent cross-laboratory differences in equipment and imaging procedures, we report the reproducible identification of several large-scale resting-state networks (RSN), including a murine default-mode network, in the majority of datasets. A combination of factors was associated with enhanced reproducibility in functional connectivity parameter estimation, including animal handling procedures and equipment performance. Our work describes a set of representative RSNs in the mouse brain and highlights key experimental parameters that can critically guide the design and analysis of future rodent rsfMRI investigations.

neuroscience↗