Our research is motivated by the observation that a big explanatory gap exists between models of brain function at different levels of inquiry (i.e. from micro- to meso- and macro-scale). This knowledge gap prevents a reliable mechanistic interpretation of whole-brain imaging findings in human brain research, especially those revealing atypical or aberrant patterns of brain function and connectivity in developmental disorders.
Our research is designed to bridge this explanatory gap. One way to achieve this goal is by implementing whole-brain neuroimaging methods in physiologically accessible species, like the laboratory mouse. This approach rests on the combined use of neuroimaging methods widely used in human research (such as fMRI) with targeted perturbational approaches (e.g. transgenics, chemogenetics, otpogenetics) that together can be used to bridge investigational scales and establish causal relationships between specific neural events and patterns of brain connectivity.
Structure to Function Research Goals
Functional Organisation of the Mouse Brain
This research leverages the use of resting-state fMRI (rsfMRI) to obtain an integrated portrayal of the macroscale mouse functional connectome, i.e. the complex network of elements and connections that govern and compose the brain at the macroscale. Our lab established the methods for the reliable implementation of rsfMRI in the mouse brain, an approach that is now used by an increasing number of labs in the world.
As part of this effort, we demonstrated that the mouse brain included plausible homologues of human distributed networks of high relevance such as the salience and “default mode network” (DMN).
We are now using multidisciplinary approaches to investigate the functional organization, network topology and dynamics structure of the mouse functional connectome via the use of different computational approaches, including graph theory, multivariate statistics, and point process analyses. Our efforts complement ongoing activities aimed at mapping the macroscale organization of the laboratory mouse, with the final goal of describe brain function as the integration of processes occurring at multiple scales.
Relevant publications:
fMRI organization of the mouse brain and characterization of mouse salience and default-mode-networks:
Gutierrez-Barragan, D., Singh, N. A., Alvino, F. G., Coletta, L., Rocchi, F., de Guzman, E., Galbusera, A., Uboldi, M., Panzeri, S., & Gozzi, A. (2022). Current Biology, 32(3), 631-644.e6.
Regional, Layer, and Cell-Type-Specific Connectivity of the Mouse Default Mode Network.
Whitesell, J. D., Liska, A., Coletta, L., Hirokawa, K. E., Bohn, P., Williford, A., Groblewski, P. A., Graddis, N., Kuan, L., Knox, J. E., Ho, A., Wakeman, W., Nicovich, P. R., Nguyen, T. N., van Velthoven, C. T. J., Garren, E., Fong, O., Naeemi, M., Henry, A. M., … Harris, J. A. (2021). Neuron, 109(3), 545-559.e8.
Coletta, L., Pagani, M., Whitesell, J. D., Harris, J. A., Bernhardt, B., & Gozzi, A. (2020). Science Advances, 6(51).
ERC Grant
Towards a physiological decoding of brain dysconnectivity
Aberrant functional connectivity as measured with resting state fMRI (rsfMRI) is a hallmark feature of brain connectopathy in psychiatric, developmental and neurological disorders. However, fundamental questions as to the origin and significance of rsfMRI-based dysconnectivity remain open: what are the fundamental elements governing the establishment of brain-wide functional connectivity? And how are these involved in the manifestation of functional dysconnectivity in brain disorders?
Funded by the European Research Council (ERC, #Disconn), our lab has started a research program aimed to unravel the developmental and neuro-physiological cascade underlying the establishment of brain connectivity. The ultimate goal of this research is to enable a reverse engineering of human functional dysconnectivity into a set of physiologically interpretable events that can aid the diagnosis, stratification and treatment of human brain disorders.
From an experimental standpoint, we aim to achieve this goal by combining awake mouse rsfMRI with cell-type specific manipulation of brain function at different scales (synapses, neurons and networks). As part of this effort, we established a novel research platform entailing the combined use of DREADD-based chemogenetics and fMRI (chemo-fMRI) enabling steady-state manipulations of neural activity that are optimally suited to deconstruct rsfMRI. This has led to the observation that silencing cortical activity can paradoxically result in patterns of hyperconnectivity and increased delta electrophysiological rhythms – a counterintuitive finding that has important implications for brain modelling and interpretation of overconnectivity in degenerative disorders. Ongoing efforts in the lab will expand these investigations to reverse-engineer under-connectivity states using opto/chemogenetics perturbations.
Giorgi, A., Migliarini, S., Galbusera, A., Maddaloni, G., Mereu, M., Margiani, G., Gritti, M., Landi, S., Trovato, F., Bertozzi, S. M., Armirotti, A., Ratto, G. M., de Luca, M. A., Tonini, R., Gozzi, A., & Pasqualetti, M. (2017). Cell Reports, 21(4), 910–918.
Gozzi, A., Jain, A., Giovanelli, A., Bertollini, C., Crestan, V., Schwarz, A. J., Tsetsenis, T., Ragozzino, D., Gross, C. T., & Bifone, A. (2010). Neuron, 67(4), 656–666.
Autism Research Goals
Molecular decoding of brain dysconnectivity in complex brain disorders (e.g., autism)
Atypical functional connectivity is a key etiopathological finding in autism. However connectivity abnormalities in people with autism are highly heterogeneous. This observation has spurred controversy, as human neuroimagers have been hoping rsfMRI and other brain mapping tehcniques would allow to “see” autism in the brain (i.e. reveal a specific signature of network dysfunction that is specific to the condition): why is functional connectivity so heterogeneous across patient cohorts, and what is the significance of this heterogeneity?
A prevalent interpretation of this finding is that the observed heterogeneity is artefactual, and reflects the act that rsfMRI and other imaging methods lack reproducibility as they are noisy and unreliable.However, an equally plausible explanation could be that the etiological variability that characterizes the autism spectrum could actually be a primary determinant of connectivity heterogeneity in autism. To test this hypothesis, we have been implementing fMRI-based connectivity in multiple mouse lines recapitulating autism-relevant genetic mutations, as part of a research program funded by the Simmons Foundation (SFARI). This approach allows to isolate and model autism-related etiologies with great precision, while tightly controlling the genetic, environmental and experimental (i.e. head motion) confounds that plague clinical rsfMRI imaging.
Our results revealed that different Autism-associated etiologies cause a broad spectrum of connectional abnormalities in which diverse and often diverging, functional connectivity signatures are recognizable. This important result goes to show that the etiological variability of the autism spectrum is a key determinant of connectivity heterogeneity, hence reconciling conflicting findings in clinical populations.
Importantly, our work also revealed that, despite this heterogeneity, connectivity alterations can be classified into distinct and segregable connectivity subtypes. In keeping with this, we also recently showed that a specific form of autism-related synaptic pathology can be clinically decoded in human rsfMRI databases using a signature of connectivity identified in the mouse. We are now expanding these investigations to other connectivity subtypes. We believe these are important result that can help unravel autism connectopathy, improve diagnostic label accuracy in autism population, and that might eventually lead to personalized treatment approaches.
Relevant publications:
Decoding human autism with cross-species fMRI
Pagani, M., Barsotti, N., Bertero, A., Trakoshis, S., Ulysse, L., Locarno, A., Miseviciute, I., de Felice, A., Canella, C., Supekar, K., Galbusera, A., Menon, V., Tonini, R., Deco, G., Lombardo, M. v., Pasqualetti, M., & Gozzi, A. (2021). Nature Communications 2021 12:1, 12(1), 1–15.
Bertero, A., Liska, A., Pagani, M., Parolisi, R., Masferrer, M. E., Gritti, M., Pedrazzoli, M., Galbusera, A., Sarica, A., Cerasa, A., Buffelli, M., Tonini, R., Buffo, A., Gross, C., Pasqualetti, M., & Gozzi, A. (2018). Brain, 141(7), 2055–2065.
Mapping the connectional landscape in autism with mouse fMRI
Zerbi, V., Pagani, M., Markicevic, M., Matteoli, M., Pozzi, D., Fagiolini, M., Bozzi, Y., Galbusera, A., Scattoni, M. L., Provenzano, G., Banerjee, A., Helmchen, F., Basson, M. A., Ellegood, J., Lerch, J. P., Rudin, M., Gozzi, A., & Wenderoth, N. (2021). Molecular Psychiatry 2021 26:12, 26(12), 7610–7620.
Signature of functional dysconnectivity in mouse mutants (Shank3, Chd8, Cntnap2, 16p11.2del)
Pagani, M., Bertero, A., Liska, A., Galbusera, A., Sabbioni, M., Barsotti, N., Colenbier, N., Marinazzo, D., Scattoni, M. L., Pasqualetti, M., & Gozzi, A. (2019). Journal of Neuroscience, 39(27), 5299–5310.
Suetterlin, P., Hurley, S., Mohan, C., Riegman, K. L. H., Pagani, M., Caruso, A., Ellegood, J., Galbusera, A., Crespo-Enriquez, I., Michetti, C., Yee, Y., Ellingford, R., Brock, O., Delogu, A., Francis-West, P., Lerch, J. P., Scattoni, M. L., Gozzi, A., Fernandes, C., & Basson, M. A. (2018). Cerebral Cortex, 28(6), 2192–2206.
Liska, A., Bertero, A., Gomolka, R., Sabbioni, M., Galbusera, A., Barsotti, N., Panzeri, S., Scattoni, M. L., Pasqualetti, M., & Gozzi, A. (2018). Cerebral Cortex, 28(4), 1141–1153.
Bertero, A., Liska, A., Pagani, M., Parolisi, R., Masferrer, M. E., Gritti, M., Pedrazzoli, M., Galbusera, A., Sarica, A., Cerasa, A., Buffelli, M., Tonini, R., Buffo, A., Gross, C., Pasqualetti, M., & Gozzi, A. (2018). Brain, 141(7), 2055–2065.
