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Multimodal Integrative Imaging of Neurodegeneratives Diseases and Therapies (MIINDt)

Maladie d'Alzheimer et vieillissement cérébral : modélisation, imagerie, biomarqueurs, évaluations thérapeutiques

Group leader: Marc Dhenain
 (DR1CNRS, French Veterinary Academy, National Academy of Medicine)​

Published on 21 May 2021


  Marc Dhenain 

Our Team is dedicated to i. the characterization of biological mechanisms involved in the development of Alzheimer's disease and its therapies. ii. the development of innovative imaging tools to follow-up cerebral pathologies. We focus on 3D microscopic imaging methods based on high performing computing (HPC) algorithms, and on resting state fMRI to evaluate neuronal networks.

Transmission of Alzheimer's disease lesions: new tools to characterize the disease

Our group has demonstrated that the key lesions of Alzheimer's disease (β-amyloid and tau lesions) are transmissible (Gary, 2019). This transmission makes it possible to explore the pathophysiogenic mechanisms of Alzheimer's disease and to test new therapies. Using novel models based on AD lesion transmission, we have shown that synapse loss is linked to tau pathology and that activation of inflammatory brain cells called microglia limits synaptic loss (Lam, 2021). We are now seeking to understand the mechanisms underlying the heterogeneity of Alzheimer's disease through an integrative and holistic approach that considers all aspects of the disease (amyloid / tau / inflammation / functional impairment).

In particular, we focus on the role of various nucleating factors that regulate AD pathophysiology. Either nucleating factors extracted from human brain samples (Collaboration with Stephane Haik and Susana Boluda from ICM, Paris) or nucleating factors based on very purified forms of amyloid with very specific mutations (collaboration with Alain Buisson from Grenoble Institute of Neurosciences).

In addition to studies in mice, one of our strength is to be able to work in primates and in particular with mouse lemur primates. The latter is a model of neurodegenerative pathologies linked to aging. This animal presents, as it ages, cognitive alterations, alterations in cerebral metabolism, cerebral atrophy and amyloid deposits.

.Click onthe image to enlarge 

Amyloid and tau lesions induced in the brain of experimental models following inoculation of amyloid 
and tau nucleating factors.

An integrative approach to understand neurodegenerative diseases

Neurodegenerative diseases are related to many different "small scale events" (pathological protein accumulation, neuroinflammation, cellular alterations) that lead to large-scale events (tissue loss, neuronal networks alterations, cognitive impairments). Our team develop tools to integrate events occurring at different scales. These new tools require advanced imaging skills combined with big data management and high performance computing.

Overview of the range of imaging methods implemented by our group.
State of the art methods to manipulate large amount of data and require high performance computing.

1) Quantification of microscopic brain alterations

Our group implemented several image-processing pipelines to perform 3D post mortem reconstruction program in primates and rodents, while focusing on the exploitation of 3D information in rodents.
3D-reconstructed brain samples can be analyzed using semi-automatic manual analysis, digital atlas-based analysis (Lebenberg, 2011) or voxel-wise SPM approach without a priori (Vandenberghe, 2018). The method can be used to detect lesions as amyloid plaque related to Alzheimer (Vandenberghe, 2018).

New methods were then implemented to quantify cells at the level of the whole brain. They are based on virtual microscopy performed after digitization of brain sections stained for cells (NeuN antibody) and on the segmentation of each cell (Random Forest -RF- machine learning algorithm) and individualization methods (You, 2019 & 2021). Performing this analysis at the level of the whole brain allows to create parametric maps synthesizing for example the morphology and distribution of individualized neurons.
Then, it is possible to synthesize this information in the form of lower-resolution parametric maps at the level of anatomical regions, sections and even, eventually, the entire brain. This step converts qualitative color microscopic images to quantitative mesoscopic images, more informative and easier to analyse, to statistically assess neuronal death or relationships between neuronal densities and brain function assessed from in vivo images.
New research to develop digital twins of brains has been initiated recently to exploit information from 3D histology to produce realistic mathematical models of cytoarchitecture.
These approaches will open perspectives to validate new imaging techniques (MRI) and to better understand and decipher the mechanisms at the cellular level. High performance computing (HPC) resources are integrated in our research projects to deal with massive data analysis and increasing algorithm complexity. This requires strong methodological development, performed though a collaboration with the TGCC of the CEA (Bruyères-le-Châtel). This structure hosts a supercomputer that is one of the ten most powerful machines in the world.

These tools are mainly developed using in-house software platform BrainVISA (


Methods used to evaluate neuronal density and other neuronal parameters based on high performance computing.
Brain sections stained for cells (NeuN antibody) are segmented and various parameters reflecting neuronal characteristics
(e.g. their density, size, orientation, etc…) are calculated.
Based on this analysis, we can produce parametric maps reflecting neuronal states at the level of the whole brain.
These maps can then be compared with other maps (lesion, brain function, etc…).

2) Quantification of neuronal networks

Individual cells function in a harmonized way that leads to harmonious brain activity through functional networks. These networks can be assessed by resting state functional MRI and sophisticated image processing tools. Our group studies brain activity with resting state functional imaging.
We participated in the development of the reference article, which, through a multicentre evaluation, defines the optimal conditions for carrying out network imaging in rodents (Grandjean, 2020).
We have developed software for analysing neural networks (Celestine, 2020;
We compare neural networks in primates and humans and seek to define how evolution has modulated these networks (Garin, 2021).

Example of detection of neural networks in humans and in the smallest primate in the world (mouse lemur)
by magnetic resonance imaging.

Members of the laboratory associated with these projects

  • Jean-Luc Picq (researcher, Professor)
  • Luc Bousset (researcher)
  • Mehdi Kabani (researcher)
  • Thierry Delzescaux (researcher)
  • Anne-Sophie Hérard (research engineer - researcher)
  • Nicolas Souedet (research engineer)
  • Fanny Petit (technician)
  • Suzanne Lam (PhD student)
  • Marina Celesetine (PhD student)
  • Huaqian Wu (PhD student)
  • Sebastien Piluso (PhD student)
  • Marie-Claude Gaillard (engineer)
  • Martine Guillermier (engineer)
Recent selected publications


Evaluation of automated segmentation algorithms for neurons in macaque cerebral microscopic images.

You, Z., M. Jiang, Z. Shi, X. Ning, C. Shi, S. Du, A. S. Hérard, C. Jan, N. Souedet, Delzescaux T.
Microscopy Research and Technique. 2021: 27 Apr 2021:

Alzheimer’s brain inoculation in Aß-plaque bearing mice: synaptic loss is linked to tau seeding and low microglial activity.
Lam S., Boluda S., Hérard A.S., Petit F., Eddarkaoui S., Cambon K., The Brainbank Neuro-CEB Neuropathology Network, Picq J.L., Buée L., Duyckaerts C., Haïk S., Dhenain M.
BioRxiv. 2021.

Resting state functional atlas and cerebral networks in mouse lemur primates at 11.7 Tesla.
Garin C. M., Nadkarni N. A., Landeau B., Chételat G., Picq J-L, Bougacha S., Dhenain M.  
NeuroImage, 226, 117589, 2021.


Induction of amyloid-beta deposits from serially transmitted, histologically silent, A-beta seeds issued from human brains. 
Herard A.S., Petit F., Gary C., Guillermier M., Boluda S., Garin C. M., French Neuropathology Network, Lam S., Dhenain M.
 Acta Neuropathologica Communications. 8, Article number: 205, 2020.

 Modèles primates et innovations thérapeutiques contre les maladies du système nerveux central.
Dhenain M.
La Lettre de l'Académie des Sciences. 2020. n°40, p34. 2020 - 

Effects of chronic masitinib treatment in APPPS1dE9 transgenic mice modeling Alzheimer's disease, 2020.
Li T., Martin E., Abada Y., Boucher C., Cès A., Youssef I., Fenaux G., Forand Y., Legrand A., Nadkarni N., Dhenain M., Hermine O., Dubreuil P., Delarasse C., Delatour B.
Journal of Alzheimer's Disease. 76.4. 1339-1345, 2020.

Sammba-MRI, a library for small animal neuroimaging data processing in Python.
Celestine M.*, Nadkarni N.A.*, Garin C., Bougacha S.*, Dhenain M. 
Frontiers in NeuroInformatics. 28 May 2020 | (These three authors participated equally to the work)

An automated open-source workflow for standards-compliant integration of small animal magnetic resonance imaging data.
Ioanas H-I., Marks M., Garin C. M., Dhenain M., Yanik M. F., Rudin M..  
Frontiers in Neuroinformatics. 2020.

Animal functional magnetic resonance imaging: Trends and path toward standardization.
Mandino F., Cerri D. H., Garin C. M., Straathof M., van Tilborg G. A. F., Chakravarty M. M., Dhenain M., Dijkhuizen R. M., Gozzi A., Hess A., Keilholz S. D., Lerch J. P., Ian Shih Y-Y., Grandjean J.
Frontiers in Neuroinformatics. 2020. Vol. 13. Art 78.

Common functional networks in the mouse brain revealed by multi-centre resting-state fMRI analysis.
Grandjean J., Canella C., Anckaerts C., Ayrancı G, Bougacha S., Bienert T., Buehlmann D., Coletta L., Gallino D., Gass N., Garin C. M. , Nadkarni N. A. , Hübner N., Karatas M., Komaki Y., Kreitz S., Mandino F., Mechling A. E., Sato C., Sauer K., Shah D., Strobelt S., Takata N., Wank I., Wu T., Yahata N., Yun Yeow L., Yee Y., Aoki I. , Chakravarty M. M., Chang W-T., Dhenain M., Von Elverfeldt D., Harsan L. A., Hess A., Jiang T., Keliris G. A., Lerch J. P., Okano H., Rudin M., Sartorius A., Van der Linden A, Verhoye M., Weber-Fahr W., Wenderoth N., Zerbi V., Gozzi A.
NeuroImage. 2020. 205, Article 116278.

Estimation of COVID-19 cases in France and in different countries: Homogeneisation based on mortality.
Dhenain Marc.  


Encephalopathy induced by Alzheimer brain inoculation in a non-human primate.
Gary C., Lam S.*, Herard A.S.*, Koch J.E., Petit F., Gipchtein P., Sawiak S.J., Caillierez R., Eddarkaoui S., Colin M., Aujard F., Deslys J.P., French Neuropathology Network, Brouillet E., Buée L., Comoy E.E., Pifferi F.*, Picq J-L*, Dhenain M., 
Acta Neuropathologica Communications. 2019. 7: 126.

Promoting healthspan and lifespan with caloric restriction in primates.
Pifferi F., Terrien J., Perret M., Epelbaum J., Blanc S., Picq J.L.*, Dhenain M.*, Aujard F.
Communication Biology, Nature Publishing Group. 2019. 2, 107. 7

 A 3D population-based brain atlas of the mouse lemur primate with examples of applications in aging studies and comparative anatomy.
Nadkarni N. A, Bougacha S., Garin C., Dhenain M., Picq J.-L.
NeuroImage. 2019. 185. 85-95.

Tridimensional mapping of Phox2b expressing neurons in the brainstem of adult Macaca fascicularis and identification of the retrotrapezoid nucleus.
Levy J., Facchinetti P., Jan C., Achour M., Bouvier C., Brunet J.F., Delzescaux T., Giuliano F. 
Journal of Comparative Neurology. 2019. May 9. 1-10.

Automated individualization of size-varying neurons in 2D microscopic images of macaque brain
You Z, Balbastre Y, Bouvier C., Souedet N., Gipchtein P, Hantraye P, Jan C, Herard A-S, Delzescaux T. 
Front Neuroanat. 2019 Dec 17;13:98.


Voxel-based statistical analysis of 3D immunostained tissue imaging.
Vandenberghe, M.E., Souedet, N., Herard, A.S., Ayral, A.M., Letronne, F., Balbastre, Y., Sadouni, E., Hantraye, P., Dhenain, M., Frouin, F., Lambert, J.C., Delzescaux, T.  
Frontiers in Neuroscience. 2018; 12(article number 754).

A combination of atlas-based and voxel-wise approaches to analyze metabolic changes in autoradiographic data from Alzheimer's mice. 
Lebenberg, J., Herard, A.S., Dubois, A., Dhenain, M., Hantraye, P., Delzescaux, T. 
Neuroimage. 2011. 57(4): 1447-1457.