Lectures

Lecture #1

Carmen Cavada

Medical School, Universidad Autónoma de Madrid, Madrid, Spain

Human Thalamus anatomy and connections with the cortex and subcortical regions

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The human thalamus is a large ovoid mass of gray matter located next to the brain midline, on the walls of the third ventricle. The cyto-, myelo-, and chemo-architectures reveal great internal heterogeneity within the thalamus. The main body of the thalamus has been named “dorsal” thalamus; it is bordered laterally by a thin sheet of neurons and passing axons, the “prethalamus” thalamus or reticular nucleus. The main body of the thalamus and the reticular nucleus have diverse ontogenies and connections.

Following concepts from the modern neuromeric model of brain development, the dorsal thalamus derives from prosomer 2 and the reticular nucleus from prosomer 3. The reticular nucleus receives projections from the cortex and subcortical regions, including the remaining thalamic nuclei, and sends inhibitory projections to the latter. By contrast, the dorsal thalamic nuclei send and receive excitatory projections, mainly to and from the cortex; they are also connected with subcortical regions.

The primate dorsal thalamus, including the human thalamus, contains a sizeable population of interneurons in addition to the projection neurons. The parcellation of the thalamus in nuclear groups and nuclei will be shown; and, using a functional perspective, sensory relay nuclei, motor relay nuclei, association and limbic nuclei will be presented, together with their main connections with the cortex and subcortical regions. Additionally, the classification of thalamic afferents as drivers and modulators will be reviewed, along with the concept of cortico-thalamo-cortical communication.

In summary, the human thalamus will be presented as a crucial structure in various brain functions, including sensory and motor processing, attention, consciousness, sleep, and cognition. Additionally, the differences between the human thalamus and those of other mammals will be presented.

Lecture #2

Elena Borra

Department of Medicine and Surgery, University of Parma, Parma, Italy.

Callosal connectivity: insight from non-human primate research

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The corpus callosum is a distinctive feature of the mammalian brain and is by far the largest fiber tract, linking both corresponding (homotopic) and non-corresponding (heterotopic) areas of the two cerebral hemispheres and cortical areas with the contralateral striatum (crossed corticostriatal projections). In the rhesus monkey, it contains approximately 56 million axons, while in humans, it comprises around 200 million axons.

There is broad consensus that callosal connections between sensorimotor areas play a key role in midline fusion, i.e., the integration of sensorimotor processes from both hemispheres into a unified perception and coordinated bimanual movement. Additional research has pointed to the importance of callosal connections in motor areas for contralateral learning transfer, a process in which training one hand facilitates skill acquisition in the opposite hand, and has linked microstructural changes in the corpus callosum to prolonged bimanual motor training 12.

Furthermore, callosal pathways between higher-order cortical areas are thought to contribute to the integration and synchronization of neural activity across hemispheres. According to 3 these connections may exert a "conditional driving" or modulatory influence on their postsynaptic targets, depending on the context and demands of a given task.

Early studies based on partial or complete section of the corpus callosum first described the overall pattern of callosal connectivity in non-human primates (e.g., 4). Several subsequent studies based on neural tracers injections in the non-human primate brain have described the callosal connectivity of different cortical sectors. More recent studies 567 have provided qualitative and quantitative data on the callosal connectivity, highlighting features of complexity and regional difference.

Altogether, these studies provide an updated general framework for understanding the interactions between the two hemispheres in sensorimotor integration and cognitive functions and the neuronal mechanisms underlying the role of cortical areas in the functional recovery after lesions in the contralateral hemisphere.

  1. Pauwels L, Gooijers J (2023) Journal of Motor Behavior The Role of the Corpus Callosum (Micro)Structure in Bimanual Coordination: A Literature Review Update.
  2. Nuara A, Mancuso A, Bazzini M, et al (2025) Asymmetric relationship between transcallosal inhibition and contralateral learning transfer of hand motor skills. Brain Stimul 18:1153–1155.
  3. Innocenti GM, Schmidt K, Milleret C, et al (2022) The functional characterization of callosal connections. Prog Neurobiol 208.
  4. Pandya DN, Karol EA, Heilbronn D (1971) The topographical distribution of interhemispheric projections in the corpus callosum of the rhesus monkey. Brain Res 32:31–43.
  5. Borra E, Biancheri D, Rizzo M, et al (2022) Crossed Corticostriatal Projections in the Macaque Brain. Journal of Neuroscience 42:7060–7076.
  6. Szczupak D, Iack PM, Rayêe D, et al (2023) The relevance of heterotopic callosal fibers to interhemispheric connectivity of the mammalian brain. Cerebral Cortex 33:4752–4760.
  7. Rizzo M, Luppino G, Borra E. (2024) Quantitative and qualitative analysis of the callosal connectivity in the macaque brain. Program PSTR027.10/G7. 2024 Neuroscience Meeting Planner. Chicago, IL: Society for Neuroscience.

Lecture #3

Hiroki Oishi

1. Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Japan
2. Core for Spin Life Sciences, Okazaki Collaborative Platform, Okazaki, Japan
3. Graduate Institute for Advanced Studies, The Graduate University for Advanced Studies (SOKENDAI), Hayama, Japan

Microstructural organization of the primate visual pathway revealed by histology and quantitative MRI

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The primate visual system is characterized by a topographic organization that preserves the spatial structure of visual input, alongside functional specialization that assigns distinct computational roles to different visual areas. Ex vivo histology in non-human primates has been the primary approach for revealing key aspects of the anatomical microarchitecture underlying this functional organization. Histological studies have identified visually discernible anatomical modules in early visual areas, such as cytochrome oxidase blobs in the primary visual area (V1) and the tripartite stripe system in V2.

However, in higher visual areas, including the inferotemporal (IT) cortex, where obvious structural landmarks are absent, the anatomical architecture that supports functional specialization has remained poorly understood. Furthermore, because histology inherently requires ex vivo tissue sampling, much less is known about the anatomical microarchitecture supporting visual function and specialization in the human brain. Recent methodological advances provide new opportunities to overcome these limitations.

In the first part of this lecture, I will highlight progress in three-dimensional histological mapping in humans and non-human primates (e.g., BigBrain and BigMac), which enable registration to MRI spaces and facilitate the integration of histology with in vivo functional MRI (fMRI). These developments allow functionally defined regions to be localized within histological data and permit depth-resolved quantitative analysis of cortical microarchitecture, revealing previously undetectable structural differentiation even in regions such as IT cortex. Moreover, 3D histology makes it possible to examine the correspondence between large-scale topographic organization, such as retinotopy, and fine-scale microarchitecture across the cortical sheet.

In the second part, I will review recent advances in quantitative MRI (qMRI), which enables non- invasive in vivo microstructural mapping of tissue properties such as myelination and iron content across the entire visual pathway. By integrating qMRI with high-resolution fMRI, recent studies have begun to reveal fine-scale anatomical microarchitecture underlying functional specialization in the human visual system, both within individuals and across populations.

Lecture #4

Paule-Joanne Toussaint

McGill University, Montreal Neurological Institute and Hospital, Montreal, Quebec, Canada

Functional correlates of metabolic changes and cognitive decline measured by PET: Exploration of univariate and multivariate approaches

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Positron emission tomography (PET) data are classically used to elucidate brain interactions by means of inter-subject correlational analysis. The methods to do so have evolved from the measurement of regional coupling using pairwise correlations, to more elaborate data-driven voxel-based approaches involving univariate and multivariate techniques. These latter analyses yield a set of spatial components, or regions, with a distribution evoking the underlying anatomy, and a signal covariance structure that can capture the essential features of functional connectivity.

The disruption of brain regions interactions is an important component in our understanding of brain pathologies, such as neurodegenerative diseases and dementias. In this presentation I will first explore the spatially distributed covariation of fluorodeoxyglucose (FDG) PET measurements (across subjects and/or scans) using univariate and multivariate analyses, as these covariations may be critical to understand or predict the disease under study.

Then, I will address the selection process for anatomo-functional networks or regions that are most representative of the change between healthy and patient populations. Last, I will discuss implications of FDG PET-derived functional connectivity measures on the characterisation and early prediction of Alzheimer's disease progression within a multi-modality framework, by combining PET issued measures with other biomarkers of the disease derived from MRI measurements, biological samples, and scores from neuropsychological testing.

Lecture #5

Dora Hermes

Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA

Learning about human neuroanatomy with intracranial brain stimulation

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The world of connectomics is growing both at the microscale, where synaptic connectomes of small volumes are discovered, and at the macroscale, where imaging methods track white matter bundles with increasing precision and resolution. Still, it remains a challenge to understanding how neural signals propagate across such networks. Before diffusion imaging was invented, electrical stimulation was already used to map effective connectivity by applying single electrical pulses to an implanted electrode and measuring how the signal propagated to connected sites.

Intracranial EEG (iEEG) electrodes implanted for clinical purposes in human patients provide unique opportunities to measure such neural signal propagation in human brain networks. I will discuss how these Brain Stimulation Evoked Potential (BSEP) measurements provide several insights about human neuroanatomy:

First, stimulation evoked potentials reveal select anatomical connectivity at the millimeter scale. Second, stimulation-based measures of effective connectivity quantify the strength of influence from different anatomical structures on directly and indirectly connected areas. Third, early evoked potentials can provide information about the speed of signal propagation in white matter pathways.

These different insights allow unique insight into the anatomy of living human brains and capture the long developmental trajectory of human brain networks across the lifespan.

Lecture #6

Henry Kennedy

1- Stem cell and Brain Research Institute, INSERM U1208, 18 Avenue Doyen Lepine, 69500 Bron, France.
2- Université de Lyon, Université Lyon I, 69003, Lyon, France

The Spatially-Embedded Brain

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Brain-wide retrograde tracing experiments in macaque, can be used to generate a consistent database of between area connectivity and subcortical projections to cortex with projection densities, distances and laminar distributions 12.

This shows that the network is neither a sparse small-world graph nor scale-free 3. Because local connectivity accounts for 80% of labeled neurons 4, one is driven to the conclusion that the cortex is heavily involved in local function. Importantly link weights, are highly characteristic across animals, follow a heavy-tailed lognormal distribution over 6 orders of magnitude, and decay exponentially with distance. The statistical properties of the cortex give insight into the nature of the processing mode of the cortex 5.

A weighted network analysis reveals a trade off between local and global efficiencies. An important finding is that a distance rule (EDR) predicts the binary features, the global and local communication efficiencies, clustered topography and the wire-minimization of the cortical graph 67. These findings underline the importance of weight-based hierarchical layering in cortical architecture and hierarchical processing 89.

We have therefore evaluated the shapes and dimensions of cortical areas, which place different parts of the same area in different neighborhoods, with respect to EDR predictions of connectivity. This shows that in the visual cortex the central representations are preferentially linked to the ventral stream and peripheral representations to the dorsal stream 2.

Altogether, analysis of quantitative measures of connectivity suggests evolutionary optimization of areal shape, location, and cortical folding and points to the need to consider the brain in space when considering the statistics of the inter-areal cortical network. I will briefly mention on-going work on the mouse connectome that shows that the EDR model applies equally well across different species and different brain sizes suggesting general principles of organization.

Interestingly however, the core-periphery structure, indicative of a global work space cognitive architecture, includes primary areas in mouse and uniquely higher order areas in macaque. The cerebral cortex is organized in a hierarchical fashion with input to the cortex arriving in the primary areas and then projecting up the cortical hierarchy via bottom-up pathways which are reciprocated by the twice as numerous top-down pathways 1011.

One interpretation of hierarchy is that it reflects bottom up and top-down signatures of long-distance connections integration into the local circuit. Because the top-down and bottom up pathways have distinct spatial and functional properties 1213 the convergence of these pathways represent a computational primitive allowing predictive coding, where the top-down pathway carries predictions and the bottom up pathways prediction errors 14.

While evidence on predictive coding is well established in mouse, this is not the case in primates 15. Our recent finding on the transcriptomics and connectomics of the claustrum 16 suggests that this telencephalic structure most likely plays distinctive role in predictive coding 15.

Because impairments of predictive processes likely lead to psychotic disease states, work in this area in primates is potentially medically urgent.

  1. Markov NT, Ercsey-Ravasz MM, Ribeiro Gomes AR, Lamy C, Magrou L, Vezoli J, Misery P, Falchier A, et al. (2014), A weighted and directed interareal connectivity matrix for macaque cerebral cortex. Cerebral Cortex 24:17-36.
  2. Wang M, Hou Y, Magrou L, Autio JA, Misery P, Coalson T, Reid E, Xu Y, et al. (2022), Retinotopic organization of feedback projections in primate early visual cortex: implications for active vision. bioRxiv:2022.2004.2027.489651.
  3. Markov NT, Ercsey-Ravasz M, Van Essen DC, Knoblauch K, Toroczkai Z, Kennedy H (2013), Cortical high-density counter-stream architectures. Science 342:1238406.
  4. Markov NT, Misery P, Falchier A, Lamy C, Vezoli J, Quilodran R, Gariel MA, Giroud P, et al. (2011), Weight Consistency Specifies Regularities of Macaque Cortical Networks. Cerebral Cortex 21:1254-1272.
  5. Markov NT, Kennedy H (2013), The importance of being hierarchical. Curr Opin Neurobiol 23:187-194.
  6. Ercsey-Ravasz M, Markov NT, Lamy C, Van Essen DC, Knoblauch K, Toroczkai Z, Kennedy H (2013), A predictive network model of cerebral cortical connectivity based on a distance rule. Neuron 80:184-197.
  7. Song HF, Kennedy H, Wang XJ (2014), Spatial embedding of structural similarity in the cerebral cortex. Proc Nat Acad Sci USA 111:16580-16585.
  8. Bastos AM, Vezoli J, Bosman CA, Schoffelen JM, Oostenveld R, Dowdall JR, De Weerd P, Kennedy H, et al. (2015), Visual Areas Exert Feedforward and Feedback Influences through Distinct Frequency Channels. Neuron 85:390-401.
  9. Michalareas G, Vezoli J, van Pelt S, Schoffelen JM, Kennedy H, Fries P (2016), Alpha-Beta and Gamma Rhythms Subserve Feedback and Feedforward Influences among Human Visual Cortical Areas. Neuron 89:384-397.
  10. Felleman DJ, Van Essen DC (1991), Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex 1:1-47.
  11. Markov NT, Vezoli J, Chameau P, Falchier A, Quilodran R, Huissoud C, Lamy C, Misery P, et al. (2014), The Anatomy of Hierarchy: Feedforward and feedback pathways in macaque visual cortex. Journal of Comparative Neurology 522:225-259.
  12. Vezoli J, Hou, Y., Kennedy, H. (2023) The evolving concept of cortical hierarchy. Oxford University Press.
  13. Vezoli J, Magrou L, Goebel R, Wang XJ, Knoblauch K, Vinck M, Kennedy H (2021), Cortical hierarchy, dual counterstream architecture and the importance of top-down generative networks. NeuroImage 225:117479.
  14. Rao RP, Ballard DH (1999), Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience 2:79-87.
  15. Hou Y, Vezoli J, Knoblauch K, Goebel R, Vinck M, Kennedy H (2025), Where, how and why do top-down and bottom-up signals interact in the primate brain? PsyArXiv.
  16. Lei Y, Liu Y, Wang M, Yuan N, Hou Y, Ding L, Zhu Z, Wu Z, et al. (2025), Single-cell spatial transcriptome atlas and whole-brain connectivity of the macaque claustrum. Cell.