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In vivo MRI at mesoscopic resolution (0.1-0.5 mm) enables detailed visualization of the brain’s angioarchitecture, which can be examined across multiple spatial scales, including (1) leptomeningeal vessels, (2) pial vessels, and (3) intracortical vessels. Recently, using multi-shot, multi-echo 3D EPI with T2* contrast, we have achieved substantial advances in imaging the venous angioarchitecture in living humans at 0.35 mm isotropic resolution. Our optimized imaging protocol provides whole-brain coverage in under seven minutes, making the mesoscopic angioarchitecture imaging both feasible and practicable for a wide range of neuroimaging studies. Additionally, we have developed novel processing and analysis methods to enhance visualization and quantification of vascular structures across spatial scales. The combination of our imaging and analysis advancements open new opportunities for studying cerebrovascular function in relation to cortical layers and columns, as well as for anatomical investigations in developmental and clinical research. [more]

A key goal of cognitive neuroscience is to generate an understanding of the functional neuroanatomy of cortical systems. Using computational encoding models and the visual system as a model system, I will describe recent empirical and computational innovations that have advanced understanding of key cognitive neuroscience questions: How do spatiotemporal computations by population receptive fields contribute to spatial and temporal integration across processing streams? What spatial/spatiotemporal computations explain elusive phenomena, such as simultaneous suppression? What computational constraints may lead to the formation of maps in visual cortex? In the first part, I will describe new empirical and computational frameworks we have developed and validated – spatiotemporal receptive fields (st-PRFs) that estimate from fMRI data the spatial and temporal summation windows of each voxel in the visual system (units of visual degrees and milliseconds). Then, using st-pRFs, we elucidate neural spatial and temporal windows across the entire visual system and assess how simple, bottom-up computations together with nonlinearities like transients, contribute to simultaneous suppression. In the second part, I will describe a new kind of deep neural network that we have developed – topographic deep neural network (TDANN). Different from a typical DNN, TANN model units are arranged retinotopically on a simulated cortical sheet and during self-supervised training the loss function encourages units on the simulated cortical sheet to have correlated responses. We find that this single unified principle predicts the function and spatial topography of maps at different scales in the visual system– from orientation, spatial frequency, and color patches in V1, to category-selective clusters in ventral temporal cortex, to the formation of three processing streams spanning the occipital, temporal, and parietal cortices. [more]

Magnetic susceptibility imaging has evolved significantly since the introduction of susceptibility-weighted imaging (SWI), which was initially developed for clinical applications such as detecting microhemorrhages. In the mid-2000s, advancements in high-resolution phase imaging led to the development of quantitative susceptibility mapping (QSM), a method that allows for the quantitative assessment of magnetic susceptibility in the brain. QSM has opened up new possibilities for neuroimaging, particularly in the quantification of iron in deep brain structures, a biomarker associated with various neurological conditions. While QSM has provided valuable insights, it has primarily been limited to measuring a combined signal from different sources of magnetic susceptibility. In the brain, iron and myelin are the two dominant contributors, each with opposite magnetic properties— iron being paramagnetic and myelin being diamagnetic. This limitation has motivated the need for more refined techniques to disentangle these distinct sources of susceptibility. In this presentation, I will introduce a technique developed in our lab called susceptibility source separation. This method provides a breakthrough by allowing the separate quantification of myelin and iron in the brain. Through susceptibility source separation, we can now obtain high-resolution maps that independently profile the distribution of myelin and iron. This is particularly valuable for studying the brain, where myelin and iron play crucial roles in both healthy brain function and the progression of neurological diseases. I will discuss the underlying physics that enables this separation, including the biophysical modeling and algorithm employed. The validation of the method has been demonstrated through various approaches including histology, showing its accuracy in separating the signals of myelin and iron. The clinical potential of this technique is significant, with early studies indicating its applicability to diseases such as multiple sclerosis, where both iron deposition and myelin degradation are key pathological features. In addition, the method holds promise for more accurate mapping of cortical myelination, which could improve our understanding of a variety of neurodegenerative diseases and brain aging. [more]

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Despite the vital role of scientific software, it remains an overlooked part of research, often developed within short funding periods with little support for long-term maintenance. This results in software that is hard to discover and challenging to install. It also lacks interoperability across different computing systems, hindering its reuse and violating the FAIR principles - which advocate for scientific outputs to be Findable, Accessible, Interoperable, and Reusable. In this talk, I will present our attempts at this problem through the Neurodesk.org project, and I will show what we are planning next. [more]

Scrutiny of the cortical neuronal circuits underlying human visual perception typically involves the summarization of large-scale recordings of brain activity under different perceptual states, with the combination of various measurement modalities and modeling techniques being critical in revealing organizing principles. In this seminar, we'll delve into the relationship between anatomical structure and evolving patterns of neuronal functional connectivity across the early visual foveal cluster (V1-V2-V3). I will show how we can inform our understanding of visual perception through different recording modalities, combining high-resolution fMRI and laminar electrophysiology with computational modeling. I will present key findings on task-dependent modulation of directed interactions across visual cortical areas in humans and laminar distinctions in visual processing in Macaque, as well as touch on preliminary validation work. Finally, I look forward to discussing new advancements and techniques and to providing a clearer picture of neuronal circuit dynamics at the mesoscopic level. [more]

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