The human thalamus is a brain structure that comprises numerous, highly specific nuclei. Since these nuclei are known to have different functions and to be connected to different areas of the cerebral cortex, it is of great interest for the neuroimaging community to study their volume, shape and connectivity in vivo with MRI. In this study, we present a probabilistic atlas of the thalamic nuclei built using ex vivo brain MRI scans and histological data, as well as the application of the atlas to in vivo MRI segmentation. The atlas was built using manual delineation of 26 thalamic nuclei on the serial histology of 12 whole thalami from six autopsy samples, combined with manual segmentations of the whole thalamus and surrounding structures (caudate, putamen, hippocampus, etc.) made on in vivo brain MR data from 39 subjects. The 3D structure of the histological data and corresponding manual segmentations was recovered using the ex vivo MRI as reference frame, and stacks of blockface photographs acquired during the sectioning as intermediate target. The atlas, which was encoded as an adaptive tetrahedral mesh, shows a good agreement with previous histological studies of the thalamus in terms of volumes of representative nuclei. When applied to segmentation of in vivo scans using Bayesian inference, the atlas shows excellent test-retest reliability, robustness to changes in input MRI contrast, and ability to detect differential thalamic effects in subjects with Alzheimer's disease. The probabilistic atlas and companion segmentation tool are publicly available as part of the neuroimaging package FreeSurfer.

The ventral occipitotemporal cortex (vOTC) is crucial for recognizing visual patterns, and previous evidence suggests that there may be different subregions within the vOTC involved in the rapid identification of word forms. Here, we characterize vOTC reading circuitry using a multimodal approach combining functional, structural, and quantitative MRI and behavioral data. Two main word-responsive vOTC areas emerged: a posterior area involved in visual feature extraction, structurally connected to the intraparietal sulcus via the vertical occipital fasciculus; and an anterior area involved in integrating information with other regions of the language network, structurally connected to the angular gyrus via the posterior arcuate fasciculus. Furthermore, functional activation in these vOTC regions predicted reading behavior outside of the scanner. Differences in the microarchitectonic properties of gray-matter cells in these segregated areas were also observed, in line with earlier cytoarchitectonic evidence. These findings advance our understanding of the vOTC circuitry by linking functional responses to anatomical structure, revealing the pathways of distinct reading-related processes.

Whether phonological deficits in developmental dyslexia are associated with impaired neural sampling of auditory information at either syllabic- or phonemic-rates is still under debate. In addition, whereas neuroanatomical alterations in auditory regions have been documented in dyslexic readers, whether and how these structural anomalies are linked to auditory sampling and reading deficits remains poorly understood. In this study, we measured auditory neural synchronization at different frequencies corresponding to relevant phonological spectral components of speech in children and adults with and without dyslexia, using magnetoencephalography. Furthermore, structural MRI was used to estimate cortical thickness of the auditory cortex of participants. Dyslexics showed atypical brain synchronization at both syllabic (slow) and phonemic (fast) rates. Interestingly, while a left hemispheric asymmetry in cortical thickness was functionally related to a stronger left hemispheric lateralization of neural synchronization to stimuli presented at the phonemic rate in skilled readers, the same anatomical index in dyslexics was related to a stronger right hemispheric dominance for neural synchronization to syllabic-rate auditory stimuli. These data suggest that the acoustic sampling deficit in development dyslexia might be linked to an atypical specialization of the auditory cortex to both low and high frequency amplitude modulations.

Neuroimaging software methods are complex, making it a near certainty that some implementations will contain errors. Modern computational techniques (i.e., public code and data repositories, continuous integration, containerization) enable the reproducibility of the analyses and reduce coding errors, but they do not guarantee the scientific validity of the results. It is difficult, nay impossible, for researchers to check the accuracy of software by reading the source code; ground truth test datasets are needed. Computational reproducibility means providing software so that for the same input anyone obtains the same result, right or wrong. Computational validity means obtaining the right result for the ground-truth test data. We describe a framework for validating and sharing software implementations, and we illustrate its usage with an example application: population receptive field (pRF) methods for functional MRI data. The framework is composed of three main components implemented with containerization methods to guarantee computational reproducibility. In our example pRF application, those components are: (1) synthesis of fMRI time series from ground-truth pRF parameters, (2) implementation of four public pRF analysis tools and standardization of inputs and outputs, and (3) report creation to compare the results with the ground truth parameters. The framework was useful in identifying realistic conditions that lead to imperfect parameter recovery in all four pRF implementations, that would remain undetected using classic validation methods. We provide means to mitigate these problems in future experiments. A computational validation framework supports scientific rigor and creativity, as opposed to the oft-repeated suggestion that investigators rely upon a few agreed upon packages. We hope that the framework will be helpful to validate other critical neuroimaging algorithms, as having a validation framework helps (1) developers to build new software, (2) research scientists to verify the software's accuracy, and (3) reviewers to evaluate the methods used in publications and grants.