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Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods., Competing Interests: Declaration of Competing Interest The authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Multimodal magnetic resonance imaging (MRI) has accelerated human neuroscience by fostering the analysis of brain microstructure, geometry, function, and connectivity across multiple scales and in living brains. The richness and complexity of multimodal neuroimaging, however, demands processing methods to integrate information across modalities and to consolidate findings across different spatial scales. Here, we present micapipe, an open processing pipeline for multimodal MRI datasets. Based on BIDS-conform input data, micapipe can generate i) structural connectomes derived from diffusion tractography, ii) functional connectomes derived from resting-state signal correlations, iii) geodesic distance matrices that quantify cortico-cortical proximity, and iv) microstructural profile covariance matrices that assess inter-regional similarity in cortical myelin proxies. The above matrices can be automatically generated across established 18 cortical parcellations (100-1000 parcels), in addition to subcortical and cerebellar parcellations, allowing researchers to replicate findings easily across different spatial scales. Results are represented on three different surface spaces (native, conte69, fsaverage5), and outputs are BIDS-conform. Processed outputs can be quality controlled at the individual and group level. micapipe was tested on several datasets and is available at https://github.com/MICA-MNI/micapipe, documented at https://micapipe.readthedocs.io/, and containerized as a BIDS App http://bids-apps.neuroimaging.io/apps/. We hope that micapipe will foster robust and integrative studies of human brain microstructure, morphology, function, cand connectivity., (Copyright © 2022. Published by Elsevier Inc.)

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process., (Copyright © 2021. Published by Elsevier Inc.)

Accurate extraction of the cortical brain surface is critical for cortical thickness estimation and a key element to perform multimodal imaging analysis, where different metrics are integrated and compared in a common space. While brain surface extraction has become widespread practice in human studies, several challenges unique to neuroimaging of non-human primates (NHP) have hindered its adoption for the study of macaques. Although, some of these difficulties can be addressed at the acquisition stage, several common artifacts can be minimized through image preprocessing. Likewise, there are several image analysis pipelines for human MRIs, but very few automated methods for extraction of cortical surfaces have been reported for NHPs and none have been tested on data from diverse sources. We present PREEMACS, a pipeline that standardizes the preprocessing of structural MRI images (T1- and T2-weighted) and carries out an automatic surface extraction of the macaque brain. Building upon and extending pre-existing tools, the first module performs volume orientation, image cropping, intensity non-uniformity correction, and volume averaging, before skull-stripping through a convolutional neural network. The second module performs quality control using an adaptation of MRIqc method to extract objective quality metrics that are then used to determine the likelihood of accurate brain surface estimation. The third and final module estimates the white matter (wm) and pial surfaces from the T1-weighted volume (T1w) using an NHP customized version of FreeSurfer aided by the T2-weighted volumes (T2w). To evaluate the generalizability of PREEMACS, we tested the pipeline using 57 T1w/T2w NHP volumes acquired at 11 different sites from the PRIME-DE public dataset. Results showed an accurate and robust automatic brain surface extraction from images that passed the quality control segment of our pipeline. This work offers a robust, efficient and generalizable pipeline for the automatic standardization of MRI surface analysis on NHP., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2020. Published by Elsevier Inc.)

Objective: Temporal lobe epilepsy (TLE) is known to affect large-scale structural networks and cognitive function in multiple domains. The study of complex relations between structural network organization and cognition requires comprehensive analytical methods and a shift towards multivariate techniques. Here, we sought to identify multidimensional associations between cognitive performance and structural network topology in TLE., Methods: We studied 34 drug-resistant adult TLE patients and 24 age- and sex-matched healthy controls. Participants underwent a comprehensive neurocognitive battery and multimodal MRI, allowing for large-scale connectomics, and morphological evaluation of subcortical and neocortical regions. Using canonical correlation analysis, we identified a multivariate mode that links cognitive performance to a brain structural network. Our approach was complemented by bootstrap-based hierarchical clustering to derive cognitive subtypes and associated patterns of macroscale connectome anomalies., Results: Both methodologies provided converging evidence for a close coupling between cognitive impairments across multiple domains and large-scale structural network compromise. Cognitive classes presented with an increasing gradient of abnormalities (increasing cortical and subcortical atrophy and less efficient white matter connectome organization in patients with increasing degrees of cognitive impairments). Notably, network topology characterized cognitive performance better than morphometric measures did., Conclusions: Our multivariate approach emphasized a close coupling of cognitive dysfunction and large-scale network anomalies in TLE. Our findings contribute to understand the complexity of structural connectivity regulating the heterogeneous cognitive deficits found in epilepsy., Competing Interests: Declaration of competing interest None., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Micro-architectural characteristics of white matter can be inferred through analysis of diffusion-weighted magnetic resonance imaging (dMRI). The diffusion-dependent signal can be analyzed through several methods, with the tensor model being the most frequently used due to its straightforward interpretation and low requirements for acquisition parameters. While valuable information can be gained from the tensor-derived metrics in regions of homogeneous tissue organization, this model does not provide reliable microstructural information at crossing fiber regions, which are pervasive throughout human white matter. Several multiple fiber models have been proposed that seem to overcome the limitations of the tensor, with few providing per-bundle dMRI-derived metrics. However, biological interpretations of such metrics are limited by the lack of histological confirmation. To this end, we developed a straightforward biological validation framework. Unilateral retinal ischemia was induced in ten rats, which resulted in axonal (Wallerian) degeneration of the corresponding optic nerve, while the contralateral was left intact; the intact and injured axonal populations meet at the optic chiasm as they cross the midline, generating a fiber crossing region in which each population has different diffusion properties. Five rats served as controls. High-resolution ex vivo dMRI was acquired five weeks after experimental procedures. We correlated and compared histology to per-bundle descriptors derived from three methodologies for dMRI analysis (constrained spherical deconvolution and two multi-tensor representations). We found a tight correlation between axonal density (as evaluated through automatic segmentation of histological sections) with per-bundle apparent fiber density and fractional anisotropy (derived from dMRI). The multi-fiber methods explored were able to correctly identify the damaged fiber populations in a region of fiber crossings (chiasm). Our results provide validation of metrics that bring substantial and clinically useful information about white-matter tissue at crossing fiber regions. Our proposed framework is useful to validate other current and future dMRI methods., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Numerous animal studies have shown the applicability of diffusion tensor imaging (DTI) to track Wallerian degeneration that occurs after injury to the neural fiber. Non-invasive biomarkers that may differentiate the early axonal breakdown and later myelin degradation have been attributed to either reduced parallel and elevated perpendicular diffusivity, respectively. While several human DTI studies have shown this potential at subacute and chronic time points, the diffusion changes that occur within the first week are unknown. Anterior temporal lobectomy (i.e. resection of hippocampus) is the standard surgical treatment of medically refractory temporal lobe epilepsy. The concomitant transection of the fimbria-fornix serves as a unique opportunity to examine the process of Wallerian degeneration since the timing is known. Six temporal lobe epilepsy patients underwent brain DTI before the surgery, three to four times within the first week post-operatively, and at one to four months following surgery. Both parallel and perpendicular diffusivities decreased markedly by a similar amount in the ipsilateral fornix within the first two days post-surgery. Approaching the end of the first week, perpendicular (but not parallel) diffusivity pseudo-recovered towards its pre-surgical value, but then increased dramatically months later. Fractional anisotropy, as a result of the combined action of the parallel and perpendicular diffusivities, stayed relatively stable within the first week and only reduced drastically at the chronic stage. DTI demonstrated acute water diffusion changes within days of transection that are not just limited to parallel diffusivity. While the chronic diffusion changes in the fornix are compatible with myelin degradation, the acute changes may reflect beading and swelling of axolemma, granular disintegration of the axonal neurofilaments, ischemia induced cytotoxic edema, and/or changes in the extra-axonal space including inflammatory changes and gliosis., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Temporal lobe epilepsy (TLE) is considered primarily a limbic disorder. Our purpose was to map limbic network organization in TLE and to statistically relate it to neocortical atrophy. We performed MRI-based cortical thickness analysis in 110 TLE patients (including 68 patients with hippocampal atrophy and 42 patients with normal hippocampal volume) and 46 healthy controls. Limbic connectivity was statistically inferred by correlating mean thickness of the entorhinal cortex (EC) with thickness at each vertex across the entire neocortex. The EC was chosen as seed region since it is the link between the neocortex and the hippocampal formation. Patients showed cortical thinning mainly in temporal and fronto-central neocortices, with a prevalence of atrophy in up to 35%. In controls, EC networks corresponded closely to known anatomical connections. In TLE the pattern of correlations was similar to controls, suggesting that pathological processes in the EC affect the same networks that co-vary with the EC in the healthy brain. Nevertheless, we found decreases in correlations mainly in the temporal lobe and increases mainly in orbitofrontal cortices. Although our analysis indicated alterations in the temporo-limbic network in TLE, there was no association between mesiotemporal connectivity and atrophy across the entire cortical surface. This divergence underlines the complexity of the pathophysiological mechanisms leading to neocortical atrophy in TLE.

Axonal degeneration of white matter fibers is a key consequence of neuronal or axonal injury. It is characterized by a series of time-related events with initial axonal membrane collapse followed by myelin degradation being its major hallmarks. Standard imaging cannot differentiate these phenomena, which would be useful for clinical investigations of degeneration, regeneration and plasticity. Animal models suggest that diffusion tensor magnetic resonance imaging (DTI) is capable of making such distinction. The applicability of this technique in humans would permit inferences on white matter microanatomy using a non-invasive technique. The surgical bisection of the anterior 2/3 of the corpus callosum for the palliative treatment of certain types of epilepsy serves as a unique opportunity to assess this method in humans. DTI was performed on three epilepsy patients before corpus callosotomy and at two time points (1 week and 2-4 months) after surgery. Tractography was used to define voxels of interest for analysis of mean diffusivity, fractional anisotropy and eigenvalues. Diffusion anisotropy was reduced in a spatially dependent manner in the genu and body of the corpus callosum at 1 week and remained low 2-4 months after the surgery. Decreased anisotropy at 1 week was due to a reduction in parallel diffusivity (consistent with axonal fragmentation), whereas at 2-4 months, it was due to an increase in perpendicular diffusivity (consistent with myelin degradation). DTI is capable of non-invasively detecting, staging and following the microstructural degradation of white matter following axonal injury.

Reading is a complex cognitive skill that requires the coordination of multiple brain regions. Although functional neuroimaging studies highlight the cortical brain regions associated with a specific cognitive task like reading, they do not directly address the underlying neural connections necessary for efficient performance of this task. Adults with reading disability have demonstrated lower regional white matter connectivity, but it is not known whether this relationship between neuronal wiring and reading performance also holds in younger readers. Using diffusion tensor magnetic resonance imaging (DTI) that highlights the structural integrity of the brain wiring, we show that regional brain connectivity in the left temporo-parietal white matter correlates with a wide range of reading ability in children as young as 8-12 years old. Diffusion tensor tractography suggests that the posterior limb of the internal capsule is consistent with the location of the largest cluster of correlation between reading ability (Word Identification subtest) and fractional anisotropy. The maturation of the white matter may play a key role in the development of cognitive processes such as reading.