DTI mapping of human brain connectivity: statistical fibre tracking and virtual dissection

From Brownian motion to virtual biopsy: a historical perspective from 40 years of diffusion MRI

Diffusion MRI was introduced in 1985, showing how the diffusive motion of molecules, especially water, could be spatially encoded with MRI to produce images revealing the underlying structure of biologic tissues at a microscopic scale. Diffusion is one of several Intravoxel Incoherent Motions (IVIM) accessible to MRI together with blood microcirculation. Diffusion imaging first revolutionized the management of acute cerebral ischemia by allowing diagnosis at an acute stage when therapies can still work, saving the outcomes of many patients. Since then, the field of diffusion imaging has expanded to the whole body, with broad applications in both clinical and research settings, providing insights into tissue integrity, structural and functional abnormalities from the hindered diffusive movement of water molecules in tissues. Diffusion imaging is particularly used to manage many neurologic disorders and in oncology for detecting and classifying cancer lesions, as well as monitoring treatment response at an early stage. The second major impact of diffusion imaging concerns the wiring of the brain (Diffusion Tensor Imaging, DTI), allowing to obtain from the anisotropic movement of water molecules in the brain white-matter images in 3 dimensions of the brain connections making up the Connectome. DTI has opened up new avenues of clinical diagnosis and research to investigate brain diseases, neurogenesis and aging, with a rapidly extending field of application in psychiatry, revealing how mental illnesses could be seen as Connectome spacetime disorders. Adding that water diffusion is closely associated to neuronal activity, as shown from diffusion fMRI, one may consider that diffusion MRI is ideally suited to investigate both brain structure and function. This article retraces the early days and milestones of diffusion MRI which spawned over 40 years, showing how diffusion MRI emerged and expanded in the research and clinical fields, up to become a pillar of modern clinical imaging.

Robust fiber orientation distribution function estimation using deep constrained spherical deconvolution for diffusion-weighted magnetic resonance imaging

Purpose

Diffusion-weighted magnetic resonance imaging (DW-MRI) is a critical imaging method for capturing and modeling tissue microarchitecture at a millimeter scale. A common practice to model the measured DW-MRI signal is via fiber orientation distribution function (fODF). This function is the essential first step for the downstream tractography and connectivity analyses. With recent advantages in data sharing, large-scale multisite DW-MRI datasets are being made available for multisite studies. However, measurement variabilities (e.g., inter- and intrasite variability, hardware performance, and sequence design) are inevitable during the acquisition of DW-MRI. Most existing model-based methods [e.g., constrained spherical deconvolution (CSD)] and learning-based methods (e.g., deep learning) do not explicitly consider such variabilities in fODF modeling, which consequently leads to inferior performance on multisite and/or longitudinal diffusion studies.

Approach

In this paper, we propose a data-driven deep CSD method to explicitly constrain the scan-rescan variabilities for a more reproducible and robust estimation of brain microstructure from repeated DW-MRI scans. Specifically, the proposed method introduces a three-dimensional volumetric scanner-invariant regularization scheme during the fODF estimation. We study the Human Connectome Project (HCP) young adults test-retest group as well as the MASiVar dataset (with inter- and intrasite scan/rescan data). The Baltimore Longitudinal Study of Aging dataset is employed for external validation.

Results

From the experimental results, the proposed data-driven framework outperforms the existing benchmarks in repeated fODF estimation. By introducing the contrastive loss with scan/rescan data, the proposed method achieved a higher consistency while maintaining higher angular correlation coefficients with the CSD modeling. The proposed method is assessing the downstream connectivity analysis and shows increased performance in distinguishing subjects with different biomarkers.

Conclusion

We propose a deep CSD method to explicitly reduce the scan-rescan variabilities, so as to model a more reproducible and robust brain microstructure from repeated DW-MRI scans. The plug-and-play design of the proposed approach is potentially applicable to a wider range of data harmonization problems in neuroimaging.

Estimating the distributional impact of innovation platforms on income of smallholder maize farmers in Nigeria

This research studies the distributional effects of IP adoption on the farm income of smallholder maize farmers in Nigeria in an effort to move beyond the standard mean impact assessment of agricultural interventions. In order to account for selection bias that may result from both observed and unobserved factors, the study used a conditional instrumental variable quantile treatment effects (IV-QTE) strategy. The use of IPs greatly affects the revenue distributions of maize producers, as empirical evidence from the outcomes shows. Particularly, the impacts of adoption are stronger at the lower tails and just above the mean of the income distributions, indicating that impoverished farming households benefit more from the strategic functions of IP adoption in boosting income. These findings highlight how important it is to effectively target and disseminate improved agricultural technologies in order to increase the revenue of smallholder maize farmers in Nigeria from maize production. Agricultural research information and access to extension services are two policy tools that can help improve the successful adoption and diffusion of any agricultural intervention without favoring any particular groups.

Deep Constrained Spherical Deconvolution for Robust Harmonization

Diffusion weighted magnetic resonance imaging (DW-MRI) captures tissue microarchitecture at millimeter scale. With recent advantages in data sharing, large-scale multi-site DW-MRI datasets are being made available for multi-site studies. However, DW-MRI suffers from measurement variability (e.g., inter- and intra-site variability, hardware performance, and sequence design), which consequently yields inferior performance on multi-site and/or longitudinal diffusion studies. In this study, we propose a novel, deep learning-based method to harmonize DW-MRI signals for a more reproducible and robust estimation of microstructure. Our method introduces a data-driven scanner-invariant regularization scheme to model a more robust fiber orientation distribution function (FODF) estimation. We study the Human Connectome Project (HCP) young adults test-retest group as well as the MASiVar dataset (with inter- and intra-site scan/rescan data). The 8th order spherical harmonics coefficients are employed as data representation. The results show that the proposed harmonization approach maintains higher angular correlation coefficients (ACC) with the ground truth signals (0.954 versus 0.942), while achieves higher consistency of FODF signals for intra-scanner data (0.891 versus 0.826), as compared with the baseline supervised deep learning scheme. Furthermore, the proposed data-driven framework is flexible and potentially applicable to a wider range of data harmonization problems in neuroimaging.

Gradual changes in microarchitectural properties of cortex and juxtacortical white matter: Observed by anatomical and diffusion MRI

PURPOSE

Characterization of cerebral cortex is challenged by the complexity and heterogeneity of its cyto- and myeloarchitecture. This study evaluates quantitative MRI metrics, measured across two cortical depths and in subcortical white matter (WM) adjacent to cortex (juxtacortical WM), indicative of myelin content, neurite density, and diffusion microenvironment, for a comprehensive characterization of cortical microarchitecture.

METHODS

High-quality structural and diffusion MRI data (N = 30) from the Human Connectome Project were processed to compute myelin index, neurite density index, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity from superficial cortex, deep cortex, and juxtacortical WM. The distributional patterns of these metrics were analyzed individually, correlated to one another, and were compared to established parcellations.

RESULTS

Our results supported that myeloarchitectonic and the coexisting cytoarchitectonic structures influence the diffusion properties of water molecules residing in cortex. Full cortical thickness showed myelination patterns similar to those previously observed in humans. Higher myelin indices with similar distributional patterns were observed in deep cortex whereas lower myelin indices were observed in superficial cortex. Neurite density index and other diffusion MRI derived parameters provided complementary information to myelination. Reliable and reproducible correlations were identified among the cortical microarchitectural properties and fiber distributional patterns in proximal WM structures.

CONCLUSION

We demonstrated gradual changes across the cortical sheath by assessing depth-specific cortical micro-architecture using anatomical and diffusion MRI. Mutually independent but coexisting features of cortical layers and juxtacortical WM provided new insights towards structural organizational units and variabilities across cortical regions and through depth.

Multivalent Gd-DOTA Decorated Oligopeptide as Sensitive MRI Molecular Probes for In Vivo Imaging of Brain Connectivity

One of the most important goals of brain imaging is to define the anatomical connections within the brain. In addition to revealing normal circuitry, studies of neural connections and neuronal transport can show rewiring and degeneration following brain injury and diseases. In this work, a highly sensitive magnetic resonance imaging (MRI)-visible neural tracer that can be used to visualize brain connectivity in vivo is developed. It is based on an oligopeptide with gadolinium chelates appended to the peptide backbone. This peptide construct is a sensitive MRI contrast agent that was conjugated to the classical neurotracer, Cholera-toxin Subunit-B. Injection of this probe enabled it to be used to trace neural connections in vivo. This complements other MRI tracing techniques such as diffusion tensor imaging and manganese-enhanced MRI for neural tracing.

Multimodal striatal neuromarkers in distinguishing parkinsonian variant of multiple system atrophy from idiopathic Parkinson's disease

AIMS

To develop an automatic method of classification for parkinsonian variant of multiple system atrophy (MSA-P) and Idiopathic Parkinson's disease (IPD) in early to moderately advanced stages based on multimodal striatal alterations and identify the striatal neuromarkers for distinction.

METHODS

77 IPD and 75 MSA-P patients underwent 3.0 T multimodal MRI comprising susceptibility-weighted imaging, resting-state functional magnetic resonance imaging, T1-weighted imaging, and diffusion tensor imaging. Iron-radiomic features, volumes, functional and diffusion scalars of bilateral 10 striatal subregions were calculated and provided to the support vector machine for classification RESULTS: A combination of iron-radiomic features, function, diffusion, and volumetric measures optimally distinguished IPD and MSA-P in the testing dataset (accuracy 0.911 and area under the receiver operating characteristic curves [AUC] 0.927). The diagnostic performance further improved when incorporating clinical variables into the multimodal model (accuracy 0.934 and AUC 0.953). The most crucial factor for classification was the functional activity of the left dorsolateral putamen.

CONCLUSION

The machine learning algorithm applied to multimodal striatal dysfunction depicted dorsal striatum and supervening prefrontal lobe and cerebellar dysfunction through the frontostriatal and cerebello-striatal connections and facilitated accurate classification between IPD and MSA-P. The dorsolateral putamen was the most valuable neuromarker for the classification.

Automated computation of nerve fibre inclinations from 3D polarised light imaging measurements of brain tissue

The method 3D polarised light imaging (3D-PLI) measures the birefringence of histological brain sections to determine the spatial course of nerve fibres (myelinated axons). While the in-plane fibre directions can be determined with high accuracy, the computation of the out-of-plane fibre inclinations is more challenging because they are derived from the amplitude of the birefringence signals, which depends e.g. on the amount of nerve fibres. One possibility to improve the accuracy is to consider the average transmitted light intensity (transmittance weighting). The current procedure requires effortful manual adjustment of parameters and anatomical knowledge. Here, we introduce an automated, optimised computation of the fibre inclinations, allowing for a much faster, reproducible determination of fibre orientations in 3D-PLI. Depending on the degree of myelination, the algorithm uses different models (transmittance-weighted, unweighted, or a linear combination), allowing to account for regionally specific behaviour. As the algorithm is parallelised and GPU optimised, it can be applied to large data sets. Moreover, it only uses images from standard 3D-PLI measurements without tilting, and can therefore be applied to existing data sets from previous measurements. The functionality is demonstrated on unstained coronal and sagittal histological sections of vervet monkey and rat brains.

Along-tract analysis of the white matter is more informative about brain ageing, compared to whole-tract analysis

Diffusion Tensor Imaging (DTI) enabled the investigation of brain White Matter (WM), both qualitatively to study the macrostructure, and quantitatively to study the microstructure. The quantitative analyses are mostly performed at the whole-tract level, i.e., providing one measure of interest per tract; however, along-tract approaches may provide finer details of the quality of the WM tracts. In this study, using the DWI data collected from 40 young and 40 old individuals, we compared the DTI measures of FA, MD, AD, and RD, estimated by both whole-tract and along-tract approaches in 18 WM bundles, between the two groups. The results of the whole-tract quantitative analysis showed a statistically significant (p-FWER < 0.05) difference between the old and young groups in 6 tracts for FA, 8 tracts for MD, 1 tract for AD, and 7 tracts for RD. On the contrary, the along-tract approach showed differences between the two groups in 10 tracts for FA, 14 tracts for MD, 8 tracts for AD, and 11 tracts for RD. All the differences between the along-tract measures of the two groups had a large effect size (Cohen'd > 0.80). This study showed that the along-tract approach for the analysis of brain WM reveals changes in some WM tracts which had not shown any changes in the whole-tract approach, and therefore this finding emphasizes the utilization of the along-tract approach along with the whole-tract method for a more accurate study of the brain WM.

A Case for 3D Integrated System Design for Neuromorphic Computing and AI Applications

Over the last decade, artificial intelligence (AI) has found many applications areas in the society. As AI solutions have become more sophistication and the use cases grew, they highlighted the need to address performance and energy efficiency challenges faced during the implementation process. To address these challenges, there has been growing interest in neuromorphic chips. Neuromorphic computing relies on non von Neumann architectures as well as novel devices, circuits and manufacturing technologies to mimic the human brain. Among such technologies, three-dimensional (3D) integration is an important enabler for AI hardware and the continuation of the scaling laws. In this paper, we overview the unique opportunities 3D integration provides in neuromorphic chip design, discuss the emerging opportunities in next generation neuromorphic architectures and review the obstacles. Neuromorphic architectures, which relied on the brain for inspiration and emulation purposes, face grand challenges due to the limited understanding of the functionality and the architecture of the human brain. Yet, high-levels of investments are dedicated to develop neuromorphic chips. We argue that 3D integration not only provides strategic advantages to the cost-effective and flexible design of neuromorphic chips, it may provide design flexibility in incorporating advanced capabilities to further benefit the designs in the future.

Detection of functional connectivity in the brain during visuo-guided grip force tracking tasks: A functional near-infrared spectroscopy study

The functional connectivity (FC) between multiple brain regions during tasks is currently gradually being explored with functional near-infrared spectroscopy (fNIRS). However, the FC present during grip force tracking tasks performed under visual feedback remains unclear. In the present study, we used fNIRS to measure brain activity during resting states and grip force tracking tasks at 25%, 50%, and 75% of maximum voluntary contraction (MVC) in 11 healthy subjects, and the activity was measured from four target brain regions: the left prefrontal cortex (lPFC), right prefrontal cortex (rPFC), left sensorimotor cortex (lSMC), and right sensorimotor cortex (rSMC). We determined the FC between these regions utilizing three different methods: Pearson's correlation method, partial correlation method, and a pairwise maximum entropy model (MEM). The results showed that the FC of lSMC-rSMC and lPFC-rPFC (interhemispheric homologous pairs) were significantly stronger than those of other brain region pairs. Moreover, FC of lPFC-rPFC was strengthened during the 75% MVC task compared to the other task states and the resting states. The FC of lSMC-lPFC and rSMC-rPFC (intrahemispheric region pairs) strengthened with a higher task load. The results provided new insights into the FC between brain regions during visuo-guided grip force tracking tasks.

Myelination of Callosal Axons Is Hampered by Early and Late Forelimb Amputation in Rats

Deafferentation is an important determinant of plastic changes in the CNS, which consists of a loss of inputs from the body periphery or from the CNS itself. Although cortical reorganization has been well documented, white matter plasticity was less explored. Our goal was to investigate microstructural interhemispheric connectivity changes in early and late amputated rats. For that purpose, we employed diffusion-weighted magnetic resonance imaging, as well as Western blotting, immunohistochemistry, and electron microscopy of sections of the white matter tracts to analyze the microstructural changes in the corticospinal tract and in the corpus callosum (CC) sector that contains somatosensory fibers integrating cortical areas representing the forelimbs and compare differences in rats undergoing forelimb amputation as neonates, with those amputated as adults. Results showed that early amputation induced decreased fractional anisotropy values and reduction of total myelin amount in the cerebral peduncle contralateral to the amputation. Both early and late forelimb amputations induced decreased myelination of callosal fibers. While early amputation affected myelination of thinner axons, late amputation disrupted axons of all calibers. Since the CC provides a modulation of inhibition and excitation between the hemispheres, we suggest that the demyelination observed among callosal fibers may misbalance this modulation.

Neuroanatomical Reconstruction of the Canine Visual Pathway Using Diffusion Tensor Imaging

The first anatomical atlas of diffusion tensor imaging (DTI) of white matter pathways in the canine brain was published in 2013; however, the anatomical orientation of the entire visual pathway in the canine brain, from the retina to the cortex, has not yet been studied using DTI. In the present study, 3T DTI magnetic resonance (MR) images of three dogs euthanized for reasons other than neurological disorders were obtained. The process of obtaining combined fractional anisotropy and directional maps was initiated within 1 h of death. The heads were amputated immediately after MR imaging and stored in 10% formalin until dissection and histological sampling was performed. The trajectory of the visual pathway is dissimilar to the horizontal representation in other literature. To our knowledge, ours is the first study to visualize the entire canine visual pathway in its full antero-posterior extension. Fibers from the retina to the cortex passed through the optic nerve, optic chiasm, optic tracts, lateral geniculate nucleus, Meyer’s and Baum’s loops, and pretectal fibers. Their projections to the cortex were similar to those in the human visual pathway. The crossing of fibers at the optic chiasm occurred in 75% of fibers. In addition to advancing our knowledge in this field of study, these results could help plan neurosurgical and radiotherapeutic procedures to avoid unnecessary damage to the visual fiber system.

Diffusion tensor imaging and tractography for preoperative assessment of benign peripheral nerve sheath tumors

PURPOSE

To evaluate the diagnostic value of fiber tractography and diffusivity analysis generated from 3D diffusion-weighted (DW) sequences for preoperative assessment of benign peripheral nerve sheath tumors.

METHOD

MR imaging at 3 T was performed in 22 patients (mean age 41.9 ± 17.1y, 13 women) with histologically confirmed schwannomas (N = 18) and histologically confirmed neurofibromas (N = 11), including a 3D DW turbo spin echo sequence with fat suppression. Diffusion tensor parameters were computed and fiber tracks were determined. Evaluation was performed by two radiologists and one orthopedic surgeon blinded for final diagnosis. Mean diffusivity was computed to allow further assessment of tumor microstructure. Preoperative fascicle visualization was graded, fascicles were categorized regarding anatomical location and amount of fascicles surrounding the tumor. The agreement of imaging findings with intraoperative findings was assessed.

RESULTS

On 78.3 % of the DTI images, the fascicle visualization was rated as good or very good. Tractography differences were observed in schwannomas and neurofibromas, showing schwannomas to be significantly more often located eccentrically to the nerve (94.8 %) than neurofibromas (0 %, P < 0.01). Fascicles were significantly more often continuous (87.5 %) in schwannomas, while in neurofibromas, none of the tracks was graded to be continuous (0 %, P = 0.014). A substantial agreement between fiber tracking and surgical anatomy was found regarding the fascicle courses surrounding the tumor (κ = 0.78). Mean diffusivity of schwannomas (1.5 ± 0.2 × 10^-3 mm²/s) was significantly lower than in neurofibromas (1.8 ± 0.2 × 10^-3 mm²/s; P < 0.001). The Youden index showed an optimal cutoff at 1.7 × 10^-3 mm²/s (sensitivity, 0.91; specificity, 0.78; J = 0.69).

CONCLUSIONS

Preoperative diffusion tensor imaging allowed to accurately differentiate between schwannomas and neurofibromas and to describe their location in relation to the nerve fascicles for preoperative planning.

Direct evidence of the relationship between brain metastatic adenocarcinoma and white matter fibers: A fiber dissection and diffusion tensor imaging tractography study

It is commonly known that brain metastases usually have clear boundaries in magnetic resonance imaging. However, little is known regarding the trajectory of white matter fibers around the tumors, especially using the fiber dissection technique. Here, we focused on the anatomical interaction between white matter fibers and the tumor, using the fiber dissection in a postmortem brain with metastatic tumor and compared the findings with those of diffusion tensor imaging (DTI) tractography. One postmortem human brain hemisphere with metastatic adenocarcinoma in the Broca's area was dissected using fiber dissection following the Klingler's method. In order to compare the in vitro and in vivo results, additional brains from 15 patients with metastatic adenocarcinomas, the volumes of which were comparable to that of the adenocarcinoma in the brain used for fiber dissection, were analyzed using DTI tractographic reconstruction. Morphological findings of white matter bundles running around the tumor were compared between the two techniques. In the fiber dissection technique, the superior longitudinal fascicle, arcuate fascicle, and frontal aslant tract could be dissected, and the white matter bundles were curved and retracted to avoid the tumor. In all the cases analyzed, white matter fibers or streamlines surrounding the tumor avoided the lesion. Using the fiber dissection technique, this is the first direct evidence to elucidate the anatomy of white matter fibers affected by a metastatic brain. This suggests that brain metastatic adenocarcinoma is an intra-axial neoplasm with extra-axial white matter structures.

Navigable maps of structural brain networks across species

Connectomes are spatially embedded networks whose architecture has been shaped by physical constraints and communication needs throughout evolution. Using a decentralized navigation protocol, we investigate the relationship between the structure of the connectomes of different species and their spatial layout. As a navigation strategy, we use greedy routing where nearest neighbors, in terms of geometric distance, are visited. We measure the fraction of successful greedy paths and their length as compared to shortest paths in the topology of connectomes. In Euclidean space, we find a striking difference between the navigability properties of mammalian and non-mammalian species, which implies the inability of Euclidean distances to fully explain the structural organization of their connectomes. In contrast, we find that hyperbolic space, the effective geometry of complex networks, provides almost perfectly navigable maps of connectomes for all species, meaning that hyperbolic distances are exceptionally congruent with the structure of connectomes. Hyperbolic maps therefore offer a quantitative meaningful representation of connectomes that suggests a new cartography of the brain based on the combination of its connectivity with its effective geometry rather than on its anatomy only. Hyperbolic maps also provide a universal framework to study decentralized communication processes in connectomes of different species and at different scales on an equal footing.

Does the superior fronto-occipital fascicle exist in the human brain? Fiber dissection and brain functional mapping in 90 patients with gliomas

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Existence of superior fronto-occipital fascicle (SFOF) in humans is controversial.

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Fiber dissection in vitro revealed Muratoff and Probst bundles but not SFOF.

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Direct functional mappings for SFOF were performed in 90 awake craniotomies.

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Eight of total 453 positive sites were located in the region believed to be SFOF.

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The anatomo-functional features suggest that SFOF might not exist in human brain.

The presence of the superior fronto-occipital fascicle (SFOF) has been reported in the Rhesus monkey; however, it is a subject of controversy in humans. The aim of this study is to identify the SFOF using both in vitro and in vivo anatomo-functional analyses.

This study consisted of two approaches. First, one acallosal brain and 12 normal postmortem hemispheres (five left and seven right sides) were dissected under a microscope using Klingler's fiber dissection technique. We focused on the medial subcallosal area superior to the Muratoff bundle, which has been indicated as a principal target area of the SFOF in previous studies. Second, 90 patients underwent awake craniotomy for gliomas with direct electrical stimulations. Functional examinations for visual, ataxic, and cognitive tasks were performed and 453 positive mapping sites were investigated by voxel-based morphometry analysis to establish the functions of the SFOF.

The corticostriatal fibers, or the Muratoff bundle, and thalamic peduncle fibers joined in the area of the caudate nucleus, making thalamic peduncle/ corticostriatal bundles, which ran antero-posteriorly in the anterior subcallosal area and radiated from the caudate superior margin in the posterior subcallosal area. However, no SFOF fiber bundle crossed perpendicular to the thalamic peduncle/ corticostriatal bundles in the posterior subcallosal area. In the acallosal hemispheres, Probst bundles were confirmed and the subcallosal areas did not show a specific organization different from the normal brain. Hence, we could not detect a long and continuous association fascicle connecting the frontal lobe and occipital or parietal lobe in the target areas. Furthermore, in the in vivo functional mappings of awake surgery and voxel-based morphometry analysis, eight positive points on the SFOF were selected from the total 453 positive points, but their functions were not related with visual processing and spatial awareness, as has been reported in previous studies.

In conclusion, in the present study we attempted to investigate the existence of the SFOF using an anatomical and functional approach. According to our results, the SFOF may not exist in the human brain.

Four-dimensional map of direct effective connectivity from posterior visual areas

Lower- and higher-order visual cortices in the posterior brain, ranging from the medial- and lateral-occipital to fusiform regions, are suggested to support visual object recognition, whereas the frontal eye field (FEF) plays a role in saccadic eye movements which optimize visual processing. Previous studies using electrophysiology and functional MRI techniques have reported that tasks requiring visual object recognition elicited cortical activation sequentially in the aforementioned posterior visual regions and FEFs. The present study aims to provide unique evidence of direct effective connectivity outgoing from the posterior visual regions by measuring the early component (10-50 ​ms) of cortico-cortical spectral responses (CCSRs) elicited by weak single-pulse direct cortical electrical stimulation. We studied 22 patients who underwent extraoperative intracranial EEG recording for clinical localization of seizure foci and functionally-important brain regions. We used animations to visualize the spatiotemporal dynamics of gamma band CCSRs elicited by stimulation of three different posterior visual regions. We quantified the strength of CCSR-defined effective connectivity between the lower- and higher-order posterior visual regions as well as from the posterior visual regions to the FEFs. We found that effective connectivity within the posterior visual regions was larger in the feedforward (i.e., lower-to higher-order) direction compared to the opposite direction. Specifically, connectivity from the medial-occipital region was largest to the lateral-occipital region, whereas that from the lateral-occipital region was largest to the fusiform region. Among the posterior visual regions, connectivity to the FEF was largest from the lateral-occipital region and the mean peak latency of CCSR propagation from the lateral-occipital region to FEF was 26 ​ms. Our invasive study of the human brain using a stimulation-based intervention supports the model that the posterior visual regions have direct cortico-cortical connectivity pathways in which neural activity is transferred preferentially from the lower-to higher-order areas. The human brain has direct cortico-cortical connectivity allowing a rapid transfer of neural activity from the lateral-occipital region to the FEF.

Focal epilepsy caused by single cerebral cavernous malformation (CCM) is associated with regional and global resting state functional connectivity (FC) disruption

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To our knowledge, this is the first study to report resting state functional connectivity (FC) abnormalities associated with focal epilepsy caused by a single cerebral cavernous malformation (CCM).

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We show, by comparing to the data acquired from the age and gender matched control group, that this type of focal epilepsy is associated with the disruption of the normal regional and global FC. The disruption includes a decrease in the coactivation between the region surrounding the CCM lesion, i.e., the lesional region, and its homotopic counterpart, a reduction in FC between the lesional region and the rest of the brain, and decreased FC among the default mode network (DMN).

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These changes may be alleviated or reversed after the surgical resection of the CCM and the epileptogenic zone has successfully stopped recurrent seizures.

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Finally, the severity of the FC disruption in the brain tissue adjacent to the CCM may be used to delineate the epileptogenic zone and to aid the surgical resection.

Epilepsy, including the type with focal onset, is increasingly viewed as a disorder of the brain network. Here we employed the functional connectivity (FC) metrics estimated from the resting state functional MRI (rsfMRI) to investigate the changes of brain network associated with focal epilepsy caused by single cerebral cavernous malformation (CCM). Eight CCM subjects and 21 age and gender matched controls were enrolled in the study. Seven of 8 CCM subjects underwent surgical resection of the CCM and became seizure free and 4 of the surgical subjects underwent a repeat rsfMRI study. We showed that there was both regional and global disruption of the FC values among the CCM subjects including decreased in homotopic FC (HFC) and global FC (GFC) in the regions of interest (ROIs) where the CCMs were located. There was also the disruption of the default mode network (DMN) especially the FC between the middle prefrontal cortex (MPFC) and the right lateral parietal cortex (LPR) among these individuals. We observed the trend of alleviation of these disruptions after the individual has become seizure free from the surgical resection of the CCM. Using a voxel-based approach, we found the disruption of the HFC and GFC in the brain tissue immediately adjacent to the CCM and the severity of the disruption appeared inversely proportional to the distance of the brain tissue to the lesion. Our findings confirm the disruption of normal brain networks from focal epilepsy, a process that may be reversible with successful surgical treatments rendering patients seizure free. Some voxel-based metrics may help identify the epileptogenic zone and guide the surgical resection.

Trigeminal nerve and white matter brain abnormalities in chronic orofacial pain disorders

Medial temporal lobe activity is investigated in meta-analyses of experimental and chronic pain. Abnormal hippocampal connectivity is found in patients with chronic low back pain.

The orofacial region is psychologically important, given that it serves fundamental and important biological purposes. Chronic orofacial pain disorders affect the head and neck region. Although some have clear peripheral etiologies, eg, classic trigeminal neuralgia, others do not have a clear etiology (eg, muscular temporomandibular disorders). However, these disorders provide a unique opportunity in terms of elucidating the neural mechanisms of these chronic pain conditions: both the peripheral and central nervous systems can be simultaneously imaged. Diffusion-weighted imaging and diffusion tensor imaging have provided a method to essentially perform in vivo white matter dissections in humans, and to elucidate abnormal structure related to clinical correlates in disorders, such as chronic orofacial pains. Notably, the trigeminal nerve anatomy and architecture can be captured using diffusion imaging. Here, we review the trigeminal somatosensory pathways, diffusion-weighted imaging methods, and how these have contributed to our understanding of the neural mechanisms of chronic pain disorders affecting the trigeminal system. We also discuss novel findings indicating the potential for trigeminal nerve diffusion imaging to develop diagnostic and precision medicine biomarkers for trigeminal neuralgia. In sum, diffusion imaging serves both an important basic science purpose in identifying pain mechanisms, but is also a clinically powerful tool that can be used to improve treatment outcomes.

Discovering hierarchical common brain networks via multimodal deep belief network

Studying a common architecture reflecting both brain's structural and functional organizations across individuals and populations in a hierarchical way has been of significant interest in the brain mapping field. Recently, deep learning models exhibited ability in extracting meaningful hierarchical structures from brain imaging data, e.g., fMRI and DTI. However, deep learning models have been rarely used to explore the relation between brain structure and function yet. In this paper, we proposed a novel multimodal deep believe network (DBN) model to discover and quantitatively represent the hierarchical organizations of common and consistent brain networks from both fMRI and DTI data. A prominent characteristic of DBN is that it is capable of extracting meaningful features from complex neuroimaging data with a hierarchical manner. With our proposed DBN model, three hierarchical layers with hundreds of common and consistent brain networks across individual brains are successfully constructed through learning a large dimension of representative features from fMRI/DTI data.

Diffusion MRI visualization

Modern diffusion magnetic resonance imaging (dMRI) acquires intricate volume datasets and biological meaning can only be found in the relationship between its different measurements. Suitable strategies for visualizing these complicated data have been key to interpretation by physicians and neuroscientists, for drawing conclusions on brain connectivity and for quality control. This article provides an overview of visualization solutions that have been proposed to date, ranging from basic grayscale and color encodings to glyph representations and renderings of fiber tractography. A particular focus is on ongoing and possible future developments in dMRI visualization, including comparative, uncertainty, interactive and dense visualizations.

Anatomically constrained tractography facilitates biologically plausible fiber reconstruction of the optic radiation in multiple sclerosis

Diffusion-weighted magnetic resonance imaging (dMRI) enables the microstructural characterization and reconstruction of white matter pathways in vivo non-invasively. However, dMRI only provides information on the orientation of potential fibers but not on their anatomical plausibility. To that end, recent methodological advances facilitate the effective use of anatomical priors in the process of fiber reconstruction, thus improving the accuracy of the results. Here, we investigated the potential of anatomically constrained tracking (ACT), a modular addition to the tractography software package MRtrix3, to accurately reconstruct the optic radiation, a commonly affected pathway in multiple sclerosis (MS). Diffusion MRI data were acquired from 28 MS patients and 22 age- and sex-matched healthy controls. For each participant, the optic radiation was segmented based on the fiber reconstruction obtained using ACT. When implementing ACT in MS, it proved essential to incorporate lesion maps to avoid incorrect reconstructions due to tissue-type misclassifications in lesional areas. The ACT-based results were compared with those obtained using two commonly used probabilistic fiber tracking procedures, based on FSL (FMRIB Software Library) and MRtrix3 without ACT. All three procedures enabled a reliable localization of the optic radiation in both MS patients and controls. However, for FSL and MRtrix3 without ACT it was necessary to place an additional waypoint halfway between the lateral geniculate nucleus and the primary visual cortex to filter out anatomically implausible tracks. In the case of ACT, the results with and without an additional waypoint were virtually identical, presumably because the employed anatomical constraints already prevented the occurrence of the most implausible tracks. Irrespective of the employed tractography procedure, increased diffusivity and decreased anisotropy were found in the optic radiation of the MS patients compared to the controls.

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Anatomical constraints improve tractography of the optic radiation in MS.

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In MS, lesion mapping is essential to implement sensible anatomical constraints.

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Patients showed increased diffusivity and decreased anisotropy in the OR.

Test-Retest Reliability of Diffusion Measures Extracted Along White Matter Language Fiber Bundles Using HARDI-Based Tractography

High angular resolution diffusion imaging (HARDI)-based tractography has been increasingly used in longitudinal studies on white matter macro- and micro-structural changes in the language network during language acquisition and in language impairments. However, test-retest reliability measurements are essential to ascertain that the longitudinal variations observed are not related to data processing. The aims of this study were to determine the reproducibility of the reconstruction of major white matter fiber bundles of the language network using anatomically constrained probabilistic tractography with constrained spherical deconvolution based on HARDI data, as well as to assess the test-retest reliability of diffusion measures extracted along them. Eighteen right-handed participants were scanned twice, one week apart. The arcuate, inferior longitudinal, inferior fronto-occipital, and uncinate fasciculi were reconstructed in the left and right hemispheres and the following diffusion measures were extracted along each tract: fractional anisotropy, mean, axial, and radial diffusivity, number of fiber orientations, mean length of streamlines, and volume. All fiber bundles showed good morphological overlap between the two scanning timepoints and the test-retest reliability of all diffusion measures in most fiber bundles was good to excellent. We thus propose a fairly simple, but robust, HARDI-based tractography pipeline reliable for the longitudinal study of white matter language fiber bundles, which increases its potential applicability to research on the neurobiological mechanisms supporting language.

Tracking and validation techniques for topographically organized tractography

Topographic regularity of axonal connections is commonly understood as the preservation of spatial relationships between nearby neurons and is a fundamental structural property of the brain. In particular the retinotopic mapping of the visual pathway can even be quantitatively computed. Inspired from this previously untapped anatomical knowledge, we propose a novel tractography method that preserves both topographic and geometric regularity. We make use of parameterized curves with Frenet-Serret frame and introduce a highly flexible mechanism for controlling geometric regularity. At the same time, we incorporate a novel local data support term in order to account for topographic organization. Unifying geometry with topographic regularity, we develop a Bayesian framework for generating highly organized streamlines that accurately follow neuroanatomy. We additionally propose two novel validation techniques to quantify topographic regularity. In our experiments, we studied the results of our approach with respect to connectivity, reproducibility and topographic regularity aspects. We present both qualitative and quantitative comparisons of our technique against three algorithms from MRtrix3. We show that our method successfully generates highly organized fiber tracks while capturing bundle anatomy that are geometrically challenging for other approaches.

Brain morphological alteration and cognitive dysfunction in multiple system atrophy

Multiple system atrophy (MSA) is a progressive neurodegenerative disease in adults, manifesting various clinical symptoms including autonomic nerve dysfunction, Parkinson's syndrome, cerebellar ataxia, and pyramidal sign. The clinical diagnosis and classification of MSA are mainly dependent on motion and non-motion symptoms, such as autonomic nerve dysfunction. In addition, an increasing amount of clinical and pathological evidence has shown that about half of the MSA patients exhibit distinct types and levels of cognitive dysfunction. However, cognitive dysfunction has not been included in the current diagnosis criteria of MSA. In most cases, it was even used as an exclusion criterion of MSA. Based on the neuroimaging, neuropathology and neuropsychology, this review summarized the morphological changes of the brain in the patients with MSA, and discussed possible brain regions that could be associated with cognitive impairment. The article may provide a theoretical basis for incorporating cognitive dysfunction into the criteria of MSA diagnosis.

Structural connectome differences in HIV infection: brain network segregation associated with nadir CD4 cell count

This study investigated structural brain organization using diffusion tensor imaging (DTI) in 35 HIV-positive and 35 HIV-negative individuals. We used global and nodal graph theory metrics to investigate whether HIV was associated with differences in brain network organization based on fractional anisotropy (FA) and mean diffusivity (MD). Participants also completed a comprehensive neuropsychological testing battery. For global network metrics, HIV-positive individuals displayed a lower FA clustering coefficient relative to HIV-negative individuals. For nodal network metrics, HIV-positive individuals had less MD nodal degree in the left thalamus. Within HIV-positive individuals, the FA global clustering coefficient was positively correlated with nadir CD4 cell count. Across the sample, cognitive performance was negatively correlated with characteristic path length and positively correlated with global efficiency for FA. These results suggest that, despite management with combination antiretroviral therapy, HIV infection is associated with altered structural brain network segregation and thalamic centrality and that low nadir CD4 cell count may be a risk factor. These graph theory metrics may serve as neural biomarkers to identify individuals at risk for HIV-related neurological complications.

Regional Quantification of Brain Tissue Strain Using Displacement-Encoding With Stimulated Echoes Magnetic Resonance Imaging

Intrinsic cardiac-induced deformation of brain tissue is thought to be important in the pathophysiology of various neurological disorders. In this study, we evaluated the feasibility of utilizing displacement encoding with stimulated echoes (DENSE) magnetic resonance imaging (MRI) to quantify two-dimensional (2D) neural tissue strain using cardiac-driven brain pulsations. We examined eight adult healthy volunteers with an electrocardiogram-gated spiral DENSE sequence performed at the midsagittal plane on a 3 Tesla MRI scanner. Displacement, pixel-wise trajectories, and principal strains were determined in seven regions of interest (ROI): the brain stem, cerebellum, corpus callosum, and four cerebral lobes. Quantification of small neural tissue motion and strain along with their spatial and temporal variations in different brain regions was found to be feasible using DENSE. The medial and inferior brain structures (brain stem, cerebellum, and corpus callosum) had significantly larger motion and strain compared to structures located more peripherally. The brain stem had the largest peak mean displacement (PMD) (187 ± 50 μm, mean ± SD). The largest mean principal strains in compression and extension were observed in the brain stem (0.38 ± 0.08%) and the corpus callosum (0.37 ± 0.08%), respectively. Measured values in percent strain were altered by as much as 0.1 between repeated scans. This study showed that DENSE can quantify regional variations in brain tissue motion and strain and has the potential to be utilized as a tool to evaluate the changes in brain tissue dynamics resulting from alterations in biomechanical stresses and tissue properties.

High-resolution 3D diffusion tensor MRI of anesthetized rhesus macaque brain at 3T

Diffusion Magnetic Resonance Imaging (dMRI) has been widely used to investigate human brain microstructure and connectivity and its abnormalities in a variety of brain deficits, whether acute, neurodevelopmental or neurodegenerative. However, the biological interpretation and validation of dMRI data modelling is still a crucial challenge in the field. In this respect, achieving high spatial resolution in-vivo dMRI in the non-human primate to compare these observations both with human dMRI on the one hand and 'ground truth' microstructural and histological data on the other hand is of outmost importance. Here, we developed a dMRI pulse sequence based on 3D-multishot Echo Planar Imaging (3D-msEPI) on a 3T human clinical scanner. We demonstrate the feasibility of cerebral dMRI at an isotropic resolution of 0.5 mm in 4 anesthetized macaque monkeys. The added value of the high-resolution dMRI is illustrated by focusing on two aspects. First, we show an enhanced descriptive power of the fine substructure of the hippocampus. Second, we show a more physiological description of the interface between cortex grey matter, superficial and deep white matter. Overall, the high spatial resolution dMRI acquisition method proposed in this study is a significant achievement with respect to the state of the art of dMRI on anesthetized monkeys. This study highlights also the potential of very high-resolution dMRI to precisely capture the microstructure of thin cerebral structures such as the hippocampus and superficial white matter.

Thresholding functional connectomes by means of mixture modeling

Functional connectivity has been shown to be a very promising tool for studying the large-scale functional architecture of the human brain. In network research in fMRI, functional connectivity is considered as a set of pair-wise interactions between the nodes of the network. These interactions are typically operationalized through the full or partial correlation between all pairs of regional time series. Estimating the structure of the latent underlying functional connectome from the set of pair-wise partial correlations remains an open research problem though. Typically, this thresholding problem is approached by proportional thresholding, or by means of parametric or non-parametric permutation testing across a cohort of subjects at each possible connection. As an alternative, we propose a data-driven thresholding approach for network matrices on the basis of mixture modeling. This approach allows for creating subject-specific sparse connectomes by modeling the full set of partial correlations as a mixture of low correlation values associated with weak or unreliable edges in the connectome and a sparse set of reliable connections. Consequently, we propose to use alternative thresholding strategy based on the model fit using pseudo-False Discovery Rates derived on the basis of the empirical null estimated as part of the mixture distribution. We evaluate the method on synthetic benchmark fMRI datasets where the underlying network structure is known, and demonstrate that it gives improved performance with respect to the alternative methods for thresholding connectomes, given the canonical thresholding levels. We also demonstrate that mixture modeling gives highly reproducible results when applied to the functional connectomes of the visual system derived from the n-back Working Memory task in the Human Connectome Project. The sparse connectomes obtained from mixture modeling are further discussed in the light of the previous knowledge of the functional architecture of the visual system in humans. We also demonstrate that with use of our method, we are able to extract similar information on the group level as can be achieved with permutation testing even though these two methods are not equivalent. We demonstrate that with both of these methods, we obtain functional decoupling between the two hemispheres in the higher order areas of the visual cortex during visual stimulation as compared to the resting state, which is in line with previous studies suggesting lateralization in the visual processing. However, as opposed to permutation testing, our approach does not require inference at the cohort level and can be used for creating sparse connectomes at the level of a single subject.

Neighborhood resolved fiber orientation distributions (NRFOD) in automatic labeling of white matter fiber pathways

Accurate digital representation of major white matter bundles in the brain is an important goal in neuroscience image computing since the representations can be used for surgical planning, intra-patient longitudinal analysis and inter-subject population connectivity studies. Reconstructing desired fiber bundles generally involves manual selection of regions of interest by an expert, which is subject to user bias and fatigue, hence an automation is desirable. To that end, we first present a novel anatomical representation based on Neighborhood Resolved Fiber Orientation Distributions (NRFOD) along the fibers. The resolved fiber orientations are obtained by generalized q-sampling imaging (GQI) and a subsequent diffusion decomposition method. A fiber-to-fiber distance measure between the proposed fiber representations is then used in a density-based clustering framework to select the clusters corresponding to the major pathways of interest. In addition, neuroanatomical priors are utilized to constrain the set of candidate fibers before density-based clustering. The proposed fiber clustering approach is exemplified on automation of the reconstruction of the major fiber pathways in the brainstem: corticospinal tract (CST); medial lemniscus (ML); middle cerebellar peduncle (MCP); inferior cerebellar peduncle (ICP); superior cerebellar peduncle (SCP). Experimental results on Human Connectome Project (HCP)'s publicly available "WU-Minn 500 Subjects + MEG2 dataset" and expert evaluations demonstrate the potential of the proposed fiber clustering method in brainstem white matter structure analysis.

Quantifying indices of short- and long-range white matter connectivity at each cortical vertex

Several neurodevelopmental diseases are characterized by impairments in cortical morphology along with altered white matter connectivity. However, the relationship between these two measures is not yet clear. In this study, we propose a novel methodology to compute and display metrics of white matter connectivity at each cortical point. After co-registering the extremities of the tractography streamlines with the cortical surface, we computed two measures of connectivity at each cortical vertex: the mean tracts’ length, and the proportion of short- and long-range connections. The proposed measures were tested in a clinical sample of 62 patients with 22q11.2 deletion syndrome (22q11DS) and 57 typically developing individuals. Using these novel measures, we achieved a fine-grained visualization of the white matter connectivity patterns at each vertex of the cortical surface. We observed an intriguing pattern of both increased and decreased short- and long-range connectivity in 22q11DS, that provides novel information about the nature and topology of white matter alterations in the syndrome. We argue that the method presented in this study opens avenues for additional analyses of the relationship between cortical properties and patterns of underlying structural connectivity, which will help clarifying the intrinsic mechanisms that lead to altered brain structure in neurodevelopmental disorders.

Bayesian Tractography Using Geometric Shape Priors

The problem of estimating neuronal fiber tracts connecting different brain regions is important for various types of brain studies, including understanding brain functionality and diagnosing cognitive impairments. The popular techniques for tractography are mostly sequential—tracts are grown sequentially following principal directions of local water diffusion profiles. Despite several advancements on this basic idea, the solutions easily get stuck in local solutions, and can't incorporate global shape information. We present a global approach where fiber tracts between regions of interest are initialized and updated via deformations based on gradients of a posterior energy. This energy has contributions from diffusion data, global shape models, and roughness penalty. The resulting tracts are relatively immune to issues such as tensor noise and fiber crossings, and achieve more interpretable tractography results. We demonstrate this framework using both simulated and real dMRI and HARDI data.

Altered structural network architecture is predictive of the presence of psychotic symptoms in patients with 22q11.2 deletion syndrome

22q11.2 deletion syndrome (22q11DS) represents a homogeneous model of schizophrenia particularly suitable for the search of neural biomarkers of psychosis. Impairments in structural connectivity related to the presence of psychotic symptoms have been reported in patients with 22q11DS. However, the relationships between connectivity changes in patients with different symptomatic profiles are still largely unknown and warrant further investigations.

In this study, we used structural connectivity to discriminate patients with 22q11DS with (N = 31) and without (N = 31) attenuated positive psychotic symptoms. Different structural connectivity measures were used, including the number of streamlines connecting pairs of brain regions, graph theoretical measures, and diffusion measures. We used univariate group comparisons as well as predictive multivariate approaches.

The univariate comparison of connectivity measures between patients with or without attenuated positive psychotic symptoms did not give significant results. However, the multivariate prediction revealed that altered structural network architecture discriminates patient subtypes (accuracy = 67.7%). Among the regions contributing to the classification we found the anterior cingulate cortex, which is known to be associated to the presence of psychotic symptoms in patients with 22q11DS. Furthermore, a significant discrimination (accuracy = 64%) was obtained with fractional anisotropy and radial diffusivity in the left inferior longitudinal fasciculus and the right cingulate gyrus.

Our results point to alterations in structural network architecture and white matter microstructure in patients with 22q11DS with attenuated positive symptoms, mainly involving connections of the limbic system. These alterations may therefore represent a potential biomarker for an increased risk of psychosis that should be further tested in longitudinal studies.

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Altered network architecture discriminates psychotic patients with 22q11DS;

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Altered diffusivity measures are evident in psychotic patients with 22q11DS;

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White matter alterations associated to psychosis are located in limbic regions.

Semi-local tractography strategies using neighborhood information

Fiber tractography based on Diffusion MRI measurements is a valuable tool for the detection and visual representation of neural pathways in vivo. We present a novel fiber orientation distribution function (ODF) based streamline tractography approach which incorporates information of neighboring regions derived from a Bayesian model. In each iteration step, the proposed algorithm defines a set of candidate fiber fragments continuing the already tracked path and assigns an a-posteriori probability. We compute the posterior as the normalized product of a likelihood function based on the given ODF-field and a prior distribution representing anatomical plausibility of a candidate fiber fragment with respect to tract curvature derived from the previously tracked fiber path by an extrapolation strategy. We derive both a deterministic tractography algorithm obtaining in each iteration a tracking direction by maximum a-posteriori estimation, as well as a probabilistic version drawing a direction from the marginalized posterior distribution. Compared to fiber tracking methods that rely only on the local ODF, the proposed algorithm proves more robust in the presence of noise and partial volume effects. We demonstrate the effectiveness of both our deterministic and probabilistic method on simulated, phantom, and in vivo data.

Potential diagnostic role of diffusion tensor imaging in early-stage osteonecrosis of the femoral head

The present study aimed to explore the potential diagnostic role of diffusion tensor magnetic resonance imaging (DTI) in the early stage of modified corticosteroid-induced osteonecrosis of the femoral head (ONFH). A total of 20 beagles were randomly classified (1:1) into either an experimental group (LM), which were intramuscularly injected with lipopolysaccharide (LPS) and methylprednisolone (MPS) on three consecutive days, or control (CON) group, which were injected with saline. Magnetic resonance imaging (MRI) and DTI were performed at pre-induction and 8 and 12 weeks post-induction. Apparent diffusion coefficient (ADC) values in the range of interest in the femoral head were quantified using DTI. Proximal femora were examined for ONFH at 8 and 12 weeks. The results demonstrated that ONFH developed in four beagles at 8 weeks and in six beagles at 12 weeks, whereas no ONFH was detected in the CON group. No abnormalities were detected by MRI and DTI, and no mortality occurred. In beagles with ONFH in the LM group, the ADC values were 4.7±0.2×10^-4 and 4.8±0.3×10^-4 mm²/sec at 8 and 12 weeks, respectively, which were significantly increased compared with the CON group (2.5±0.3×10^-4 and 2.4±0.3×10^-4 mm², respectively) and the LM group without ONFH (2.6±0.4×10^-4 and 2.4±0.3×10^-4 mm², respectively) (P<0.05). The results of the present study indicated that intramuscular injection of LPS and MPS may lead to early-stage ONFH in beagles. As such, the detection of locally elevated ADC values in the femoral head may aid in the early diagnosis of ONFH.

Detection of functional brain network reconfiguration during task-driven cognitive states

Network science offers computational tools to elucidate the complex patterns of interactions evident in neuroimaging data. Recently, these tools have been used to detect dynamic changes in network connectivity that may occur at short time scales. The dynamics of fMRI connectivity, and how they differ across time scales, are far from understood. A simple way to interrogate dynamics at different time scales is to alter the size of the time window used to extract sequential (or rolling) measures of functional connectivity. Here, in n=82 participants performing three distinct cognitive visual tasks in recognition memory and strategic attention, we subdivided regional BOLD time series into variable sized time windows and determined the impact of time window size on observed dynamics. Specifically, we applied a multilayer community detection algorithm to identify temporal communities and we calculated network flexibility to quantify changes in these communities over time. Within our frequency band of interest, large and small windows were associated with a narrow range of network flexibility values across the brain, while medium time windows were associated with a broad range of network flexibility values. Using medium time windows of size 75-100s, we uncovered brain regions with low flexibility (considered core regions, and observed in visual and attention areas) and brain regions with high flexibility (considered periphery regions, and observed in subcortical and temporal lobe regions) via comparison to appropriate dynamic network null models. Generally, this work demonstrates the impact of time window length on observed network dynamics during task performance, offering pragmatic considerations in the choice of time window in dynamic network analysis. More broadly, this work reveals organizational principles of brain functional connectivity that are not accessible with static network approaches.

Corticobulbar Tract Involvement in Neuropsychiatric Systemic Lupus Erythematosus: A Case Report

A 36-year-old woman, diagnosed with systemic lupus erythematosus (SLE), showed bulbar symptoms including impaired memory, slurred speech and swallowing difficulty 7 days before admission. Magnetic resonance imaging (MRI) showed symmetric confluent hyperintensities in the bilateral cerebral white matter on T2 weighted imaging (T2-WI), extended into the genu of the internal capsule and the crus cerebri of the midbrain. MR spectroscopy showed increased choline and decreased N-acetyl aspartate (NAA) peak and positron emission computed tomography (PET CT) showed decreased fluorodeoxyglucose (FDG) uptake on the lateral portion of the frontal lobe, suggesting demyelination of the white matter. The value of apparent diffusion coefficient, fractional anisotropy, tensor linear, tensor planar and relative anisotropy of the corticobulbar tract (CBT) were lower than those of the corticospinal tract. This is the first case report of CBT involvement in a patient with neuropsychiatric SLE (NPSLE) as far as we know. The findings of T2-WI and diffusion tensor imaging (DTI) showed precise anatomical location of neuronal damage of CBT. In addition, magnetic resonance spectroscopy (MRS), PET-CT and parameters of DTI supported the explanations of the inflammatory process and metabolic change of the white matter caused by NPSLE.

Long-range dysconnectivity in frontal and midline structures is associated to psychosis in 22q11.2 deletion syndrome

Patients affected by 22q11.2 deletion syndrome (22q11DS) present a characteristic cognitive and psychiatric profile and have a genetic predisposition to develop schizophrenia. Although brain morphological alterations have been shown in the syndrome, they do not entirely account for the complex clinical picture of the patients with 22q11DS and for their high risk of psychotic symptoms. Since Friston proposed the "disconnection hypothesis" in 1998, schizophrenia is commonly considered as a disorder of brain connectivity. In this study, we review existing evidence pointing to altered brain structural and functional connectivity in 22q11DS, with a specific focus on the role of dysconnectivity in the emergence of psychotic symptoms. We show that widespread alterations of structural and functional connectivity have been described in association with 22q11DS. Moreover, alterations involving long-range association tracts as well as midline structures, such as the corpus callosum and the cingulate gyrus, have been associated with psychotic symptoms in this population. These results suggest common mechanisms for schizophrenia in syndromic and non-syndromic populations. Future directions for investigations are also discussed.

Using Tractography to Distinguish SWEDD from Parkinson's Disease Patients Based on Connectivity

Background. It is critical to distinguish between Parkinson's disease (PD) and scans without evidence of dopaminergic deficit (SWEDD), because the two groups are different and require different therapeutic approaches. Objective. The aim of this study was to distinguish SWEDD patients from PD patients using connectivity information derived from diffusion tensor imaging tractography. Methods. Diffusion magnetic resonance images of SWEDD (n = 37) and PD (n = 40) were obtained from a research database. Tractography, the process of obtaining neural fiber information, was performed using custom software. Group-wise differences between PD and SWEDD patients were quantified using the number of connected fibers between two regions, and correlation analyses were performed based on clinical scores. A support vector machine classifier (SVM) was applied to distinguish PD and SWEDD based on group-wise differences. Results. Four connections showed significant group-wise differences and correlated with the Unified Parkinson's Disease Rating Scale sponsored by the Movement Disorder Society. The SVM classifier attained 77.92% accuracy in distinguishing between SWEDD and PD using these identified connections. Conclusions. The connections and regions identified represent candidates for future research investigations.

Volumetric and fiber-tracing MRI methods for gray and white matter

Magnetic resonance imaging (MRI) is capable of generating high-resolution brain images with fine anatomic detail and unique tissue contrasts that reveal structures that are not visible to the eye. Sharply defined gray- and white-matter interfaces allow for quantitative anatomic analysis that can be accurately performed with largely automated segmentation methods. In an analogous fashion, diffusion MRI in the brain provides structural information based on contrasts derived from the diffusivity of water in brain tissue, which can highlight the orientation of neuronal axons. Also using largely automated methods, diffusion MRI can be used to generate models of white-matter tracts throughout the brain, a method known as tractography, as well as characterize the microstructural integrity of neuronal axons. Tractographic analysis has helped to define connectivity in the brain that powerfully informs understanding of brain function, and, together with other diffusion metrics, is useful in evaluation of the normal and diseased brain. The quantitative methods of brain segmentation, tractography, and diffusion MRI extend MRI into a realm beyond visual inspection and provide otherwise unachievable sensitivity and specificity in the analysis of brain structure and function.

Intermediate phenotypes and biomarkers of treatment outcome in major depressive disorder

Major depressive disorder (MDD) is a pleomorphic illness originating from gene x environment interactions. Patients with differing symptom phenotypes receive the same diagnosis and similar treatment recommendations without regard to genomics, brain structure or function, or other physiologic or psychosocial factors. Using this present approach, only one third of patients enter remission with the first medication prescribed, and patients may take longer than 1 year to enter remission with repeated trials. Research to improve treatment effectiveness recently has focused on identification of intermediate phenotypes (IPs) that could parse the heterogeneous population of patients with MDD into subgroups with more homogeneous responses to treatment. Such IPs could be used to develop biomarkers that could be applied clinically to match patients with the treatment that would be most likely to lead to remission. Putative biomarkers include genetic polymorphisms, RNA and protein expression (transcriptome and proteome), neurotransmitter levels (metabolome), additional measures of signaling cascades, oscillatory synchrony, neuronal circuits and neural pathways (connectome), along with other possible physiologic measures. All of these measures represent components of a continuum that extends from proximity to the genome to proximity to the clinical phenotype of depression, and there are many levels along this continuum at which useful IPs may be defined. Because of the highly integrative nature of brain systems and the complex neurobiology of depression, the most useful biomarkers are likely to be those with intermediate proximity both to the genome and the clinical phenotype of MDD. Translation of findings across the spectrum from genotype to phenotype promises to better characterize the complex disruptions in signaling and neuroplasticity that accompany MDD, and ultimately to lead to greater understanding of the causes of depressive illness.

Local inhibitory plasticity tunes macroscopic brain dynamics and allows the emergence of functional brain networks

Rich, spontaneous brain activity has been observed across a range of different temporal and spatial scales. These dynamics are thought to be important for efficient neural functioning. A range of experimental evidence suggests that these neural dynamics are maintained across a variety of different cognitive states, in response to alterations of the environment and to changes in brain configuration (e.g., across individuals, development and in many neurological disorders). This suggests that the brain has evolved mechanisms to maintain rich dynamics across a broad range of situations. Several mechanisms based around homeostatic plasticity have been proposed to explain how these dynamics emerge from networks of neurons at the microscopic scale. Here we explore how a homeostatic mechanism may operate at the macroscopic scale: in particular, focusing on how it interacts with the underlying structural network topology and how it gives rise to well-described functional connectivity networks. We use a simple mean-field model of the brain, constrained by empirical white matter structural connectivity where each region of the brain is simulated using a pool of excitatory and inhibitory neurons. We show, as with the microscopic work, that homeostatic plasticity regulates network activity and allows for the emergence of rich, spontaneous dynamics across a range of brain configurations, which otherwise show a very limited range of dynamic regimes. In addition, the simulated functional connectivity of the homeostatic model better resembles empirical functional connectivity network. To accomplish this, we show how the inhibitory weights adapt over time to capture important graph theoretic properties of the underlying structural network. Therefore, this work presents suggests how inhibitory homeostatic mechanisms facilitate stable macroscopic dynamics to emerge in the brain, aiding the formation of functional connectivity networks.

Toward a standardized structural-functional group connectome in MNI space

The analysis of the structural architecture of the human brain in terms of connectivity between its subregions has provided profound insights into its underlying functional organization and has coined the concept of the "connectome", a structural description of the elements forming the human brain and the connections among them. Here, as a proof of concept, we introduce a novel group connectome in standard space based on a large sample of 169 subjects from the Enhanced Nathan Kline Institute-Rockland Sample (eNKI-RS). Whole brain structural connectomes of each subject were estimated with a global tracking approach, and the resulting fiber tracts were warped into standard stereotactic (MNI) space using DARTEL. Employing this group connectome, the results of published tracking studies (i.e., the JHU white matter and Oxford thalamic connectivity atlas) could be largely reproduced directly within MNI space. In a second analysis, a study that examined structural connectivity between regions of a functional network, namely the default mode network, was reproduced. Voxel-wise structural centrality was then calculated and compared to others' findings. Furthermore, including additional resting-state fMRI data from the same subjects, structural and functional connectivity matrices between approximately forty thousand nodes of the brain were calculated. This was done to estimate structure-function agreement indices of voxel-wise whole brain connectivity. Taken together, the combination of a novel whole brain fiber tracking approach and an advanced normalization method led to a group connectome that allowed (at least heuristically) performing fiber tracking directly within MNI space. Such an approach may be used for various purposes like the analysis of structural connectivity and modeling experiments that aim at studying the structure-function relationship of the human connectome. Moreover, it may even represent a first step toward a standard DTI template of the human brain in stereotactic space. The standardized group connectome might thus be a promising new resource to better understand and further analyze the anatomical architecture of the human brain on a population level.

Thalamic Reorganization in Chronic Patients With Intracerebral Hemorrhage: A Retrospective Cross-Sectional Study

The aim of this study was to investigate changes of synaptic area of the spinothalamic tract and its thalamocortical pathway (STT) in the thalamus in chronic patients with putaminal hemorrhage.Twenty four patients with a lesion in the ventral posterior lateral nucleus (VPL) of the thalamus following putaminal hemorrhage were recruited for this study. The subscale for tactile sensation of the Nottingham Sensory Assessment (NSA) was used for the determination of somatosensory function. Diffusion tensor tractography of the STT was reconstructed using the Functional Magnetic Resonance Imaging of the Brain Software Library. We classified patients according to 2 groups: the VPL group, patients whose STTs were synapsed in the VPL; and the non-VPL group, patients whose STTs were synapsed in other thalamic areas, except for the VPL.Thirteen patients belonged to the VPL group, and 8 patients belonged to the non-VPL group. Three patients were excluded from grouping due to interrupted integrity of the STTs. The tactile sensation score of the NSA in the non-VPL group (10.50 ± 0.93) was significantly decreased compared with that of the VPL group (19.45 ± 1.33) (P < 0.05).We found that 2 types of patient had recovered via the VPL area or other areas of the STT. It appears that patients who showed shifting of the thalamic synaptic area of the STT might have recovered by the process of thalamic reorganization following thalamic injury. In addition, thalamic reorganization appears to be related to poorer somatosensory outcome.

Injury of the corticobulbar tract in patients with dysarthria following cerebral infarct: diffusion tensor tractography study

OBJECTIVES

Little is known about injury of the corticobulbar tract (CBT) in stroke patients. We attempted to investigate injury of the CBT in patients with dysarthria following cerebral infarct, using diffusion tensor tractography (DTT).

METHODS

Eight patients with dysarthria following a corona radiata infarct and 12 control subjects were recruited for this study. Diffusion tensor imaging was performed at 14.3 days after onset and reconstruction of the CBT was performed using the probabilistic tractography method. Fractional anisotropy, mean diffusivity, and tract volume of the CBT were measured.

RESULTS

Reconstructed CBTs in the affected hemisphere of the patient group were thinner than those of the unaffected hemisphere of the patient group and the control group. Regarding the DTT parameters of the CBTs, fractional anisotropy and tract volume were significantly lower in the affected hemisphere of the patient group than in the unaffected hemisphere of the patient group and the control group (p < 0.05). However, we did not observe any difference in the mean diffusivity value (p > 0.05).

CONCLUSIONS

We demonstrated injury of the CBT in patients with dysarthria following cerebral infarct in the corona radiata using DTT. This result indicates the importance of CBT evaluation for dysarthria in patients with cerebral infarct. Therefore, we suggest that evaluations of the CBT using DTT would be useful for patients with dysarthria following cerebral infarct.

Robust and efficient linear registration of white-matter fascicles in the space of streamlines

The neuroscientific community today is very much interested in analyzing specific white matter bundles like the arcuate fasciculus, the corticospinal tract, or the recently discovered Aslant tract to study sex differences, lateralization and many other connectivity applications. For this reason, experts spend time manually segmenting these fascicles and bundles using streamlines obtained from diffusion MRI tractography. However, to date, there are very few computational tools available to register these fascicles directly so that they can be analyzed and their differences quantified across populations. In this paper, we introduce a novel, robust and efficient framework to align bundles of streamlines directly in the space of streamlines. We call this framework Streamline-based Linear Registration. We first show that this method can be used successfully to align individual bundles as well as whole brain streamlines. Additionally, if used as a piecewise linear registration across many bundles, we show that our novel method systematically provides higher overlap (Jaccard indices) than state-of-the-art nonlinear image-based registration in the white matter. We also show how our novel method can be used to create bundle-specific atlases in a straightforward manner and we give an example of a probabilistic atlas construction of the optic radiation. In summary, Streamline-based Linear Registration provides a solid registration framework for creating new methods to study the white matter and perform group-level tractometry analysis.