Using GPUs to accelerate computational diffusion MRI: From microstructure estimation to tractography and connectomes

Relation of MRI visible perivascular spaces with global and regional brain structural connectivity measures: The Framingham Heart Study (FHS)

MRI visible perivascular spaces (PVS) are associated with cognitive impairment and dementia, which are also associated with disrupted network connectivity. PVS may relate to dementia risk through disruption in brain connectivity. We studied the relation between PVS grade and global and regional structural connectivity in Framingham Heart Study participants free of stroke and dementia. PVS were rated on axial T2 sequences in the basal ganglia (BG) and centrum semiovale (CSO). We assessed structural global and regional network architecture using global efficiency, local efficiency and modularity. Analysis of covariance was used to relate PVS grades with structural network measures. Models adjusted for age, sex (model 1), and vascular risk factors (model 2). Effect modification on the associations by age, sex, hypertension and APOE-ɛ4 status was assessed. Among 2525 participants (mean age 54 ± 13 years, 53 % female), significant associations were observed between grade III and IV PVS in the BG and CSO with reduced global efficiency. Grade III (β -0.0030; 95 % confidence interval [CI] -0.0041, -0.0019) and IV (β -0.0033, CI -0.0060, -0.0007) PVS in the BG and grade IV (β -0.0015; CI -0.0024, -0.0007) PVS in the CSO were associated with reduced local efficiency. We observed shared and different strength of association by age, hypertension, sex and APOE-ɛ4 in the relationship between high burden PVS in the BG and CSO with structural network measures. Findings suggest that higher grade PVS are associated with disruption of global and regional structural brain networks.

Glymphatic Impairment Associated with Neurocognitive Dysfunction in Moyamoya Disease

Glymphatic system alterations have been proved to be associated with cognitive dysfunction in neurodegenerative diseases. The glymphatic pathway has not been elucidated in moyamoya disease (MMD), which was recognized as a chronic hypoperfusion model for neurodegenerative disease. Here, we aimed to investigate the glymphatic system activity and its relation with neurocognition, and associated hallmarks in MMD. We prospectively recruited 30 MMD patients and 30 matched healthy controls (HC). Participants underwent MRI and neurocognition evaluation. The glymphatic function was assessed by diffusion tensor image analysis along perivascular space (DTI-ALPS) index. Gray matter volume (GMV) and microstructural alterations were calculated. Neurodegenerative-related serum biomarkers were examined. The mediation effect of ALPS index in the associations between variables and neurocognition were further explored. A lower ALPS index was identified in patients with MMD (P < 0.001). The decreased ALPS index was significantly correlated with declined neurocognitive performance. Moreover, the reduced ALPS index was notably linked with lower total GMV% and deep GMV% (P < 0.01). Microstructural changes in the periventricular areas were detected and associated with ALPS index in MMD. Serum neurodegenerative biomarkers (ApoE, Aβ40, Aβ42, and Aβ42/Aβ40) were significantly elevated and related to ALPS index. Additionally, the ALPS index significantly mediated the associations of microstructural alterations and ApoE level with neurocognitive dysfunction. The ALPS index was notably lower MMD in patients, suggesting the utility as a marker of potential glymphatic dysfunction. The index acted as a significant mediator in neurocognitive dysfunction. These findings indicated that glymphatic impairment may interact with MMD-related pathophysiological processes.

Uncertainty mapping and probabilistic tractography using Simulation-based Inference in diffusion MRI: A comparison with classical Bayes

Simulation-Based Inference (SBI) has recently emerged as a powerful framework for Bayesian inference: Neural networks are trained on simulations from a forward model, and learn to rapidly estimate posterior distributions. We here present an SBI framework for parametric spherical deconvolution of diffusion MRI data of the brain. We demonstrate its utility for estimating white matter fibre orientations, mapping uncertainty of voxel-based estimates and performing probabilistic tractography by spatially propagating fibre orientation uncertainty. We conduct an extensive comparison against established Bayesian methods based on Markov-Chain Monte-Carlo (MCMC) and find that: a) in-silico training can lead to calibrated SBI networks with accurate parameter estimates and uncertainty mapping for both single- and multi-shell diffusion MRI, b) SBI allows amortised inference of the posterior distribution of model parameters given unseen observations, which is orders of magnitude faster than MCMC, c) SBI-based tractography yields reconstructions that have a high level of agreement with their MCMC-based counterparts, equal to or higher than scan-rescan reproducibility of estimates. We further demonstrate how SBI design considerations (such as dealing with noise, defining priors and handling model selection) can affect performance, allowing us to identify optimal practices. Taken together, our results show that SBI provides a powerful alternative to classical Bayesian inference approaches for fast and accurate model estimation and uncertainty mapping in MRI.

Mapping the coupling between tract reachability and cortical geometry of the human brain

The study of cortical geometry and connectivity is prevalent in research on the human brain. However, these two aspects of brain structure are usually examined separately, leaving the essential connections between the brain's folding patterns and white matter connectivity unexplored. In this study, we aimed to elucidate fundamental links between cortical geometry and white matter tract connectivity. We developed the concept of tract-geometry coupling (TGC) by optimizing the alignment between tract connectivity to the cortex and multiscale cortical geometry. Specifically, spectral analyses of the cortical surface yielded a set of geometrical eigenmodes, which were then used to explain the locations on the cortical surface reached by specific white matter tracts, referred to as tract reachability. In two independent datasets, we confirmed that tract reachability was well characterized by cortical geometry. We further observed that TGC had high test-retest ability and was specific to each individual. Interestingly, low-frequency TGC was found to be heritable and more informative than the high-frequency components in behavior prediction. Finally, we found that TGC could reproduce task-evoked cortical activation patterns. Collectively, our study provides a new approach to mapping coupling between cortical geometry and connectivity, highlighting how these two aspects jointly shape the connected brain.

Early Development and Co-Evolution of Microstructural and Functional Brain Connectomes: A Multi-Modal MRI Study in Preterm and Full-Term Infants

Functional networks characterized by coherent neural activity across distributed brain regions have been observed to emerge early in neurodevelopment. Synchronized maturation across regions that relate to functional connectivity (FC) could be partially reflected in the developmental changes in underlying microstructure. Nevertheless, covariation of regional microstructural properties, termed "microstructural connectivity" (MC), and its relationship to the emergence of functional specialization during the early neurodevelopmental period remain poorly understood. We investigated the evolution of MC and FC postnatally across a set of cortical and subcortical regions, focusing on 45 preterm infants scanned longitudinally, and compared to 45 matched full-term neonates as part of the developing Human Connectome Project (dHCP) using direct comparisons of grey-matter connectivity strengths as well as network-based analyses. Our findings revealed a global strengthening of both MC and FC with age, with connection-specific variability influenced by the connection maturational stage. Prematurity at term-equivalent age was associated with significant connectivity disruptions, particularly in FC. During the preterm period, direct comparisons of MC and FC strength showed a positive linear relationship, which seemed to weaken with development. On the other hand, overlaps between MC- and FC-derived networks (estimated with Mutual Information) increased with age, suggesting a potential convergence towards a shared underlying network structure that may support the co-evolution of microstructural and functional systems. Our study offers novel insights into the dynamic interplay between microstructural and functional brain development and highlights the potential of MC as a complementary descriptor for characterizing brain network development and alterations due to perinatal insults such as premature birth.

Effects of trace element dysregulation on brain structure and function in spinocerebellar Ataxia type 3

Spinocerebellar ataxia type 3 (SCA3), a neurodegenerative disorder caused by excess CAG repeats in the ATXN3 gene, leads to progressive cerebellar ataxia and other symptoms. The results of previous studies suggest that trace element dysregulation contributes to neurodegenerative disorder onset. Here, we investigated the relationships of trace element dysregulation with CAG repeat length, clinical severity, and brain structural and functional connectivity in 45 patients with SCA3 and 44 healthy controls (HCs). Blood levels of lithium (Li), selenium (Se), and copper (Cu) were significantly lower in patients with SCA3 than in HCs; Li and Se levels were negatively correlated with CAG repeat length, especially in the manifest subgroup. Diffusion tensor imaging combined with resting-state functional magnetic resonance imaging revealed that Li levels were negatively correlated with fractional anisotropy in the white matter (WM) of bilateral frontal and parietal regions; tractography mapping showed disorder structural connectivity of Li-associated region nerve fiber pathways in patients with SCA3. Dynamic causal modeling analyses showed bidirectional causal connectivity from the inferior parietal lobule(IPL) to the cerebellum was significantly correlated with the blood level of Li in patients with SCA3. Time series correlation-based functional connectivity analysis revealed that the intrinsic connectivities of the bilateral dorsal premotor cortex(PMd) and IPL with local cerebellar regions were significantly weaker in patients with SCA3 than in HCs. Our results suggest that trace element dysregulation, especially Li deficiency, induces brain alterations and clinical manifestations in patients with SCA3; Li supplementation may be beneficial for WM or astrocytes in this patient population.

White matter microstructural alterations are associated with cognitive decline in benzodiazepine use disorders: a multi-shell diffusion magnetic resonance imaging study

Background

Benzodiazepine use disorders (BUDs) have become a public health issue that cannot be ignored. We aimed to demonstrate that patients with BUDs might undergo changes in white matter (WM) integrity, which are related to impaired cognitive function.

Methods

We used diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), and mean apparent propagator (MAP) to observe changes in WM structure from 29 patients with sleep disorders with BUD (SDBUD), 33 patients with sleep disorders with non-BUD (SDNBUD), and 25 healthy participants. We also compared the diagnostic performance of the diffusion metrics and models in predicting the status of BUDs and evaluated the relationship between WM changes and cognitive impairment.

Results

BUD was closely associated with WM damage in the corpus callosum (CC) and pontine crossing tract (PCT). There were 14 main diffusion metrics that could be used to predict BUD status (P=0.001-0.023). DTI, DKI, NODDI, and MAP had similar satisfactory performance for predicting BUD status (P=0.001-0.021). Pearson correlation analysis showed a close relationship between the Trail Making Test B (TMT-B) and DTI/NODDI metrics in the splenium of the CC and PCT and between the Montreal Cognitive Assessment (MoCA) and MAP metrics in the splenium of the CC in the SDBUD group (P=0.008-0.040).

Conclusions

Our findings provide evidence for the neurobiological mechanism of benzodiazepine addiction and a novel method for the clinical diagnosis of BUDs.

Topographic Axes of Wiring Space Converge to Genetic Topography in Shaping the Human Cortical Layout

Genetic information is involved in the gradual emergence of cortical areas since the neural tube begins to form, shaping the heterogeneous functions of neural circuits in the human brain. Informed by invasive tract-tracing measurements, the cortex exhibits marked interareal variation in connectivity profiles, revealing the heterogeneity across cortical areas. However, it remains unclear about the organizing principles possibly shared by genetics and cortical wiring to manifest the spatial heterogeneity across the cortex. Instead of considering a complex one-to-one mapping between genetic coding and interareal connectivity, we hypothesized the existence of a more efficient way that the organizing principles are embedded in genetic profiles to underpin the cortical wiring space. Leveraging vertex-wise tractography in diffusion-weighted MRI, we derived the global connectopies (GCs) in both female and male to reliably index the organizing principles of interareal connectivity variation in a low-dimensional space, which captured three dominant topographic patterns along the dorsoventral, rostrocaudal, and mediolateral axes of the cortex. More importantly, we demonstrated that the GCs converge with the gradients of a vertex-by-vertex genetic correlation matrix on the phenotype of cortical morphology and the cortex-wide spatiomolecular gradients. By diving into the genetic profiles, we found that the critical role of genes scaffolding the GCs was related to brain morphogenesis and enriched in radial glial cells before birth and excitatory neurons after birth. Taken together, our findings demonstrated the existence of a genetically determined space that encodes the interareal connectivity variation, which may give new insights into the links between cortical connections and arealization.

Lesion network mapping of REM Sleep Behaviour Disorder

REM Sleep Behaviour Disorder (RBD) is a parasomnia characterised by dream enactment behaviour due to loss of sleep atonia during REM sleep. It is of considerable interest as idiopathic RBD is strongly associated with a high risk of future α-synuclein disorders. Whilst candidate brainstem structures for sleep atonia have been identified in animal studies, the precise mechanisms underpinning RBD in humans remain unclear. Here, we set out to empirically define a candidate anatomical RBD network using lesion network mapping. Our objective was to test the hypothesis that RBD is either due to damage to canonical RBD nodes previously identified in the animal literature, or disruption to the white matter connections between these nodes, or as a consequence of damage to some other brains regions. All published cases of secondary RBD arising due to discrete brain lesions were reviewed and those providing sufficient detail to estimate the original lesion selected. This resulted in lesion masks for 25 unique RBD cases. These were combined to create a lesion probability map, demonstrating the area of maximal overlap. We also obtained MRI lesion masks for 15 pontine strokes that had undergone sleep polysomnography investigations confirming the absence of RBD. We subsequently used these as an exclusion mask and removed any intersecting voxels from the aforementioned region of maximal overlap, creating a single candidate region-of-interest (ROIs) within the pons. This remaining region overlapped directly with the locus coeruleus. As sleep atonia is unlikely to be lateralized, a contralateral ROI was created via a left-right flip, and both were warped to the 100 healthy adult Human Connectome dataset. Probabilistic tractography was run from each ROI to map and characterize the white-matter tracts and connectivity properties. All reported lesions were within the brainstem but there was significant variability in location. Only half of these intersected with at least one of the six a priori RBD anatomical nodes assessed, however 72 % directly intersected with the white matter tracts created from the region of maximum overlap pontine ROIs, and the remainder were within 3.05 mm (±1.51 mm) of these tracts. 92 % of lesions were either at the level of region of maximum overlap or caudal to it. These results suggest that RBD is a brainstem disconnection syndrome, where damage anywhere along the tract connecting the rostral locus coeruleus and medulla may result in failure of sleep atonia, in line with the animal literature. This implies idiopathic disease may emerge through different patterns of damage across this brainstem circuit. This observation may account for the both the paucity of brainstem neuroimaging results reported to date and the observed phenotypic variability seen in idiopathic RBD.

Relationship of left piriform cortex network centrality with temporal lobe epilepsy duration and drug resistance

BACKGROUND

We investigated the relationship of piriform cortex (PC) structural network centrality with drug resistance and epilepsy duration as markers of sustained epileptic activity.

METHODS

PCs were manually delineated on retrospectively collected 3D-T1-MRI images of patients with temporal lobe epilepsy (TLE). Connectomes were computed from diffusion MRI scans, including the PC as network nodes. Betweenness centrality (BC) and node degree were computed and compared across drug-resistant versus drug-sensitive patients. Correlations of centrality metrics with the duration of epilepsy were calculated.

RESULTS

Sixty-two patients (36 females, 43/62 drug-resistant) were included in the main analysis. Greater centrality of the left PC was associated with drug resistance (degree: p = 0.00696, d = 0.85; BC: p = 0.00859, d = 0.59; alpha = 0.0125). Furthermore, left PC centrality was correlated with epilepsy duration (degree: rho = 0.39, p = 0.00181; BC: rho = 0.35, p = 0.0047; alpha = 0.0125). Results were robust to analysis of different parcellation schemes. Exploratory whole-network analysis yielded the largest effects in the left PC. Finer parcellations showed stronger effects for both analyses in the left olfactory cortex rostral to PC. In 28 subjects who had received epilepsy surgery, a trend of smaller centrality in patients with ILAE I outcome was observed in this area.

CONCLUSIONS

We demonstrated an increased centrality of the left PC in patients with drug-resistant TLE, which was also associated with the epilepsy duration. Recurring seizures over long periods may lead to changes of network properties of the PC. Large effects immediately rostral to our delineated PC region suggest a role of olfactory cortex anterior to the limen insulae in epileptogenic networks.

Association of concussion history with psychiatric symptoms, limbic system structure, and kynurenine pathway metabolites in healthy, collegiate-aged athletes

Psychiatric outcomes are commonly observed in individuals with repeated concussions, though their underlying mechanism is unknown. One potential mechanism linking concussion with psychiatric symptoms is inflammation-induced activation of the kynurenine pathway, which is thought to play a role in the pathogenesis of mood disorders. Here, we investigated the association of prior concussion with multiple psychiatric-related outcomes in otherwise healthy male and female collegiate-aged athletes (N = 212) with varying histories of concussion recruited from the community. Specially, we tested the hypotheses that concussion history is associated with worse psychiatric symptoms, limbic system structural abnormalities (hippocampal volume, white matter microstructure assessed using neurite orientation dispersion and density imaging; NODDI), and elevations in kynurenine pathway (KP) metabolites (e.g., Quinolinic acid; QuinA). Given known sex-effects on concussion risk and recovery, psychiatric outcomes, and the kynurenine pathway, the moderating effect of sex was considered for all analyses. More concussions were associated with greater depression, anxiety, and anhedonia symptoms in female athletes (ps ≤ 0.005) and greater depression symptoms in male athletes (p = 0.011). More concussions were associated with smaller bilateral hippocampal tail (ps < 0.010) and left hippocampal body (p < 0.001) volumes across male and female athletes. Prior concussion was also associated with elevations in the orientation dispersion index (ODI) and lower intracellular volume fraction in several white matter tracts including the in uncinate fasciculus, cingulum-gyrus, and forceps major and minor, with evidence of female-specific associations in select regions. Regarding serum KP metabolites, more concussions were associated with elevated QuinA in females and lower tryptophan in males (ps ≤ 0.010). Finally, serum levels of QuinA were associated with elevated ODI (male and female athletes) and worse anxiety symptoms (females only), while higher ODI in female athletes and smaller hippocampal volumes in male athletes were associated with more severe anxiety and depression symptoms (ps ≤ 0.05). These data suggest that cumulative concussion is associated with psychiatric symptoms and limbic system structure in healthy athletes, with increased susceptibility to these effects in female athletes. Moreover, the associations of outcomes with serum KP metabolites highlight the KP as one potential molecular pathway underlying these observations.

In vivo human neurite exchange imaging (NEXI) at 500 mT/m diffusion gradients

Evaluating tissue microstructure and membrane integrity in the living human brain through diffusion-water exchange imaging is challenging due to requirements for a high signal-to-noise ratio and short diffusion times dictated by relatively fast exchange processes. The goal of this work was to demonstrate the feasibility of in vivo imaging of tissue micro-geometries and water exchange within the brain gray matter using the state-of-the-art Connectome 2.0 scanner equipped with an ultra-high-performance gradient system (maximum gradient strength=500 mT/m, maximum slew rate=600 T/m/s). We performed diffusion MRI measurements in 15 healthy volunteers at multiple diffusion times (13-30 ms) and b -values up to 17.5 ms/μm2. The anisotropic Kärger model was applied to estimate the exchange time between intra-neurite and extracellular water in gray matter. The estimated exchange time across the cortical ribbon was around (median±interquartile range) 13±8 ms on Connectome 2.0, substantially faster than that measured using an imaging protocol compatible with Connectome 1.0-alike systems on the same cohort. Our investigation suggested that the NEXI exchange time estimation using a Connectome 1.0 compatible protocol was more prone to residual noise floor biases due to the small time-dependent signal contrasts across diffusion times when the exchange is fast (≤20 ms). Furthermore, spatial variation of exchange time was observed across the cortex, where the motor cortex, somatosensory cortex and visual cortex exhibit longer exchange times compared to other cortical regions. Non-linear fitting for the anisotropic Kärger model was accelerated 100 times using a GPU-based pipeline compared to the conventional CPU-based approach. This study highlighted the importance of the chosen diffusion times and measures to address Rician noise in dMRI data, which can have a substantial impact on the estimated NEXI exchange time and require extra attention when comparing NEXI results between various hardware setups.

White matter connectivity linked to novel word learning in children

Children and adults are excellent word learners. Increasing evidence suggests that the neural mechanisms that allow us to learn words change with age. In a recent fMRI study from our group, several brain regions exhibited age-related differences when accessing newly learned words in a second language (L2; Takashima et al. Dev Cogn Neurosci 37, 2019). Namely, while the Teen group (aged 14-16 years) activated more left frontal and parietal regions, the Young group (aged 8-10 years) activated right frontal and parietal regions. In the current study we analyzed the structural connectivity data from the aforementioned study, examining the white matter connectivity of the regions that showed age-related functional activation differences. Age group differences in streamline density as well as correlations with L2 word learning success and their interaction were examined. The Teen group showed stronger connectivity than the Young group in the right arcuate fasciculus (AF). Furthermore, white matter connectivity and memory for L2 words across the two age groups correlated in the left AF and the right anterior thalamic radiation (ATR) such that higher connectivity in the left AF and lower connectivity in the right ATR was related to better memory for L2 words. Additionally, connectivity in the area of the right AF that exhibited age-related differences predicted word learning success. The finding that across the two age groups, stronger connectivity is related to better memory for words lends further support to the hypothesis that the prolonged maturation of the prefrontal cortex, here in the form of structural connectivity, plays an important role in the development of memory.

A combined DTI-fMRI approach for optimizing the delineation of posteromedial versus anterolateral entorhinal cortex

In the entorhinal cortex (EC), attempts have been made to identify the human homologue regions of the medial (MEC) and lateral (LEC) subregions using either functional magnetic resonance imaging (fMRI) or diffusion tensor imaging (DTI). However, there are still discrepancies between entorhinal subdivisions depending on the choice of connectivity seed regions and the imaging modality used. While DTI can be used to follow the white matter tracts of the brain, fMRI can identify functionally connected brain regions. In this study, we used both DTI and resting-state fMRI in 103 healthy adults to investigate both structural and functional connectivity between the EC and associated cortical brain regions. Differential connectivity with these regions was then used to predict the locations of the human homologues of MEC and LEC. Our results from combining DTI and fMRI support a subdivision into posteromedial (pmEC) and anterolateral (alEC) EC and reveal a confined border between the pmEC and alEC. Furthermore, the EC subregions obtained by either imaging modality showed similar distinct whole-brain connectivity profiles. Optimizing the delineation of the human homologues of MEC and LEC with a combined, cross-validated DTI-fMRI approach allows to define a likely border between the two subdivisions and has implications for both cognitive and translational neuroscience research.

Reorganization of structural connectivity in the brain supports preservation of cognitive ability in healthy aging

The global population is aging rapidly, and a research question of critical importance is why some older adults suffer tremendous cognitive decline while others are mostly spared. Past aging research has shown that older adults with spared cognitive ability have better local short-range information processing while global long-range processing is less efficient. We took this research a step further to investigate whether the underlying structural connections, measured in vivo using diffusion magnetic resonance imaging (dMRI), show a similar shift to support cognitive ability. We analyzed the structural connectivity streamline probability (representing the probability of connection between regions) and nodal efficiency and local efficiency regional graph theory metrics to determine whether age and cognitive ability are related to structural network differences. We found that the relationship between structural connectivity and cognitive ability with age was nuanced, with some differences with age that were associated with poorer cognitive outcomes, but other reorganizations that were associated with spared cognitive ability. These positive changes included strengthened local intrahemispheric connectivity and increased nodal efficiency of the ventral occipital-temporal stream, nucleus accumbens, and hippocampus for older adults, and widespread local efficiency primarily for middle-aged individuals.

Human Claustrum Connections: Robust In Vivo Detection by DWI-Based Tractography in Two Large Samples

Despite substantial neuroscience research in the last decade revealing the claustrum's prominent role in mammalian forebrain organization, as evidenced by its extraordinarily widespread connectivity pattern, claustrum studies in humans are rare. This is particularly true for studies focusing on claustrum connections. Two primary reasons may account for this situation: First, the intricate anatomy of the human claustrum located between the external and extreme capsule hinders straightforward and reliable structural delineation. In addition, the few studies that used diffusion-weighted-imaging (DWI)-based tractography could not clarify whether in vivo tractography consistently and reliably identifies claustrum connections in humans across different subjects, cohorts, imaging methods, and connectivity metrics. To address these issues, we combined a recently developed deep-learning-based claustrum segmentation tool with DWI-based tractography in two large adult cohorts: 81 healthy young adults from the human connectome project and 81 further healthy young participants from the Bavarian longitudinal study. Tracts between the claustrum and 13 cortical and 9 subcortical regions were reconstructed in each subject using probabilistic tractography. Probabilistic group average maps and different connectivity metrics were generated to assess the claustrum's connectivity profile as well as consistency and replicability of tractography. We found, across individuals, cohorts, DWI-protocols, and measures, consistent and replicable cortical and subcortical ipsi- and contralateral claustrum connections. This result demonstrates robust in vivo tractography of claustrum connections in humans, providing a base for further examinations of claustrum connectivity in health and disease.

Imbalanced optimal feedback motor control system in spinocerebellar ataxia type 3

BACKGROUND AND PURPOSE

Human motor planning and control depend highly on optimal feedback control systems, such as the neocortex-cerebellum circuit. Here, diffusion tensor imaging was used to verify the disruption of the neocortex-cerebellum circuit in spinocerebellar ataxia type 3 (SCA3), and the circuit's disruption correlation with SCA3 motor dysfunction was investigated.

METHODS

This study included 45 patients with familial SCA3, aged 17-67 years, and 49 age- and sex-matched healthy controls, aged 21-64 years. Tract-based spatial statistics and probabilistic tractography was conducted using magnetic resonance images of the patients and controls. The correlation between the local probability of probabilistic tractography traced from the cerebellum and clinical symptoms measured using specified symptom scales was also calculated.

RESULTS

The cerebellum-originated probabilistic tractography analysis showed that structural connectivity, mainly in the subcortical cerebellar-thalamo-cortical tract, was significantly reduced and the cortico-ponto-cerebellar tract was significantly stronger in the SCA3 group than in the control group. The enhanced tract was extended to the right lateral parietal region and the right primary motor cortex. The enhanced neocortex-cerebellum connections were highly associated with disease progression, including duration and symptomatic deterioration. Tractography probabilities from the cerebellar to parietal and sensorimotor areas were significantly negatively correlated with motor abilities in patients with SCA3.

CONCLUSION

To our knowledge, this study is the first to reveal that disrupting the neocortex-cerebellum loop can cause SCA3-induced motor dysfunctions. The specific interaction between the cerebellar-thalamo-cortical and cortico-ponto-cerebellar pathways in patients with SCA3 and its relationship with ataxia symptoms provides a new direction for future research.

aXonica: A support package for MRI based Neuroimaging

Magnetic Resonance Imaging (MRI) assists in studying the nervous system. MRI scans undergo significant processing before presenting the final images to medical practitioners. These processes are executed with ease due to excellent software pipelines. However, establishing software workstations is non-trivial and requires researchers in life sciences to be comfortable in downloading, installing, and scripting software that is non-user-friendly and may lack basic GUI. As researchers struggle with these skills, there is a dire need to develop software packages that can automatically install software pipelines speeding up building software workstations and laboratories. Previous solutions include NeuroDebian, BIDS Apps, Flywheel, QMENTA, Boutiques, Brainlife and Neurodesk. Overall, all these solutions complement each other. NeuroDebian covers neuroscience and has a wider scope, providing only 51 tools for MRI. Whereas, BIDS Apps is committed to the BIDS format, covering only 45 software related to MRI. Boutiques is more flexible, facilitating its pipelines to be easily installed as separate containers, validated, published, and executed. Whereas, both Flywheel and Qmenta are propriety, leaving four for users looking for 'free for use' tools, i.e., NeuroDebian, Brainlife, Neurodesk, and BIDS Apps. This paper presents an extensive survey of 317 tools published in MRI-based neuroimaging in the last ten years, along with 'aXonica,' an MRI-based neuroimaging support package that is unbiased towards any formatting standards and provides 130 applications, more than that of NeuroDebian (51), BIDS App (45), Flywheel (70), and Neurodesk (85). Using a technology stack that employs GUI as the front-end and shell scripted back-end, aXonica provides (i) 130 tools that span the entire MRI-based neuroimaging analysis, and allow the user to (ii) select the software of their choice, (iii) automatically resolve individual dependencies and (iv) installs them. Hence, aXonica can serve as an important resource for researchers and teachers working in the field of MRI-based Neuroimaging to (a) develop software workstations, and/or (b) install newer tools in their existing workstations.

Microstructural Properties of the Cerebellar Peduncles in Children With Developmental Language Disorder

Children with developmental language disorder (DLD) struggle to learn their native language for no apparent reason. While research on the neurobiological underpinnings of the disorder has focused on the role of corticostriatal systems, little is known about the role of the cerebellum in DLD. Corticocerebellar circuits might be involved in the disorder as they contribute to complex sensorimotor skill learning, including the acquisition of spoken language. Here, we used diffusion-weighted imaging data from 77 typically developing and 54 children with DLD and performed probabilistic tractography to identify the cerebellum's white matter tracts: the inferior, middle, and superior cerebellar peduncles. Children with DLD showed lower fractional anisotropy (FA) in the inferior cerebellar peduncles (ICP), fiber tracts that carry motor and sensory input via the inferior olive to the cerebellum. Lower FA in DLD was driven by lower axial diffusivity. Probing this further with more sophisticated modeling of diffusion data, we found higher orientation dispersion but no difference in neurite density in the ICP of children with DLD. Reduced FA is therefore unlikely to be reflecting microstructural differences in myelination, rather the organization of axons in these pathways is disrupted. ICP microstructure was not associated with language or motor coordination performance in our sample. We also found no differences in the middle and superior peduncles, the main pathways connecting the cerebellum with the cortex. To conclude, it is not corticocerebellar but atypical olivocerebellar white matter connections that characterize DLD and suggest the involvement of the olivocerebellar system in speech and language acquisition and development.

Variability in white matter structure relates to hallucination proneness

Hallucinations are a prominent transdiagnostic psychiatric symptom but are also prevalent in individuals who do not require clinical care. Moreover, persistent psychosis-like experience in otherwise healthy individuals may be related to an increased risk to transition to a psychotic disorder. This suggests a common etiology across clinical and non-clinical individuals along a multidimensional psychosis continuum that may be detectable in structural variations of the brain. The current diffusion tensor imaging study assessed 50 healthy individuals (35 females) to identify possible differences in white matter associated with hallucination proneness (HP). This approach circumvents potential confounds related to medication, hospitalization, and disease progression common in clinical individuals. We determined how HP relates to white matter structure in selected association, commissural, and projection fiber pathways putatively linked to psychosis. Increased HP was associated with enhanced fractional anisotropy (FA) in the right uncinate fasciculus, the right anterior and posterior arcuate fasciculus, and the corpus callosum. These findings support the notion of a psychosis continuum, providing first evidence of structural white matter variability associated with HP in healthy individuals. Furthermore, alterations in the targeted pathways likely indicate an association between HP-related structural variations and the putative salience and attention mechanisms that these pathways subserve.

Spatial and temporal pattern of structure-function coupling of human brain connectome with development

Brain structural circuitry shapes a richly patterned functional synchronization, supporting for complex cognitive and behavioural abilities. However, how coupling of structural connectome (SC) and functional connectome (FC) develops and its relationships with cognitive functions and transcriptomic architecture remain unclear. We used multimodal magnetic resonance imaging data from 439 participants aged 5.7-21.9 years to predict functional connectivity by incorporating intracortical and extracortical structural connectivity, characterizing SC-FC coupling. Our findings revealed that SC-FC coupling was strongest in the visual and somatomotor networks, consistent with evolutionary expansion, myelin content, and functional principal gradient. As development progressed, SC-FC coupling exhibited heterogeneous alterations dominated by an increase in cortical regions, broadly distributed across the somatomotor, frontoparietal, dorsal attention, and default mode networks. Moreover, we discovered that SC-FC coupling significantly predicted individual variability in general intelligence, mainly influencing frontoparietal and default mode networks. Finally, our results demonstrated that the heterogeneous development of SC-FC coupling is positively associated with genes in oligodendrocyte-related pathways and negatively associated with astrocyte-related genes. This study offers insight into the maturational principles of SC-FC coupling in typical development.

Comparative Analysis of Human-Chimpanzee Divergence in Brain Connectivity and its Genetic Correlates

Chimpanzees (Pan troglodytes) are humans' closest living relatives, making them the most directly relevant comparison point for understanding human brain evolution. Zeroing in on the differences in brain connectivity between humans and chimpanzees can provide key insights into the specific evolutionary changes that might have occured along the human lineage. However, conducting comparisons of brain connectivity between humans and chimpanzees remains challenging, as cross-species brain atlases established within the same framework are currently lacking. Without the availability of cross-species brain atlases, the region-wise connectivity patterns between humans and chimpanzees cannot be directly compared. To address this gap, we built the first Chimpanzee Brainnetome Atlas (ChimpBNA) by following a well-established connectivity-based parcellation framework. Leveraging this new resource, we found substantial divergence in connectivity patterns across most association cortices, notably in the lateral temporal and dorsolateral prefrontal cortex between the two species. Intriguingly, these patterns significantly deviate from the patterns of cortical expansion observed in humans compared to chimpanzees. Additionally, we identified regions displaying connectional asymmetries that differed between species, likely resulting from evolutionary divergence. Genes associated with these divergent connectivities were found to be enriched in cell types crucial for cortical projection circuits and synapse formation. These genes exhibited more pronounced differences in expression patterns in regions with higher connectivity divergence, suggesting a potential foundation for brain connectivity evolution. Therefore, our study not only provides a fine-scale brain atlas of chimpanzees but also highlights the connectivity divergence between humans and chimpanzees in a more rigorous and comparative manner and suggests potential genetic correlates for the observed divergence in brain connectivity patterns between the two species. This can help us better understand the origins and development of uniquely human cognitive capabilities.

BAI-Net: Individualized Anatomical Cerebral Cartography Using Graph Neural Network

Brain atlas is an important tool in the diagnosis and treatment of neurological disorders. However, due to large variations in the organizational principles of individual brains, many challenges remain in clinical applications. Brain atlas individualization network (BAI-Net) is an algorithm that subdivides individual cerebral cortex into segregated areas using brain morphology and connectomes. The presented method integrates group priors derived from a population atlas, adjusts areal probabilities using the context of connectivity fingerprints derived from the fiber-tract embedding of tractography, and provides reliable and explainable individualized brain areas across multiple sessions and scanners. We demonstrate that BAI-Net outperforms the conventional iterative clustering approach by capturing significantly heritable topographic variations in individualized cartographies. The topographic variability of BAI-Net cartographies has shown strong associations with individual variability in brain morphology, connectivity as well as higher relationship on individual cognitive behaviors and genetics. This study provides an explainable framework for individualized brain cartography that may be useful in the precise localization of neuromodulation and treatments on individual brains.

Clinical applicability of automated tractography for stroke rehabilitation: Z-score conversion of fractional anisotropy

[Purpose] To expand the applicability of diffusion-tensor tractography fractional anisotropy for stroke rehabilitation, this study aimed to provide references for representative neural tracts from non-lesioned hemispheres. Therefore, we applied the assessment of neural integrity to representative stroke patients using Z-score conversion. [Participants and Methods] Fractional anisotropy values were assessed in neural tracts, including the corticospinal tract, inferior fronto-occipital fasciculus, uncinate fasciculus, and anterior thalamic radiation, of stroke patients receiving acute care. [Results] Data were collected from 60 patients for the non-lesioned right hemisphere and 68 patients for the non-lesioned left hemisphere. Mean fractional anisotropy values in the corticospinal tract and inferior fronto-occipital fasciculus were notably elevated, reaching approximately 0.6 and 0.5, respectively. The mean fractional anisotropy values for other neural tracts were approximately 0.4, and, the overall standard deviations were approximately 0.04. In two typical stroke patients assessed using Z-scores, the scores in the corticospinal tract corresponded to the severity of the hemiparesis. The scores in the anterior thalamic radiation and inferior fronto-occipital fasciculus were associated with more significant brain dysfunction, including inattention and aphasia. [Conclusion] In this study, the Z-score findings related to stroke symptoms align with those reported in the literature, indicating the appropriateness of the methodology used and its potential in future applications.

Predicting 2-year neurodevelopmental outcomes in preterm infants using multimodal structural brain magnetic resonance imaging with local connectivity

The neurodevelopmental outcomes of preterm infants can be stratified based on the level of prematurity. We explored brain structural networks in extremely preterm (EP; < 28 weeks of gestation) and very-to-late (V-LP; ≥ 28 and < 37 weeks of gestation) preterm infants at term-equivalent age to predict 2-year neurodevelopmental outcomes. Using MRI and diffusion MRI on 62 EP and 131 V-LP infants, we built a multimodal feature set for volumetric and structural network analysis. We employed linear and nonlinear machine learning models to predict the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III) scores, assessing predictive accuracy and feature importance. Our findings revealed that models incorporating local connectivity features demonstrated high predictive performance for BSID-III subsets in preterm infants. Specifically, for cognitive scores in preterm (variance explained, 17%) and V-LP infants (variance explained, 17%), and for motor scores in EP infants (variance explained, 15%), models with local connectivity features outperformed others. Additionally, a model using only local connectivity features effectively predicted language scores in preterm infants (variance explained, 15%). This study underscores the value of multimodal feature sets, particularly local connectivity, in predicting neurodevelopmental outcomes, highlighting the utility of machine learning in understanding microstructural changes and their implications for early intervention.

Age of onset of obsessive-compulsive disorder differentially affects white matter microstructure

Previous diffusion MRI studies have reported mixed findings on white matter microstructure alterations in obsessive-compulsive disorder (OCD), likely due to variation in demographic and clinical characteristics, scanning methods, and underpowered samples. The OCD global study was created across five international sites to overcome these challenges by harmonizing data collection to identify consistent brain signatures of OCD that are reproducible and generalizable. Single-shell diffusion measures (e.g., fractional anisotropy), multi-shell Neurite Orientation Dispersion and Density Imaging (NODDI) and fixel-based measures, were extracted from skeletonized white matter tracts in 260 medication-free adults with OCD and 252 healthy controls. We additionally performed structural connectome analysis. We compared cases with controls and cases with early (<18) versus late (18+) OCD onset using mixed-model and Bayesian multilevel analysis. Compared with healthy controls, adult OCD individuals showed higher fiber density in the sagittal stratum (B[SE] = 0.10[0.05], P = 0.04) and credible evidence for higher fiber density in several other tracts. When comparing early (n = 145) and late-onset (n = 114) cases, converging evidence showed lower integrity of the posterior thalamic radiation -particularly radial diffusivity (B[SE] = 0.28[0.12], P = 0.03)-and lower global efficiency of the structural connectome (B[SE] = 15.3[6.6], P = 0.03) in late-onset cases. Post-hoc analyses indicated divergent direction of effects of the two OCD groups compared to healthy controls. Age of OCD onset differentially affects the integrity of thalamo-parietal/occipital tracts and the efficiency of the structural brain network. These results lend further support for the role of the thalamus and its afferent fibers and visual attentional processes in the pathophysiology of OCD.

Spatial and temporal pattern of structure-function coupling of human brain connectome with development

Brain structural circuitry shapes a richly patterned functional synchronization, supporting for complex cognitive and behavioural abilities. However, how coupling of structural connectome (SC) and functional connectome (FC) develops and its relationships with cognitive functions and transcriptomic architecture remain unclear. We used multimodal magnetic resonance imaging data from 439 participants aged 5.7 to 21.9 years to predict functional connectivity by incorporating intracortical and extracortical structural connectivity, characterizing SC-FC coupling. Our findings revealed that SC-FC coupling was strongest in the visual and somatomotor networks, consistent with evolutionary expansion, myelin content, and functional principal gradient. As development progressed, SC-FC coupling exhibited heterogeneous alterations dominated by an increase in cortical regions, broadly distributed across the somatomotor, frontoparietal, dorsal attention, and default mode networks. Moreover, we discovered that SC-FC coupling significantly predicted individual variability in general intelligence, mainly influencing frontoparietal and default mode networks. Finally, our results demonstrated that the heterogeneous development of SC-FC coupling is positively associated with genes in oligodendrocyte-related pathways and negatively associated with astrocyte-related genes. This study offers insight into the maturational principles of SC-FC coupling in typical development.

Feature similarity gradients detect alterations in the neonatal cortex associated with preterm birth

The early life environment programmes cortical architecture and cognition across the life course. A measure of cortical organisation that integrates information from multimodal MRI and is unbound by arbitrary parcellations has proven elusive, which hampers efforts to uncover the perinatal origins of cortical health. Here, we use the Vogt-Bailey index to provide a fine-grained description of regional homogeneities and sharp variations in cortical microstructure based on feature gradients, and we investigate the impact of being born preterm on cortical development at term-equivalent age. Compared with term-born controls, preterm infants have a homogeneous microstructure in temporal and occipital lobes, and the medial parietal, cingulate, and frontal cortices, compared with term infants. These observations replicated across two independent datasets and were robust to differences that remain in the data after matching samples and alignment of processing and quality control strategies. We conclude that cortical microstructural architecture is altered in preterm infants in a spatially distributed rather than localised fashion.

GPU-Based Parallel Processing Techniques for Enhanced Brain Magnetic Resonance Imaging Analysis: A Review of Recent Advances

The approach of using more than one processor to compute in order to overcome the complexity of different medical imaging methods that make up an overall job is known as GPU (graphic processing unit)-based parallel processing. It is extremely important for several medical imaging techniques such as image classification, object detection, image segmentation, registration, and content-based image retrieval, since the GPU-based parallel processing approach allows for time-efficient computation by a software, allowing multiple computations to be completed at once. On the other hand, a non-invasive imaging technology that may depict the shape of an anatomy and the biological advancements of the human body is known as magnetic resonance imaging (MRI). Implementing GPU-based parallel processing approaches in brain MRI analysis with medical imaging techniques might be helpful in achieving immediate and timely image capture. Therefore, this extended review (the extension of the IWBBIO2023 conference paper) offers a thorough overview of the literature with an emphasis on the expanding use of GPU-based parallel processing methods for the medical analysis of brain MRIs with the imaging techniques mentioned above, given the need for quicker computation to acquire early and real-time feedback in medicine. Between 2019 and 2023, we examined the articles in the literature matrix that include the tasks, techniques, MRI sequences, and processing results. As a result, the methods discussed in this review demonstrate the advancements achieved until now in minimizing computing runtime as well as the obstacles and problems still to be solved in the future.

On the stability of canonical correlation analysis and partial least squares with application to brain-behavior associations

Associations between datasets can be discovered through multivariate methods like Canonical Correlation Analysis (CCA) or Partial Least Squares (PLS). A requisite property for interpretability and generalizability of CCA/PLS associations is stability of their feature patterns. However, stability of CCA/PLS in high-dimensional datasets is questionable, as found in empirical characterizations. To study these issues systematically, we developed a generative modeling framework to simulate synthetic datasets. We found that when sample size is relatively small, but comparable to typical studies, CCA/PLS associations are highly unstable and inaccurate; both in their magnitude and importantly in the feature pattern underlying the association. We confirmed these trends across two neuroimaging modalities and in independent datasets with n ≈ 1000 and n = 20,000, and found that only the latter comprised sufficient observations for stable mappings between imaging-derived and behavioral features. We further developed a power calculator to provide sample sizes required for stability and reliability of multivariate analyses. Collectively, we characterize how to limit detrimental effects of overfitting on CCA/PLS stability, and provide recommendations for future studies.

Applicability of multiple quantitative magnetic resonance methods in genetic brain white matter disorders

BACKGROUND AND PURPOSE

Magnetic resonance imaging (MRI) measures of tissue microstructure are important for monitoring brain white matter (WM) disorders like leukodystrophies and multiple sclerosis. They should be sensitive to underlying pathological changes. Three whole-brain isotropic quantitative methods were applied and compared within a cohort of controls and leukodystrophy patients: two novel myelin water imaging (MWI) techniques (multi-compartment relaxometry diffusion-informed MWI: MCR-DIMWI, and multi-echo T2 relaxation imaging with compressed sensing: METRICS) and neurite orientation dispersion and density imaging (NODDI).

METHODS

For 9 patients with different leukodystrophies (age range 0.4-62.4 years) and 15 control subjects (2.3-61.3 years), T1-weighted MRI, fluid-attenuated inversion recovery, multi-echo gradient echo with variable flip angles, METRICS, and multi-shell diffusion-weighted imaging were acquired on 3 Tesla. MCR-DIMWI, METRICS, NODDI, and quality control measures were extracted to evaluate differences between patients and controls in WM and deep gray matter (GM) regions of interest (ROIs). Pearson correlations, effect size calculations, and multi-level analyses were performed.

RESULTS

MCR-DIMWI and METRICS-derived myelin water fractions (MWFs) were lower and relaxation times were higher in patients than in controls. Effect sizes of MWF values and relaxation times were large for both techniques. Differences between patients and controls were more pronounced in WM ROIs than in deep GM. MCR-DIMWI-MWFs were more homogeneous within ROIs and more bilaterally symmetrical than METRICS-MWFs. The neurite density index was more sensitive in detecting differences between patients and controls than fractional anisotropy. Most measures obtained from MCR-DIMWI, METRICS, NODDI, and diffusion tensor imaging correlated strongly with each other.

CONCLUSION

This proof-of-concept study shows that MCR-DIMWI, METRICS, and NODDI are sensitive techniques to detect changes in tissue microstructure in WM disorders.

Longitudinal development of the human white matter structural connectome and its association with brain transcriptomic and cellular architecture

From childhood to adolescence, the spatiotemporal development pattern of the human brain white matter connectome and its underlying transcriptomic and cellular mechanisms remain largely unknown. With a longitudinal diffusion MRI cohort of 604 participants, we map the developmental trajectory of the white matter connectome from global to regional levels and identify that most brain network properties followed a linear developmental trajectory. Importantly, connectome-transcriptomic analysis reveals that the spatial development pattern of white matter connectome is potentially regulated by the transcriptomic architecture, with positively correlated genes involve in ion transport- and development-related pathways expressed in excitatory and inhibitory neurons, and negatively correlated genes enriches in synapse- and development-related pathways expressed in astrocytes, inhibitory neurons and microglia. Additionally, the macroscale developmental pattern is also associated with myelin content and thicknesses of specific laminas. These findings offer insights into the underlying genetics and neural mechanisms of macroscale white matter connectome development from childhood to adolescence.

Applicability of fractional anisotropy from standardized automated tractography for outcome prediction of patients after stroke

[Purpose] Diffusion-tensor fractional anisotropy has been used for outcome prediction in stroke patients. We assessed the clinical applicability of the two major fractional anisotropy methodologies-fractional anisotropy derived from segmentation maps in the standard brain (region of interest) and fractional anisotropy derived from standardized automated tractography-in relation to outcomes. [Participants and Methods] The study design was a retrospective survey of medical records collected from October 2021 to September 2022. Diffusion-tensor imaging was conducted in the second week after stroke onset. Outcomes were assessed using the total score of the motor component of the Stroke Impairment Assessment Set (null to full, 0 to 25). Correlations between fractional anisotropy and the outcomes were then assessed. [Results] Fourteen patients with hemorrhagic stroke were sampled. The fractional anisotropy from standardized automated tractography of the corticospinal tract on the lesion side (mean ± standard deviation, 0.403 ± 0.070) was significantly and tightly correlated (r=0.813) with the outcomes (13.4 ± 9.2), whereas the fractional anisotropy from a region of interest set in the cerebral peduncle on the lesion side (0.548 ± 0.064) was not significantly correlated with the outcomes (r=0.507). [Conclusion] The findings suggest that fractional anisotropy derived from standardized automated tractography can be more applicable to outcome prediction than that derived from a region of interest defined in the standard brain.

Automated Tractography for the Assessment of Aphasia in Acute Care Stroke Rehabilitation: A Case Series

Background

Aphasia is a common disorder among stroke patients. Assessment of aphasia is essential for scheduling appropriate rehabilitative treatment. Although this is conventionally accomplished using neuropsychological test batteries, these tests are not always accessible because of attention and/or consciousness disturbances during acute care. To overcome this issue, we have introduced a newly developed automated tractography known as XTRACT.

Cases

Diffusion-tensor images were acquired from three patients on days 10-14. Brain images were processed by XTRACT, which automatically extracts neural tracts using standardized protocols. Fractional anisotropy (FA) values were then bilaterally evaluated in the following neural tracts associated with aphasia: arcuate fasciculus, inferior fronto-occipital fasciculus, middle longitudinal fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus. Case 1 had word-finding difficulty on admission. FA values in the lesioned left hemisphere were not decreased in all tracts and this patient fully recovered during acute care. Case 2 had reduced spontaneous speech and a low FA value in the left arcuate fasciculus. Rehabilitative treatment was scheduled to improve the verbal output of sentences and word recall. Case 3 could not complete the conventional aphasia test battery because of attention disturbance. He had low FA values in all tracts in the left hemisphere. Rehabilitative treatment was designed to focus on both speaking and auditory comprehension.

Discussion

Automated tractography enables quantitative assessment of the neural damage associated with aphasia, even in patients with attention and/or consciousness disturbances. This modality can aid in the assessment of aphasia and allows the planning of appropriate rehabilitative treatment.

Cognitive, behavioral, neuroimaging and inflammatory biomarkers after hospitalization for covid-19 in Brazil

Post-COVID-19 Condition (PCC) refers to a multisystemic syndrome that persists for months after SARS-CoV-2 infection. Cognitive deficits, fatigue, depression, and anxiety are common manifestations of the condition, but the underlying mechanisms driving these long-lasting neuropsychiatric features are still unclear. We conducted a prospective multi-method investigation of post-hospitalization COVID-19 patients in Rio de Janeiro, Brazil. After months from hospital admission (mean = 168.45 ± 90.31 days; range = 75.00-365.00 days), COVID-19 survivors (n = 72) presented significant difficulties in tests tapping global cognition, episodic memory, working memory and inhibitory control relative to controls and to validated normative scores. A considerable proportion of participants suffered from fatigue (36.1 %), anxiety (27.8 %), and depressive symptoms (43.1 %). Elevated blood levels of TNF-α, during hospitalization, and TNF-α and IL-1β, at follow-up, correlated with changes in brain microstructural diffusion indices (β = 0.144, p = 0.005). These neuroimaging markers were associated with decreased episodic memory (β = -0.221, p = 0.027), working memory (β = -0.209, p = 0.034) and inhibitory control (β = -0.183, p = 0.010) at follow-up. Severity of depressive symptoms correlated with deficits in global cognition in post-COVID-19 cases (β = -0.366, p = 0.038). Our study provides preliminary evidence that long-term cognitive dysfunction following COVID-19 may be mediated by brain microstructural damage, triggered by persistent neuroinflammation. In addition, depressive symptoms may contribute to prolongated global cognitive impairments in those cases.

Non-reversibility outperforms functional connectivity in characterisation of brain states in MEG data

Characterising brain states during tasks is common practice for many neuroscientific experiments using electrophysiological modalities such as electroencephalography (EEG) and magnetoencephalography (MEG). Brain states are often described in terms of oscillatory power and correlated brain activity, i.e. functional connectivity. It is, however, not unusual to observe weak task induced functional connectivity alterations in the presence of strong task induced power modulations using classical time-frequency representation of the data. Here, we propose that non-reversibility, or the temporal asymmetry in functional interactions, may be more sensitive to characterise task induced brain states than functional connectivity. As a second step, we explore causal mechanisms of non-reversibility in MEG data using whole brain computational models. We include working memory, motor, language tasks and resting-state data from participants of the Human Connectome Project (HCP). Non-reversibility is derived from the lagged amplitude envelope correlation (LAEC), and is based on asymmetry of the forward and reversed cross-correlations of the amplitude envelopes. Using random forests, we find that non-reversibility outperforms functional connectivity in the identification of task induced brain states. Non-reversibility shows especially better sensitivity to capture bottom-up gamma induced brain states across all tasks, but also alpha band associated brain states. Using whole brain computational models we find that asymmetry in the effective connectivity and axonal conduction delays play a major role in shaping non-reversibility across the brain. Our work paves the way for better sensitivity in characterising brain states during both bottom-up as well as top-down modulation in future neuroscientific experiments.

An open resource combining multi-contrast MRI and microscopy in the macaque brain

Understanding brain structure and function often requires combining data across different modalities and scales to link microscale cellular structures to macroscale features of whole brain organisation. Here we introduce the BigMac dataset, a resource combining in vivo MRI, extensive postmortem MRI and multi-contrast microscopy for multimodal characterisation of a single whole macaque brain. The data spans modalities (MRI and microscopy), tissue states (in vivo and postmortem), and four orders of spatial magnitude, from microscopy images with micrometre or sub-micrometre resolution, to MRI signals on the order of millimetres. Crucially, the MRI and microscopy images are carefully co-registered together to facilitate quantitative multimodal analyses. Here we detail the acquisition, curation, and first release of the data, that together make BigMac a unique, openly-disseminated resource available to researchers worldwide. Further, we demonstrate example analyses and opportunities afforded by the data, including improvement of connectivity estimates from ultra-high angular resolution diffusion MRI, neuroanatomical insight provided by polarised light imaging and myelin-stained histology, and the joint analysis of MRI and microscopy data for reconstruction of the microscopy-inspired connectome. All data and code are made openly available.

Microstructural Properties of the Cerebellar Peduncles in Children with Developmental Language Disorder

Children with developmental language disorder (DLD) struggle to learn their native language for no apparent reason. While research on the neurobiological underpinnings of the disorder has focused on the role of cortico-striatal systems, little is known about the role of the cerebellum in DLD. Cortico-cerebellar circuits might be involved in the disorder as they contribute to complex sensorimotor skill learning, including the acquisition of spoken language. Here, we used diffusion-weighted imaging data from 77 typically developing and 54 children with DLD and performed probabilistic tractography to identify the cerebellum's white matter tracts: the inferior, middle, and superior cerebellar peduncles. Children with DLD showed lower fractional anisotropy (FA) in the inferior cerebellar peduncles (ICP), fiber tracts that carry motor and sensory input via the inferior olive to the cerebellum. Lower FA in DLD was driven by lower axial diffusivity. Probing this further with more sophisticated modeling of diffusion data, we found higher orientation dispersion but no difference in neurite density in the ICP of DLD. Reduced FA is therefore unlikely to be reflecting microstructural differences in myelination in this tract, rather the organization of axons in these pathways is disrupted. ICP microstructure was not associated with language or motor coordination performance in our sample. We also found no differences in the middle and superior peduncles, the main pathways connecting the cerebellum with the cortex. To conclude, it is not cortico-cerebellar but atypical olivocerebellar white matter connections that characterize DLD and suggest the involvement of the olivocerebellar system in speech acquisition and development.

Characterization of Atypical Corticospinal Tract Microstructure and Hand Impairments in Early-Onset Hemiplegic Cerebral Palsy: Preliminary Findings

Unilateral brain injuries occurring before at or shortly after full-term can result in hemiplegic cerebral palsy (HCP). HCP affects one side of the body and can be characterized in the hand with measures of weakness and a loss of independent hand control resulting in mirror movements. Hand impairment severity is extremely heterogeneous across individuals with HCP and the neural basis for this variability is unclear. We used diffusion MRI and tractography to investigate the relationship between structural morphology of the supraspinal corticospinal tract (CST) and the severity of two typical hand impairments experienced by individuals with HCP, grasp weakness and mirror movements. Results from nine children with HCP and eight children with typical development show that there is a significant hemispheric association between CST microstructure and hand impairment severity that may be explained by atypical development and fiber distribution of motor pathways. Further analysis in the non-lesioned (dominant) hemisphere shows significant differences for CST termination in the cortex between participants with HCP and those with typical development. These findings suggest that structural disparities at the cellular level in the seemingly unaffected hemisphere after early unilateral brain injury may be the cause of heterogeneous hand impairments seen in this population.Clinical Relevance- Quantitative measurement of the variability in hand function in individuals with HCP is necessary to represent the distinct impairments experienced by each person. Further understanding of the structural neural morphology underlying distal upper extremity motor deficits after early unilateral brain injury will help lead to the development of more specific targeted interventions that increase functional outcomes.

A framework for focal and connectomic mapping of transiently disrupted brain function

The distributed nature of the neural substrate, and the difficulty of establishing necessity from correlative data, combine to render the mapping of brain function a far harder task than it seems. Methods capable of combining connective anatomical information with focal disruption of function are needed to disambiguate local from global neural dependence, and critical from merely coincidental activity. Here we present a comprehensive framework for focal and connective spatial inference based on sparse disruptive data, and demonstrate its application in the context of transient direct electrical stimulation of the human medial frontal wall during the pre-surgical evaluation of patients with focal epilepsy. Our framework formalizes voxel-wise mass-univariate inference on sparsely sampled data within the statistical parametric mapping framework, encompassing the analysis of distributed maps defined by any criterion of connectivity. Applied to the medial frontal wall, this transient dysconnectome approach reveals marked discrepancies between local and distributed associations of major categories of motor and sensory behaviour, revealing differentiation by remote connectivity to which purely local analysis is blind. Our framework enables disruptive mapping of the human brain based on sparsely sampled data with minimal spatial assumptions, good statistical efficiency, flexible model formulation, and explicit comparison of local and distributed effects.

A spatiotemporal complexity architecture of human brain activity

The human brain operates in large-scale functional networks. These networks are an expression of temporally correlated activity across brain regions, but how global network properties relate to the neural dynamics of individual regions remains incompletely understood. Here, we show that the brain's network architecture is tightly linked to critical episodes of neural regularity, visible as spontaneous "complexity drops" in functional magnetic resonance imaging signals. These episodes closely explain functional connectivity strength between regions, subserve the propagation of neural activity patterns, and reflect interindividual differences in age and behavior. Furthermore, complexity drops define neural activity states that dynamically shape the connectivity strength, topological configuration, and hierarchy of brain networks and comprehensively explain known structure-function relationships within the brain. These findings delineate a principled complexity architecture of neural activity-a human "complexome" that underpins the brain's functional network organization.

Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution

Knee osteoarthritis (OA) is the most common osteoarthritis and a leading cause of disability. Cartilage defects are regarded as major manifestations of knee OA, which are visible by magnetic resonance imaging (MRI). Thus early detection and assessment for knee cartilage defects are important for protecting patients from knee OA. In this way, many attempts have been made on knee cartilage defect assessment by applying convolutional neural networks (CNNs) to knee MRI. However, the physiologic characteristics of the cartilage may hinder such efforts: the cartilage is a thin curved layer, implying that only a small portion of voxels in knee MRI can contribute to the cartilage defect assessment; heterogeneous scanning protocols further challenge the feasibility of the CNNs in clinical practice; the CNN-based knee cartilage evaluation results lack interpretability. To address these challenges, we model the cartilages structure and appearance from knee MRI into a graph representation, which is capable of handling highly diverse clinical data. Then, guided by the cartilage graph representation, we design a non-Euclidean deep learning network with the self-attention mechanism, to extract cartilage features in the local and global, and to derive the final assessment with a visualized result. Our comprehensive experiments show that the proposed method yields superior performance in knee cartilage defect assessment, plus its convenient 3D visualization for interpretability.

Applicability of automated tractography during acute care stroke rehabilitation

[Purpose] To assess the clinical applicability of a novel automated tractography tool named XTRACT during acute stroke rehabilitation. [Participants and Methods] Three patients with left hemisphere stroke were sampled. Diffusion tensor images were acquired on the second week, and automated tractography was then applied. Tractography images and fractional anisotropy (FA) values in the corticospinal tract (CST) and arcuate fasciculus (AF) were assessed in relation to hemiparesis and aphasia. [Results] Patient 1 was nearly asymptomatic; FA in the left CST was 0.610 and that in the AF was 0.509. Patient 2 had severe hemiparesis and mild motor aphasia. Tractography images of the CST and AF were blurred; FA in the left CST was 0.295 and that in the AF was 0.304. Patient 3 showed no hemiparesis or aphasia at initial assessment. Tractography image of the CST was intact but that of the AF was less clear; FA in the left CST was 0.586 and that in the AF was 0.338. Considering the less clear images of the AF and lower FA value in Patients 2 and 3, further examinations for aphasia were performed, which revealed agraphia. [Conclusion] Visualization and quantification of neural fibers using automated tractography promoted planning acute care rehabilitative treatment in patients with stroke.

White matter alterations associated with lifetime and current depression in adolescents: Evidence for cingulum disruptions

INTRODUCTION

Compared to research on adults with depression, relatively little work has examined white matter microstructure differences in depression arising earlier in life. Here we tested hypotheses about disruptions to white matter structure in adolescents with current and past depression, with an a priori focus on the cingulum bundles, uncinate fasciculi, corpus collosum, and superior longitudinal fasciculus.

METHODS

One hundred thirty-one children from the Preschool Depression Study were assessed using a Human Connectome Project style diffusion imaging sequence which was processed with HCP pipelines and TRACULA to generate estimates of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD).

RESULTS

We found that reduced FA, reduced AD, and increased RD in the dorsal cingulum bundle were associated with a lifetime diagnosis of major depression and greater cumulative and current depression severity. Reduced FA, reduced AD, and increased RD in the ventral cingulum were associated with greater cumulative depression severity.

CONCLUSION

These findings support the emergence of white matter differences detected in adolescence associated with earlier life and concurrent depression. They also highlight the importance of connections of the cingulate to other brain regions in association with depression, potentially relevant to understanding emotion dysregulation and functional connectivity differences in depression.

Early structural connectivity within the sensorimotor network: Deviations related to prematurity and association to neurodevelopmental outcome

Consisting of distributed and interconnected structures that interact through cortico-cortical connections and cortico-subcortical loops, the sensorimotor (SM) network undergoes rapid maturation during the perinatal period and is thus particularly vulnerable to preterm birth. However, the impact of prematurity on the development and integrity of the emerging SM connections and their relationship to later motor and global impairments are still poorly understood. In this study we aimed to explore to which extent the early microstructural maturation of SM white matter (WM) connections at term-equivalent age (TEA) is modulated by prematurity and related with neurodevelopmental outcome at 18 months corrected age. We analyzed 118 diffusion MRI datasets from the developing Human Connectome Project (dHCP) database: 59 preterm (PT) low-risk infants scanned near TEA and a control group of full-term (FT) neonates paired for age at MRI and sex. We delineated WM connections between the primary SM cortices (S1, M1 and paracentral region) and subcortical structures using probabilistic tractography, and evaluated their microstructure with diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) models. To go beyond tract-specific univariate analyses, we computed a maturational distance related to prematurity based on the multi-parametric Mahalanobis distance of each PT infant relative to the FT group. Our results confirmed the presence of microstructural differences in SM tracts between PT and FT infants, with effects increasing with lower gestational age at birth. Maturational distance analyses highlighted that prematurity has a differential effect on SM tracts with higher distances and thus impact on (i) cortico-cortical than cortico-subcortical connections; (ii) projections involving S1 than M1 and paracentral region; and (iii) the most rostral cortico-subcortical tracts, involving the lenticular nucleus. These different alterations at TEA suggested that vulnerability follows a specific pattern coherent with the established WM caudo-rostral progression of maturation. Finally, we highlighted some relationships between NODDI-derived maturational distances of specific tracts and fine motor and cognitive outcomes at 18 months. As a whole, our results expand understanding of the significant impact of premature birth and early alterations on the emerging SM network even in low-risk infants, with possible relationship with neurodevelopmental outcomes. This encourages further exploration of these potential neuroimaging markers for prediction of neurodevelopmental disorders, with special interest for subtle neuromotor impairments frequently observed in preterm-born children.

Estimating axial diffusivity in the NODDI model

To estimate microstructure-related parameters from diffusion MRI data, biophysical models make strong, simplifying assumptions about the underlying tissue. The extent to which many of these assumptions are valid remains an open research question. This study was inspired by the disparity between the estimated intra-axonal axial diffusivity from literature and that typically assumed by the Neurite Orientation Dispersion and Density Imaging (NODDI) model (d_∥=1.7μm²/ms). We first demonstrate how changing the assumed axial diffusivity results in considerably different NODDI parameter estimates. Second, we illustrate the ability to estimate axial diffusivity as a free parameter of the model using high b-value data and an adapted NODDI framework. Using both simulated and in vivo data we investigate the impact of fitting to either real-valued or magnitude data, with Gaussian and Rician noise characteristics respectively, and what happens if we get the noise assumptions wrong in this high b-value and thus low SNR regime. Our results from real-valued human data estimate intra-axonal axial diffusivities of ∼2-2.5μm²/ms, in line with current literature. Crucially, our results demonstrate the importance of accounting for both a rectified noise floor and/or a signal offset to avoid biased parameter estimates when dealing with low SNR data.

Diffusion magnetic resonance imaging connectome features are predictive of functional lateralization of semantic processing in the anterior temporal lobes

Assessment of regional language lateralization is crucial in many scenarios, but not all populations are suited for its evaluation via task-functional magnetic resonance imaging (fMRI). In this study, the utility of structural connectome features for the classification of language lateralization in the anterior temporal lobes (ATLs) was investigated. Laterality indices for semantic processing in the ATL were computed from task-fMRI in 1038 subjects from the Human Connectome Project who were labeled as stronger rightward lateralized (RL) or stronger leftward to bilaterally lateralized (LL) in a data-driven approach. Data of unrelated subjects (n = 432) were used for further analyses. Structural connectomes were generated from diffusion-MRI tractography, and graph theoretical metrics (node degree, betweenness centrality) were computed. A neural network (NN) and a random forest (RF) classifier were trained on these metrics to classify subjects as RL or LL. After classification, comparisons of network measures were conducted via permutation testing. Degree-based classifiers produced significant above-chance predictions both during cross-validation (NN: AUC-ROC[CI] = 0.68[0.64-0.73], accuracy[CI] = 68.34%[63-73.2%]; RF: AUC-ROC[CI] = 0.7[0.66-0.73], accuracy[CI] = 64.81%[60.9-68.5]) and testing (NN: AUC-ROC[CI] = 0.69[0.53-0.84], accuracy[CI] = 68.09[53.2-80.9]; RF: AUC-ROC[CI] = 0.68[0.53-0.84], accuracy[CI] = 68.09[55.3-80.9]). Comparison of network metrics revealed small effects of increased node degree within the right posterior middle temporal gyrus (pMTG) in subjects with RL, while degree was decreased in the right posterior cingulate cortex (PCC). Above-chance predictions of functional language lateralization in the ATL are possible based on diffusion-MRI connectomes alone. Increased degree within the right pMTG as a right-sided homologue of a known semantic hub, and decreased hubness of the right PCC may form a structural basis for rightward-lateralized semantic processing.

Methodological evaluation of individual cognitive prediction based on the brain white matter structural connectome

An emerging trend is to use regression-based machine learning approaches to predict cognitive functions at the individual level from neuroimaging data. However, individual prediction models are inherently influenced by the vast options for network construction and model selection in machine learning pipelines. In particular, the brain white matter (WM) structural connectome lacks a systematic evaluation of the effects of different options in the pipeline on predictive performance. Here, we focused on the methodological evaluation of brain structural connectome-based predictions. For network construction, we considered two parcellation schemes for defining nodes and seven strategies for defining edges. For the regression algorithms, we used eight regression models. Four cognitive domains and brain age were targeted as predictive tasks based on two independent datasets (Beijing Aging Brain Rejuvenation Initiative [BABRI]: 633 healthy older adults; Human Connectome Projects in Aging [HCP-A]: 560 healthy older adults). Based on the results, the WM structural connectome provided a satisfying predictive ability for individual age and cognitive functions, especially for executive function and attention. Second, different parcellation schemes induce a significant difference in predictive performance. Third, prediction results from different data sets showed that dMRI with distinct acquisition parameters may plausibly result in a preference for proper fiber reconstruction algorithms and different weighting options. Finally, deep learning and Elastic-Net models are more accurate and robust in connectome-based predictions. Together, significant effects of different options in WM network construction and regression algorithms on the predictive performances are identified in this study, which may provide important references and guidelines to select suitable options for future studies in this field.

The structural brain network topology of episodic memory

Episodic memory is supported by a distributed network of brain regions, and this complex network of regions does not operate in isolation. To date, neuroscience research in this area has typically focused on the activation levels in specific regions or pairwise connectivity between such regions. However, research has yet to investigate how the complex interactions of structural brain networks influence episodic memory abilities. We applied graph theory methods to diffusion-based anatomical networks in order to examine the structural architecture of the medial temporal lobe needed to support effective episodic memory functioning. We examined the relationship between performance on tests of verbal and non-verbal episodic memory with node strength, which indexes how well connected a brain region is in the network. Findings mapped onto the Posterior Medial memory system, subserved by the parahippocampal cortex and overlapped with findings of previous studies of episodic memory employing different methodologies. This expands our current understanding by providing independent evidence for the importance of identified regions and suggesting the particular manner in which these regions support episodic memory.

Laterality and Sex Differences of Human Lateral Habenula Afferent and Efferent Fiber Tracts

Introduction

The lateral habenula (LHb) is an epithalamic nucleus associated with negative valence and affective disorders. It receives input via the stria medullaris (SM) and sends output via the fasciculus retroflexus (FR). Here, we use tractography to reconstruct and characterize this pathway.

Methods

Multi-shell human diffusion magnetic resonance imaging (dMRI) data was obtained from the human connectome project (HCP) (n = 20, 10 males) and from healthy controls (n = 10, 6 males) scanned at our institution. We generated LHb afferents and efferents using probabilistic tractography by selecting the pallidum as the seed region and the ventral tegmental area as the output target.

Results

We were able to reconstruct the intended streamlines in all individuals from the HCP dataset and our dataset. Our technique also aided in identification of the LHb. In right-handed individuals, the streamlines were significantly more numerous in the left hemisphere (mean ratio 1.59 ± 0.09, p = 0.04). In left-handed individuals, there was no hemispheric asymmetry on average (mean ratio 1.00 ± 0.09, p = 1.0). Additionally, these streamlines were significantly more numerous in females than in males (619.9 ± 159.7 vs. 225.9 ± 66.03, p = 0.04).

Conclusion

We developed a method to reconstruct the SM and FR without manual identification of the LHb. This technique enables targeting of these fiber tracts as well as the LHb. Furthermore, we have demonstrated that there are sex and hemispheric differences in streamline number. These findings may have therapeutic implications and warrant further investigation.