Sex and mental health are related to subcortical brain microstructure.

Some mental health problems such as depression and anxiety are more common in females, while others such as autism and attention deficit/hyperactivity (AD/H) are more common in males. However, the neurobiological origins of these sex differences are poorly understood. Animal studies have shown substantial sex differences in neuronal and glial cell structure, while human brain imaging studies have shown only small differences, which largely reflect overall body and brain size. Advanced diffusion MRI techniques can be used to examine intracellular, extracellular, and free water signal contributions and provide unique insights into microscopic cellular structure. However, the extent to which sex differences exist in these metrics of subcortical gray matter structures implicated in psychiatric disorders is not known. Here, we show large sex-related differences in microstructure in subcortical regions, including the hippocampus, thalamus, and nucleus accumbens in a large sample of young adults. Unlike conventional T1-weighted structural imaging, large sex differences remained after adjustment for age and brain volume. Further, diffusion metrics in the thalamus and amygdala were associated with depression, anxiety, AD/H, and antisocial personality problems. Diffusion MRI may provide mechanistic insights into the origin of sex differences in behavior and mental health over the life course and help to bridge the gap between findings from experimental, epidemiological, and clinical mental health research.

Evolutionary continuity and divergence of auditory dorsal and ventral pathways in primates revealed by ultra-high field diffusion MRI.

Auditory dorsal and ventral pathways in the human brain play important roles in supporting speech and language processing. However, the evolutionary root of the dual auditory pathways in the primate brain is unclear. By parcellating the auditory cortex of marmosets (a New World monkey species), macaques (an Old World monkey species), and humans using the same individual-based analysis method and tracking the pathways from the auditory cortex based on multi-shell diffusion-weighted MRI (dMRI), homologous auditory dorsal and ventral fiber tracks were identified in these primate species. The ventral pathway was found to be well conserved in all three primate species analyzed but extend to more anterior temporal regions in humans. In contrast, the dorsal pathway showed a divergence between monkey and human brains. First, frontal regions in the human brain have stronger connections to the higher-level auditory regions than to the lower-level auditory regions along the dorsal pathway, while frontal regions in the monkey brain show opposite connection patterns along the dorsal pathway. Second, the left lateralization of the dorsal pathway is only found in humans. Moreover, the connectivity strength of the dorsal pathway in marmosets is more similar to that of humans than macaques. These results demonstrate the continuity and divergence of the dual auditory pathways in the primate brains along the evolutionary path, suggesting that the putative neural networks supporting human speech and language processing might have emerged early in primate evolution.

Brain imaging and neuropsychological assessment of individuals recovered from a mild to moderate SARS-CoV-2 infection.

As severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infections have been shown to affect the central nervous system, the investigation of associated alterations of brain structure and neuropsychological sequelae is crucial to help address future health care needs. Therefore, we performed a comprehensive neuroimaging and neuropsychological assessment of 223 nonvaccinated individuals recovered from a mild to moderate SARS-CoV-2 infection (100 female/123 male, age [years], mean ± SD, 55.54 ± 7.07; median 9.7 mo after infection) in comparison with 223 matched controls (93 female/130 male, 55.74 ± 6.60) within the framework of the Hamburg City Health Study. Primary study outcomes were advanced diffusion MRI measures of white matter microstructure, cortical thickness, white matter hyperintensity load, and neuropsychological test scores. Among all 11 MRI markers tested, significant differences were found in global measures of mean diffusivity (MD) and extracellular free water which were elevated in the white matter of post-SARS-CoV-2 individuals compared to matched controls (free water: 0.148 ± 0.018 vs. 0.142 ± 0.017, P < 0.001; MD [10-3 mm2/s]: 0.747 ± 0.021 vs. 0.740 ± 0.020, P < 0.001). Group classification accuracy based on diffusion imaging markers was up to 80%. Neuropsychological test scores did not significantly differ between groups. Collectively, our findings suggest that subtle changes in white matter extracellular water content last beyond the acute infection with SARS-CoV-2. However, in our sample, a mild to moderate SARS-CoV-2 infection was not associated with neuropsychological deficits, significant changes in cortical structure, or vascular lesions several months after recovery. External validation of our findings and longitudinal follow-up investigations are needed.

Diffusion MRI of the Hippocampus.

The hippocampus is a brain structure that plays key roles in a variety of cognitive processes. Critically, a wide range of neurological disorders are associated with degeneration of the hippocampal microstructure, defined as neurons, dendrites, glial cells, and more. Thus, the hippocampus is a key target for methods that are sensitive to these microscale properties. Diffusion MRI is one such method, which can noninvasively probe neural architecture. Here we review the extensive use of diffusion MRI to capture hippocampal microstructure in both health and disease. The results of these studies indicate that (1) diffusion tensor imaging is sensitive but not specific to the hippocampal microstructure; (2) biophysical modeling of diffusion MRI signals is a promising avenue to capture more specific aspects of the hippocampal microstructure; (3) use of ultra-short diffusion times have shown unique laminar-specific microstructure and response to hippocampal injury; (4) dispersion of microstructure is likely abundant in the hippocampus; and (5) the angular richness of the diffusion MRI signal can be leveraged to improve delineation of the internal hippocampal circuitry. Overall, extant findings suggest that diffusion MRI offers a promising avenue for characterizing hippocampal microstructure.

Explaining slow seizure propagation with white matter tractography.

Epileptic seizures recorded with stereoelectroencephalography (SEEG) can take a fraction of a second or several seconds to propagate from one region to another. What explains such propagation patterns? We combine tractography and SEEG to determine the relationship between seizure propagation and the white matter architecture and to describe seizure propagation mechanisms. Patient-specific spatiotemporal seizure propagation maps were combined with tractography from diffusion imaging of matched subjects from the Human Connectome Project. The onset of seizure activity was marked on a channel-by-channel basis by two board-certified neurologists for all channels involved in the seizure. We measured the tract connectivity (number of tracts) between regions-of-interest pairs among the seizure onset zone, regions of seizure spread, and non-involved regions. We also investigated how tract-connected the seizure onset zone is to regions of early seizure spread compared to regions of late spread. Comparisons were made after correcting for differences in distance. Sixty-nine seizures were marked across 26 patients with drug-resistant epilepsy; 11 were seizure free after surgery (Engel IA) and 15 were not (Engel IB-IV). The seizure onset zone was more tract connected to regions of seizure spread than to non-involved regions (p<0.0001); however, regions of seizure spread were not differentially tract-connected to other regions of seizure spread compared to non-involved regions. In seizure free patients only, regions of seizure spread were more tract connected to the seizure onset zone than to other regions of spread (p<0.0001). Over the temporal evolution of a seizure, the seizure onset zone was significantly more tract connected to regions of early spread compared to regions of late spread in seizure free patients only (p<0.0001). By integrating information on structure, we demonstrate that seizure propagation is likely mediated by white matter tracts. The pattern of connectivity between seizure onset zone, regions of spread and non-involved regions demonstrates that the onset zone may be largely responsible for seizures propagating throughout the brain, rather than seizures propagating to intermediate points, from which further propagation takes place. Our findings also suggest that seizure propagation over seconds may be the result of a continuous bombardment of action potentials from the seizure onset zone to regions of spread. In non-seizure free patients, the paucity of tracts from the presumed seizure onset zone to regions of spread suggests that the onset zone was missed. Fully understanding the structure-propagation relationship may eventually provide insight into selecting the correct targets for epilepsy surgery.

Prenatal exposure to maternal social disadvantage and psychosocial stress and neonatal white matter connectivity at birth.

Early life adversity (social disadvantage and psychosocial stressors) is associated with altered microstructure in fronto-limbic pathways important for socioemotional development. Understanding when these associations begin to emerge may inform the timing and design of preventative interventions. In this longitudinal study, 399 mothers were oversampled for low income and completed social background measures during pregnancy. Measures were analyzed with structural equation analysis resulting in two latent factors: social disadvantage (education, insurance status, income-to-needs ratio [INR], neighborhood deprivation, and nutrition) and psychosocial stress (depression, stress, life events, and racial discrimination). At birth, 289 healthy term-born neonates underwent a diffusion MRI (dMRI) scan. Mean diffusivity (MD) and fractional anisotropy (FA) were measured for the dorsal and inferior cingulum bundle (CB), uncinate, and fornix using probabilistic tractography in FSL. Social disadvantage and psychosocial stress were fitted to dMRI parameters using regression models adjusted for infant postmenstrual age at scan and sex. Social disadvantage, but not psychosocial stress, was independently associated with lower MD in the bilateral inferior CB and left uncinate, right fornix, and lower MD and higher FA in the right dorsal CB. Results persisted after accounting for maternal medical morbidities and prenatal drug exposure. In moderation analysis, psychosocial stress was associated with lower MD in the left inferior CB among the lower-to-higher socioeconomic status (SES) (INR ≥ 200%) group, but not the extremely low SES (INR < 200%) group. Increasing access to social welfare programs that reduce the burden of social disadvantage and related psychosocial stressors may be an important target to protect fetal brain development in fronto-limbic pathways.

Spatiotemporal Developmental Gradient of Thalamic Morphology, Microstructure, and Connectivity fromthe Third Trimester to Early Infancy.

Thalamus is a critical component of the limbic system that is extensively involved in both basic and high-order brain functions. However, how the thalamic structure and function develops at macroscopic and microscopic scales during the perinatal period development is not yet well characterized. Here, we used multishell high-angular resolution diffusion MRI of 144 preterm-born and full-term infants in both sexes scanned at 32-44 postmenstrual weeks (PMWs) from the Developing Human Connectome Project database to investigate the thalamic development in morphology, microstructure, associated connectivity, and subnucleus division. We found evident anatomic expansion and linear increases of fiber integrity in the lateral side of thalamus compared with the medial part. The tractography results indicated that thalamic connection to the frontal cortex developed later than the other thalamocortical connections (parieto-occipital, motor, somatosensory, and temporal). Using a connectivity-based segmentation strategy, we revealed that functional partitions of thalamic subdivisions were formed at 32 PMWs or earlier, and the partition developed toward the adult pattern in a lateral-to-medial pattern. Collectively, these findings revealed faster development of the lateral thalamus than the central part as well as a posterior-to-anterior developmental gradient of thalamocortical connectivity from the third trimester to early infancy.SIGNIFICANCE STATEMENT This is the first study that characterizes the spatiotemporal developmental pattern of thalamus during the third trimester to early infancy. We found that thalamus develops in a lateral-to-medial pattern for both thalamic microstructures and subdivisions; and thalamocortical connectivity develops in a posterior-to-anterior gradient that thalamofrontal connectivity appears later than the other thalamocortical connections. These findings may enrich our understanding of the developmental principles of thalamus and provide references for the atypical brain growth in neurodevelopmental disorders.

High-Resolution Tractography Protocol to Investigate the Pathways between Human Mediodorsal Thalamic Nucleus and Prefrontal Cortex.

Animal studies have established that the mediodorsal nucleus (MD) of the thalamus is heavily and reciprocally connected with all areas of the prefrontal cortex (PFC). In humans, however, these connections are difficult to investigate. High-resolution imaging protocols capable of reliably tracing the axonal tracts linking the human MD with each of the PFC areas may thus be key to advance our understanding of the variation, development, and plastic changes of these important circuits, in health and disease. Here, we tested in adult female and male humans the reliability of a new reconstruction protocol based on in vivo diffusion MRI to trace, measure, and characterize the fiber tracts interconnecting the MD with 39 human PFC areas per hemisphere. Our protocol comprised the following three components: (1) defining regions of interest; (2) preprocessing diffusion data; and, (3) modeling white matter tracts and tractometry. This analysis revealed largely separate PFC territories of reciprocal MD-PFC tracts bearing striking resemblance with the topographic layout observed in macaque connection-tracing studies. We then examined whether our protocol could reliably reconstruct each of these MD-PFC tracts and their profiles across test and retest sessions. Results revealed that this protocol was able to trace and measure, in both left and right hemispheres, the trajectories of these 39 area-specific axon bundles with good-to-excellent test-retest reproducibility. This protocol, which has been made publicly available, may be relevant for cognitive neuroscience and clinical studies of normal and abnormal PFC function, development, and plasticity.SIGNIFICANCE STATEMENT Reciprocal MD-PFC interactions are critical for complex human cognition and learning. Reliably tracing, measuring and characterizing MD-PFC white matter tracts using high-resolution noninvasive methods is key to assess individual variation of these systems in humans. Here, we propose a high-resolution tractography protocol that reliably reconstructs 39 area-specific MD-PFC white matter tracts per hemisphere and quantifies structural information from diffusion MRI data. This protocol revealed a detailed mapping of thalamocortical and corticothalamic MD-PFC tracts in four different PFC territories (dorsal, medial, orbital/frontal pole, inferior frontal) showing structural connections resembling those observed in tracing studies with macaques. Furthermore, our automated protocol revealed high test-retest reproducibility and is made publicly available, constituting a step forward in mapping human MD-PFC circuits in clinical and academic research.

A unified filtering method for estimating asymmetric orientation distribution functions.

Numerous filtering methods have been proposed for estimating asymmetric orientation distribution functions (ODFs) for diffusion magnetic resonance imaging (dMRI). It can be hard to make sense of all these different methods, which share similar features and result in similar outputs. In this work, we disentangle these many filtering methods proposed in the past and combine them into a novel, unified filtering equation. We also propose a self-supervised data-driven approach for calibrating the filtering parameter values. Our equation is implemented in an open-source GPU-accelerated python software to facilitate its integration into any existing dMRI processing pipeline. Our method is applied on multi-shell multi-tissue fiber ODFs from the Human Connectome Project dataset (1.25 mm3 native resolution) and on single-shell single-tissue fiber ODFs from the Bilingualism and the Brain dataset (2.0 mm3 isotropic resolution) to evaluate the occurrence of asymmetric patterns on different spatial resolutions, representing cutting-edge and "clinical" research data. Asymmetry measures such as the asymmetric index (ASI) and our novel number of fiber directions (NuFiD) are then used to explain the behaviour of our method in these images. The contributions of this work are: (i) the disentanglement and unification of filtering methods for estimating asymmetric ODFs; (ii) a calibration method for automatically fixing the parameters governing the filtering; (iii) an open-source, efficient implementation of our unified filtering method for estimating asymmetric ODFs; (iv) a novel number of fiber directions (NuFiD) index for explaining asymmetric fiber configurations; and (v) a novel template of asymmetries, revealing that our filtering method estimates asymmetric configurations in at least 50% of the brain voxels (∼31% of the white matter and ∼63% of the gray matter).

Anatomically constrained tractography of the fetal brain.

Diffusion-weighted Magnetic Resonance Imaging (dMRI) is increasingly used to study the fetal brain in utero. An important computation enabled by dMRI is streamline tractography, which has unique applications such as tract-specific analysis of the brain white matter and structural connectivity assessment. However, due to the low fetal dMRI data quality and the challenging nature of tractography, existing methods tend to produce highly inaccurate results. They generate many false streamlines while failing to reconstruct the streamlines that constitute the major white matter tracts. In this paper, we advocate for anatomically constrained tractography based on an accurate segmentation of the fetal brain tissue directly in the dMRI space. We develop a deep learning method to compute the segmentation automatically. Experiments on independent test data show that this method can accurately segment the fetal brain tissue and drastically improve the tractography results. It enables the reconstruction of highly curved tracts such as optic radiations. Importantly, our method infers the tissue segmentation and streamline propagation direction from a diffusion tensor fit to the dMRI data, making it applicable to routine fetal dMRI scans. The proposed method can facilitate the study of fetal brain white matter tracts with dMRI.

The connectivity-based architecture of the human piriform cortex.

The anatomy of the human piriform cortex (PC) is poorly understood. We used a bimodal connectivity-based-parcellation approach to investigate subregions of the PC and its connectional differentiation from the amygdala. One hundred (55 % female) genetically unrelated subjects from the Human Connectome Project were included. A region of interest (ROI) was delineated bilaterally covering PC and amygdala, and functional and structural connectivity of this ROI with the whole gray matter was computed. Spectral clustering was performed to obtain bilateral parcellations at granularities of k = 2-10 clusters and combined bimodal parcellations were computed. Validity of parcellations was assessed via their mean individual-to-group similarity per adjusted rand index (ARI). Individual-to-group similarity was higher than chance in both modalities and in all clustering solutions. The amygdala was clearly distinguished from PC in structural parcellations, and olfactory amygdala was connectionally more similar to amygdala than to PC. At higher granularities, an anterior and ventrotemporal and a posterior frontal cluster emerged within PC, as well as an additional temporal cluster at their boundary. Functional parcellations also showed a frontal piriform cluster, and similar temporal clusters were observed with less consistency. Results from bimodal parcellations were similar to the structural parcellations. Consistent results were obtained in a validation cohort. Distinction of the human PC from the amygdala, including its olfactory subregions, is possible based on its structural connectivity alone. The canonical fronto-temporal boundary within PC was reproduced in both modalities and with consistency. All obtained parcellations are freely available.

Developing an AI-empowered head-only ultra-high-performance gradient MRI system for high spatiotemporal neuroimaging.

Recent advances in neuroscience requires high-resolution MRI to decipher the structural and functional details of the brain. Developing a high-performance gradient system is an ongoing effort in the field to facilitate high spatial and temporal encoding. Here, we proposed a head-only gradient system NeuroFrontier, dedicated for neuroimaging with an ultra-high gradient strength of 650 mT/m and 600 T/m/s. The proposed system features in 1) ultra-high power of 7MW achieved by running two gradient power amplifiers using a novel paralleling method; 2) a force/torque balanced gradient coil design with a two-step mechanical structure that allows high-efficiency and flexible optimization of the peripheral nerve stimulation; 3) a high-density integrated RF system that is miniaturized and customized for the head-only system; 4) an AI-empowered compressed sensing technique that enables ultra-fast acquisition of high-resolution images and AI-based acceleration in q-t space for diffusion MRI (dMRI); and 5) a prospective head motion correction technique that effectively corrects motion artifacts in real-time with 3D optical tracking. We demonstrated the potential advantages of the proposed system in imaging resolution, speed, and signal-to-noise ratio for 3D structural MRI (sMRI), functional MRI (fMRI) and dMRI in neuroscience applications of submillimeter layer-specific fMRI and dMRI. We also illustrated the unique strength of this system for dMRI-based microstructural mapping, e.g., enhanced lesion contrast at short diffusion-times or high b-values, and improved estimation accuracy for cellular microstructures using diffusion-time-dependent dMRI or for neurite microstructures using q-space approaches.

Arterial pulsation dependence of perivascular cerebrospinal fluid flow measured by dynamic diffusion tensor imaging in the human brain.

Perivascular cerebrospinal fluid (pCSF) flow is a key component of the glymphatic system. Arterial pulsation has been proposed as the main driving force of pCSF influx along the superficial and penetrating arteries; however, evidence of this mechanism in humans is limited. We proposed an experimental framework of dynamic diffusion tensor imaging with low b-values and ultra-long echo time (dynDTIlow-b) to capture pCSF flow properties during the cardiac cycle in human brains. Healthy adult volunteers (aged 17-28 years; seven men, one woman) underwent dynDTIlow-b using a 3T scanner (MAGNETOM Prisma, Siemens Healthcare, Erlangen, Germany) with simultaneously recorded cardiac output. The results showed that diffusion tensors reconstructed from pCSF were mainly oriented in the direction of the neighboring arterial flow. When switching from vasoconstriction to vasodilation, the axial and radial diffusivities of the pCSF increased by 5.7 % and 4.94 %, respectively, suggesting that arterial pulsation alters the pCSF flow both parallel and perpendicular to the arterial wall. DynDTIlow-b signal intensity at b=0 s/mm2 (i.e., T2-weighted, [S(b=0 s/mm2)]) decreased in systole, but this change was ∼7.5 % of a cardiac cycle slower than the changes in apparent diffusivity, suggesting that changes in S(b=0 s/mm2) and apparent diffusivity arise from distinct physiological processes and potential biomarkers associated with perivascular space volume and pCSF flow, respectively. Additionally, the mean diffusivities of white matter showed cardiac-cycle dependencies similar to pCSF, although a delay relative to the peak time of apparent diffusivity in pCSF was present, suggesting that dynDTIlow-b could potentially reveal the dynamics of magnetic resonance imaging-invisible pCSF surrounding small arteries and arterioles in white matter; this delay may result from pulse wave propagation along penetrating arteries. In conclusion, the vasodilation-induced increases in axial and radial diffusivities of pCSF and mean diffusivities of white matter are consistent with the notion that arterial pulsation can accelerate pCSF flow in human brain. Furthermore, the proposed dynDTIlow-b technique can capture various pCSF dynamics in artery pulsation.

Bundle-specific tractogram distribution estimation using higher-order streamline differential equation.

Streamline tractography locally traces peak directions extracted from fiber orientation distribution (FOD) functions, lacking global information about the trend of the whole fiber bundle. Therefore, it is prone to producing erroneous tracks while missing true positive connections. In this work, we propose a new bundle-specific tractography (BST) method based on a bundle-specific tractogram distribution (BTD) function, which directly reconstructs the fiber trajectory from the start region to the termination region by incorporating the global information in the fiber bundle mask. A unified framework for any higher-order streamline differential equation is presented to describe the fiber bundles with disjoint streamlines defined based on the diffusion vectorial field. At the global level, the tractography process is simplified as the estimation of BTD coefficients by minimizing the energy optimization model, and is used to characterize the relations between BTD and diffusion tensor vector under the prior guidance by introducing the tractogram bundle information to provide anatomic priors. Experiments are performed on simulated Hough, Sine, Circle data, ISMRM 2015 Tractography Challenge data, FiberCup data, and in vivo data from the Human Connectome Project (HCP) for qualitative and quantitative evaluation. Results demonstrate that our approach reconstructs complex fiber geometry more accurately. BTD reduces the error deviation and accumulation at the local level and shows better results in reconstructing long-range, twisting, and large fanning tracts.

Divergent functional connectivity changes associated with white matter hyperintensities.

Age-related white matter hyperintensities are a common feature and are known to be negatively associated with structural integrity, functional connectivity, and cognitive performance. However, this has yet to be fully understood mechanistically. We analyzed multiple MRI modalities acquired in 465 non-demented individuals from the Swedish BioFINDER study including 334 cognitively normal and 131 participants with mild cognitive impairment. White matter hyperintensities were automatically quantified using fluid-attenuated inversion recovery MRI and parameters from diffusion tensor imaging were estimated in major white matter fibre tracts. We calculated fMRI resting state-derived functional connectivity within and between predefined cortical regions structurally linked by the white matter tracts. How change in functional connectivity is affected by white matter lesions and related to cognition (in the form of executive function and processing speed) was explored. We examined the functional changes using a measure of sample entropy. As expected hyperintensities were associated with disrupted structural white matter integrity and were linked to reduced functional interregional lobar connectivity, which was related to decreased processing speed and executive function. Simultaneously, hyperintensities were also associated with increased intraregional functional connectivity, but only within the frontal lobe. This phenomenon was also associated with reduced cognitive performance. The increased connectivity was linked to increased entropy (reduced predictability and increased complexity) of the involved voxels' blood oxygenation level-dependent signal. Our findings expand our previous understanding of the impact of white matter hyperintensities on cognition by indicating novel mechanisms that may be important beyond this particular type of brain lesions.

Fast and robust quantification of uncertainty in non-linear diffusion MRI models.

Diffusion MRI (dMRI) allows for non-invasive investigation of brain tissue microstructure. By fitting a model to the dMRI signal, various quantitative measures can be derived from the data, such as fractional anisotropy, neurite density and axonal radii maps. We investigate the Fisher Information Matrix (FIM) and uncertainty propagation as a generally applicable method for quantifying the parameter uncertainties in linear and non-linear diffusion MRI models. In direct comparison with Markov Chain Monte Carlo (MCMC) sampling, the FIM produces similar uncertainty estimates at much lower computational cost. Using acquired and simulated data, we then list several characteristics that influence the parameter variances, including data complexity and signal-to-noise ratio. For practical purposes we investigate a possible use of uncertainty estimates in decreasing intra-group variance in group statistics by uncertainty-weighted group estimates. This has potential use cases for detection and suppression of imaging artifacts.

Associations of quantitative susceptibility mapping with cortical atrophy and brain connectome in Alzheimer's disease: A multi-parametric study.

Aberrant susceptibility due to iron level abnormality and brain network disconnections are observed in Alzheimer's disease (AD), with disrupted iron homeostasis hypothesized to be linked to AD pathology and neuronal loss. However, whether associations exist between abnormal quantitative susceptibility mapping (QSM), brain atrophy, and altered brain connectome in AD remains unclear. Based on multi-parametric brain imaging data from 30 AD patients and 26 healthy controls enrolled at the China-Japan Friendship Hospital, we investigated the abnormality of the QSM signal and volumetric measure across 246 brain regions in AD patients. The structural and functional connectomes were constructed based on diffusion MRI tractography and functional connectivity, respectively. The network topology was quantified using graph theory analyses. We identified seven brain regions with both reduced cortical thickness and abnormal QSM (p < 0.05) in AD, including the right superior frontal gyrus, left superior temporal gyrus, right fusiform gyrus, left superior parietal lobule, right superior parietal lobule, left inferior parietal lobule, and left precuneus. Correlations between cortical thickness and network topology computed across patients in the AD group resulted in statistically significant correlations in five of these regions, with higher correlations in functional compared to structural topology. We computed the correlation between network topological metrics, QSM value and cortical thickness across regions at both individual and group-averaged levels, resulting in a measure we call spatial correlations. We found a decrease in the spatial correlation of QSM and the global efficiency of the structural network in AD patients at the individual level. These findings may provide insights into the complex relationships among QSM, brain atrophy, and brain connectome in AD.

High-fidelity intravoxel incoherent motion parameter mapping using locally low-rank and subspace modeling.

PURPOSE: Intravoxel incoherent motion (IVIM) is a quantitative magnetic resonance imaging (MRI) method used to quantify perfusion properties of tissue non-invasively without contrast. However, clinical applications are limited by unreliable parameter estimates, particularly for the perfusion fraction (f) and pseudodiffusion coefficient (D*). This study aims to develop a high-fidelity reconstruction for reliable estimation of IVIM parameters. The proposed method is versatile and amenable to various acquisition schemes and fitting methods. METHODS: To address current challenges with IVIM, we adapted several advanced reconstruction techniques. We used a low-rank approximation of IVIM images and temporal subspace modeling to constrain the magnetization dynamics of the bi-exponential diffusion signal decay. In addition, motion-induced phase variations were corrected between diffusion directions and b-values, facilitating the use of high SNR real-valued diffusion data. The proposed method was evaluated in simulations and in vivo brain acquisitions in six healthy subjects and six individuals with a history of SARS-CoV-2 infection and compared with the conventionally reconstructed magnitude data. Following reconstruction, IVIM parameters were estimated voxel-wise. RESULTS: Our proposed method reduced noise contamination in simulations, resulting in a 60%, 58.9%, and 83.9% reduction in the NRMSE for D, f, and D*, respectively, compared to the conventional reconstruction. In vivo, anisotropic properties of D, f, and D* were preserved with the proposed method, highlighting microvascular differences in gray matter between individuals with a history of COVID-19 and those without (p = 0.0210), which wasn't observed with the conventional reconstruction. CONCLUSION: The proposed method yielded a more reliable estimation of IVIM parameters with less noise than the conventional reconstruction. Further, the proposed method preserved anisotropic properties of IVIM parameter estimates and demonstrated differences in microvascular perfusion in COVID-affected subjects, which weren't observed with conventional reconstruction methods.

Altered thalamocortical tract trajectory growth with undisrupted thalamic parcellation pattern in human lissencephaly brain at mid-gestational stage.

Proper topographically organized neural connections between the thalamus and the cerebral cortex are mandatory for thalamus function. Thalamocortical (TC) fiber growth begins during the embryonic period and completes by the third trimester of gestation, so that human neonates at birth have a thalamus with a near-facsimile of adult functional parcellation. Whether congenital neocortical anomaly (e.g., lissencephaly) affects TC connection in humans is unknown. Here, via diffusion MRI fiber-tractography analysis of long-term formalin-fixed postmortem fetal brain diagnosed as lissencephaly in comparison with an age-matched normal one, we found similar topological patterns of thalamic subregions and of internal capsule parcellated by TC fibers. However, lissencephaly fetal brain showed white matter structural changes, including fewer/less organized TC fibers and optic radiations, and much less cortical plate invasion by TC fibers - particularly around the shallow central sulcus. Diffusion MRI fiber tractography of normal fetal brains at 15, 23, and 26 gestational weeks (GW) revealed dynamic volumetric change of each parcellated thalamic subregion, suggesting coupled developmental progress of the thalamus with the corresponding cortex. Moreover, from GW23 and GW26 normal fetal brains, TC endings in the cortical plate could be delineated to reflect cumulative progressive TC invasion of cortical plate. By contrast, lissencephaly brain showed a dramatic decrease in TC invasion of the cortical plate. Our study thus shows the feasibility of diffusion MRI fiber tractography in postmortem long-term formalin-fixed fetal brains to disclose the developmental progress of TC tracts coordinating with thalamic and neocortical growth both in normal and lissencephaly fetal brains at mid-gestational stage.

Diffusion MRI harmonization via personalized template mapping.

One fundamental challenge in diffusion magnetic resonance imaging (dMRI) harmonization is to disentangle the contributions of scanner-related effects from the variable brain anatomy for the observed imaging signals. Conventional harmonization methods rely on establishing an atlas space to resolve anatomical variability and generate a unified inter-site mapping function. However, this approach is limited in accounting for the misalignment of neuroanatomy that still widely persists even after registration, especially in regions close to cortical boundaries. To overcome this challenge, we propose a personalized framework in this paper to more effectively address the confounding from the misalignment of neuroanatomy in dMRI harmonization. Instead of using a common template representing site-effects for all subjects, the main novelty of our method is the adaptive computation of personalized templates for both source and target scanning sites to estimate the inter-site mapping function. We integrate our method with the rotation invariant spherical harmonics (RISH) features to achieve the harmonization of dMRI signals between sites. In our experiments, the proposed approach is applied to harmonize the dMRI data acquired from two scanning platforms: Siemens Prisma and GE MR750 from the Adolescent Brain Cognitive Development dataset and compared with a state-of-the-art method based on RISH features. Our results indicate that the proposed harmonization framework achieves superior performance not only in reducing inter-site variations due to scanner differences but also in preserving sex-related biological variability in original cohorts. Moreover, we assess the impact of harmonization on the estimation of fiber orientation distributions and show the robustness of the personalized harmonization procedure in preserving the fiber orientation of original dMRI signals.