High angular resolution diffusion imaging (HARDI) of porcine menisci: a comparison of diffusion tensor imaging and generalized q-sampling imaging.

Background: Diffusion magnetic resonance imaging (MRI) allows for the quantification of water diffusion properties in soft tissues. The goal of this study was to characterize the 3D collagen fiber network in the porcine meniscus using high angular resolution diffusion imaging (HARDI) acquisition with both diffusion tensor imaging (DTI) and generalized q-sampling imaging (GQI). Methods: Porcine menisci (n=7) were scanned ex vivo using a three-dimensional (3D) HARDI spin-echo pulse sequence with an isotropic resolution of 500 µm at 7.0 Tesla. Both DTI and GQI reconstruction techniques were used to quantify the collagen fiber alignment and visualize the complex collagen network of the meniscus. The MRI findings were validated with conventional histology. Results: DTI and GQI exhibited distinct fiber orientation maps in the meniscus using the same HARDI acquisition. We found that crossing fibers were only resolved with GQI, demonstrating the advantage of GQI over DTI to visualize the complex collagen fiber orientation in the meniscus. Furthermore, the MRI findings were consistent with conventional histology. Conclusions: HARDI acquisition with GQI reconstruction more accurately resolves the complex 3D collagen architecture of the meniscus compared to DTI reconstruction. In the future, these technologies have the potential to nondestructively assess both normal and abnormal meniscal structure.

Dynamic monitoring of radiation-induced white matter microstructure injury in nasopharyngeal carcinoma via high-angular resolution diffusion imaging.

PURPOSE: To investigate white matter microstructural abnormalities caused by radiotherapy in nasopharyngeal carcinoma (NPC) patients using MRI high-angular resolution diffusion imaging (HARDI). METHODS: We included 127 patients with pathologically confirmed NPC: 36 in the pre-radiotherapy group, 29 in the acute response period (post-RT-AP), 23 in the early delayed period (post-RT-ED) group, and 39 in the late-delayed period (post-RT-LD) group. HARDI data were acquired for each patient, and dispersion parameters were calculated to compare the differences in specific fibre bundles among the groups. The Montreal Neurocognitive Assessment (MoCA) was used to evaluate neurocognitive function, and the correlations between dispersion parameters and MoCA were analysed. RESULTS: In the right cingulum frontal parietal bundles, the fractional anisotropy value decreased to the lowest level post-RT-AP and then reversed and increased post-RT-ED and post-RT-LD. The mean, axial, and radial diffusivity were significantly increased in the post-RT-AP (p < 0.05) and decreased in the post-RT-ED and post-RT-LD groups to varying degrees. MoCA scores were decreased post-radiotherapy than those before radiotherapy (p = 0.005). MoCA and mean diffusivity exhibited a mild correlation in the left cingulum frontal parahippocampal bundle. CONCLUSIONS: White matter tract changes detected by HARDI are potential biomarkers for monitoring radiotherapy-related brain damage in NPC patients.

High-angular resolution diffusion imaging generation using 3d u-net.

PURPOSE: To investigate the effects on tractography of artificial intelligence-based prediction of motion-probing gradients (MPGs) in diffusion-weighted imaging (DWI). METHODS: The 251 participants in this study were patients with brain tumors or epileptic seizures who underwent MRI to depict tractography. DWI was performed with 64 MPG directions and b = 0 s/mm2 images. The dataset was divided into a training set of 191 (mean age 45.7 [± 19.1] years), a validation set of 30 (mean age 41.6 [± 19.1] years), and a test set of 30 (mean age 49.6 [± 18.3] years) patients. Supervised training of a convolutional neural network was performed using b = 0 images and the first 32 axes of MPG images as the input data and the second 32 axes as the reference data. The trained model was applied to the test data, and tractography was performed using (a) input data only; (b) input plus prediction data; and (c) b = 0 images and the 64 MPG data (as a reference). RESULTS: In Q-ball imaging tractography, the average dice similarity coefficient (DSC) of the input plus prediction data was 0.715 (± 0.064), which was significantly higher than that of the input data alone (0.697 [± 0.070]) (p < 0.05). In generalized q-sampling imaging tractography, the average DSC of the input plus prediction data was 0.769 (± 0.091), which was also significantly higher than that of the input data alone (0.738 [± 0.118]) (p < 0.01). CONCLUSION: Diffusion tractography is improved by adding predicted MPG images generated by an artificial intelligence model.

Neural network algorithms predict new diffusion MRI data for multi-compartmental analysis of brain microstructure in a clinical setting.

High angular resolution diffusion imaging (HARDI) is a promising method for advanced analysis of brain microstructure. However, comprehensive HARDI analysis requires multiple acquisitions of diffusion images (multi-shell HARDI), which is time consuming and often impractical in clinical settings. This study aimed to establish neural network models that can predict new diffusion datasets from clinically feasible brain diffusion MRI for multi-shell HARDI. The development included 2 algorithms: multi-layer perceptron (MLP) and convolutional neural network (CNN). Both followed a voxel-based approach for model training (70%), validation (15%), and testing (15%). The investigations involved 2 multi-shell HARDI datasets: 1) 11 healthy subjects from the Human Connectome Project (HCP); and 2) 10 local subjects with multiple sclerosis (MS). To assess outcomes, we conducted neurite orientation dispersion and density imaging using both predicted and original data and compared their orientation dispersion index (ODI) and neurite density index (NDI) in different brain tissues with 2 measures: peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Results showed that both models achieved robust predictions, which provided competitive ODI and NDI, especially in brain white matter. The CNN outperformed MLP with the HCP data on both PSNR (p < 0.001) and SSIM (p < 0.01). With the MS data, the models performed similarly. Overall, the optimized neural networks can help generate non-acquired brain diffusion MRI, which will make advanced HARDI analysis possible in clinical practice following further validation. Enabling detailed characterization of brain microstructure will allow enhanced understanding of brain function in both health and disease.

Mapping white matter structural and network alterations in betel quid-dependent chewers using high angular resolution diffusion imaging.

Background: To evaluate brain white matter diffusion characteristics and anatomical network alterations in betel quid dependence (BQD) chewers using high angular resolution diffusion imaging (HARDI). Methods: The current study recruited 53 BQD chewers and 37 healthy controls (HC) in two groups. We explored regional diffusion metrics alternations in the BQD group compared with the HC group using automated fiber quantification (AFQ). We further employed the white matter (WM) anatomical network of HARDI to explore connectivity alterations in BQD chewers using graph theory. Results: BQD chewers presented significantly lower FA values in the left and right cingulum cingulate, the left and right thalamic radiation, and the right uncinate. The BQD has a significantly higher RD value in the right uncinate fasciculus than the HC group. At the global WM anatomical network level, global network efficiency (p = 0.008) was poorer and Lp (p = 0.016) was greater in the BQD group. At the nodal WM anatomical network level, nodal efficiency (p < 0.05) was lower in the BQD group. Conclusion: Our findings provide novel morphometric evidence that brain structural changes in BQD are characterized by white matter diffusivity and anatomical network connectivity among regions of the brain, potentially leading to the enhanced reward system and impaired inhibitory control.

Advanced diffusion MRI and image texture analysis detect widespread brain structural differences between relapsing-remitting and secondary progressive multiple sclerosis.

Introduction: Disease development in multiple sclerosis (MS) causes dramatic structural changes, but the exact changing patterns are unclear. Our objective is to investigate the differences in brain structure locally and spatially between relapsing-remitting MS (RRMS) and its advanced form, secondary progressive MS (SPMS), through advanced analysis of diffusion magnetic resonance imaging (MRI) and image texture. Methods: A total of 20 patients with RRMS and nine patients with SPMS from two datasets underwent 3T anatomical and diffusion tensor imaging (DTI). The DTI was harmonized, augmented, and then modeled, which generated six voxel- and sub-voxel-scale measures. Texture analysis focused on T2 and FLAIR MRI, which produced two phase-based measures, namely, phase congruency and weighted mean phase. Data analysis was 3-fold, i.e., histogram analysis of whole-brain normal appearing white matter (NAWM); region of interest (ROI) analysis of NAWM and lesions within three critical white matter tracts, namely, corpus callosum, corticospinal tract, and optic radiation; and along-tract statistics. Furthermore, by calculating the z-score of core-rim pathology within lesions based on diffusion measures, we developed a novel method to define chronic active lesions and compared them between cohorts. Results: Histogram features from diffusion and all but one texture measure differentiated between RRMS and SPMS. Within-tract ROI analysis detected cohort differences in both NAWM and lesions of the corpus callosum body in three measures of neurite orientation and anisotropy. Along-tract statistics detected cohort differences from multiple measures, particularly lesion extent, which increased significantly in SPMS in posterior corpus callosum and optic radiations. The number of chronic active lesions were also significantly higher (by 5-20% over z-scores 0.5 and 1.0) in SPMS than RRMS based on diffusion anisotropy, neurite content, and diameter. Conclusion: Advanced diffusion MRI and texture analysis may be promising approaches for thorough understanding of brain structural changes from RRMS to SPMS, thereby providing new insight into disease development mechanisms in MS.

Using Advanced Diffusion-Weighted Imaging to Predict Cell Counts in Gray Matter: Potential and Pitfalls.

Recent advances in diffusion imaging have given it the potential to non-invasively detect explicit neurobiological properties, beyond what was previously possible with conventional structural imaging. However, there is very little known about what cytoarchitectural properties these metrics, especially those derived from newer multi-shell models like Neurite Orientation Dispersion and Density Imaging (NODDI) correspond to. While these diffusion metrics do not promise any inherent cell type specificity, different brain cells have varying morphologies, which could influence the diffusion signal in distinct ways. This relationship is currently not well-characterized. Understanding the possible cytoarchitectural signatures of diffusion measures could allow them to estimate important neurobiological properties like cell counts, potentially resulting in a powerful clinical diagnostic tool. Here, using advanced diffusion imaging (NODDI) in the mouse brain, we demonstrate that different regions have unique relationships between cell counts and diffusion metrics. We take advantage of this exclusivity to introduce a framework to predict cell counts of different types of cells from the diffusion metrics alone, in a region-specific manner. We also outline the challenges of reliably developing such a model and discuss the precautions the field must take when trying to tie together medical imaging modalities and histology.

General orientation transform for the estimation of fiber orientations in white matter tissues.

PURPOSE: The orientation distribution function (ODF), which is obtained from the radial integral of the probability density function weighted by rn$$ {r}^n $$ ( r$$ r $$ is the radial length), has been used to estimate fiber orientations of white matter tissues. Currently, there is no general expression of the ODF that is suitable for any n value in the HARDI methods. THEORY AND METHODS: A novel methodology is proposed to calculate the ODF for any n>-1$$ n>-1 $$ through the Taylor series expansion and a generalized expression for -1

Delineating the Decussating Dentato-rubro-thalamic Tract and Its Connections in Humans Using Diffusion Spectrum Imaging Techniques.

The objective of this study was to identify the decussating dentato-rubro-thalamic tract (d-DRTT) and its afferent and efferent connections in healthy humans using diffusion spectrum imaging (DSI) techniques. In the present study, the trajectory and lateralization of the d-DRTT was explored using data from subjects in the Massachusetts General Hospital-Human Connectome Project adult diffusion dataset. The afferent and efferent networks that compose the cerebello-thalamo-cerebral pathways were also reconstructed. Correlation analysis was performed to identify interrelationships between subdivisions of the cerebello-dentato-rubro-thalamic and thalamo-cerebral connections. The d-DRTT was visualized bilaterally in 28 subjects. According to a normalized quantitative anisotropy and lateralization index evaluation, the left and right d-DRTT were relatively symmetric. Afferent regions were found mainly in the posterior cerebellum, especially the entire lobule VII (crus I, II and VIIb). Efferent fibers mainly are projected to the contralateral frontal cortex, including the motor and nonmotor regions. Correlations between cerebello-thalamic connections and thalamo-cerebral connections were positive, including the lobule VIIa (crus I and II) to the medial prefrontal cortex (MPFC) and the dorsolateral prefrontal cortex and lobules VI, VIIb, VIII, and IX, to the MPFC and motor and premotor areas. These results provide DSI-based tratographic evidence showing segregated and parallel cerebellar outputs to cerebral regions. The posterior cerebellum may play an important role in supporting and handling cognitive activities through d-DRTT. Future studies will allow for a more comprehensive understanding of cerebello-cerebral connections.

Advanced Analysis of Diffusion Tensor Imaging Along With Machine Learning Provides New Sensitive Measures of Tissue Pathology and Intra-Lesion Activity in Multiple Sclerosis.

Tissue pathology in multiple sclerosis (MS) is highly complex, requiring multi-dimensional analysis. In this study, our goal was to test the feasibility of obtaining high angular resolution diffusion imaging (HARDI) metrics through single-shell modeling of diffusion tensor imaging (DTI) data, and investigate how advanced measures from single-shell HARDI and DTI tractography perform relative to classical DTI metrics in assessing MS pathology. We examined 52 relapsing-remitting MS patients who had 3T anatomical brain MRI and DTI. Single-shell HARDI modeling yielded 5 sub-voxel-based metrics, totalling 11 diffusion measures including 4 DTI and 2 tractography metrics. Based on machine learning of 3-dimensional regions of interest, we evaluated the importance of the measures through several tissue classification tasks. These included two within-subject comparisons: lesion versus normal appearing white matter (NAWM); and lesion core versus shell. Further, by stratifying patients as having high (above 75% ile ) and low (below 25% ile ) number of MS lesions, we also performed 2 classifications between subjects for lesions and NAWM respectively. Results showed that in lesion-NAWM analysis, HARDI orientation distribution function (ODF) energy, DTI fractional anisotropy (FA), and HARDI orientation dispersion index were the top three metrics, which together achieved 65.2% accuracy and 0.71 area under the receiver operating characteristic curve (AUROC). In core-shell analysis, DTI mean diffusivity (MD), radial diffusivity, and FA were the top three metrics, and MD dominated the classification, which achieved 59.3% accuracy and 0.59 AUROC alone. Between patients, FA was the leading feature in lesion comparisons, while ODF energy was the best in NAWM separation. Collectively, single-shell modeling of common diffusion data can provide robust orientation measures of lesion and NAWM pathology, and DTI metrics are most sensitive to intra-lesion abnormality. Combined analysis of both advanced and classical diffusion measures may be critical for improved understanding of MS pathology.

Diffusion coefficient orientation distribution function for diffusion magnetic resonance imaging.

BACKGROUND: Diffusion magnetic resonance imaging (dMRI) is a popular non-invasive imaging technique applied for the study of nerve fibers in vivo, with diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI) as the commonly used dMRI methods. However, DTI cannot resolve complex fiber orientations in a local area and HARDI lacks a solid physical basis. NEW METHOD: We introduce a diffusion coefficient orientation distribution function (DCODF). It has a clear physical meaning to represent the orientation distribution of diffusion coefficients for Gaussian and non-Gaussian diffusion. Based on DCODF, we then propose a new HARDI method, termed as diffusion coefficient orientation distribution transform (DCODT), to estimate the orientation distribution of nerve fibers in voxels. RESULTS: The method is verified on the simulated data, ISMRM-2015-Tracto-challenge data, and HCP datasets. The results show the superior capability of DCODT in resolving the complex distribution of multiple fiber bundles effectively. COMPARISON WITH EXISTING METHOD(S): The method is compared to other common model-free HARDI estimators. In the numerical simulations, DCODT achieves a better trade-off between the resolution and accuracy than the counterparts for high b-values. In the comparisons based on the challenge data, the improvement of DCODT is significant in scoring. The results on the HCP datasets show that DCODT provides fewer spurious lobes in the glyphs, resulting in more coherent fiber orientations. CONCLUSIONS: We conclude that DCODT may be a reliable method to extract accurate information about fiber orientations from dMRI data and promising for the study of neural architecture.

Quantitative analyses of high-angular resolution diffusion imaging (HARDI)-derived long association fibers in children with sensorineural hearing loss.

Sensorineural hearing loss (SNHL) is the most common developmental sensory disorder due to a loss of function within the inner ear or its connections to the brain. While successful intervention for auditory deprivation with hearing amplification and cochlear implants during a sensitive early developmental period can improve spoken-language outcomes, SNHL patients can suffer several cognitive dysfunctions including executive function deficits, visual cognitive impairment, and abnormal visual dominance in speaking perception even after successful intervention. To evaluate whether long association fibers are involved in the pathogenesis of impairment on the extra-auditory cognitive process in SNHL participants, we quantitatively analyzed high-angular resolution diffusion imaging (HARDI) tractography-derived fibers in participants with SNHL. After excluding cases with congenital disorders, perinatal brain damage, or premature birth, we enrolled 17 participants with SNHL aged under 10 years old. Callosal pathways (CP) and six types of cortico-cortical association fibers (arcuate fasciculus [AF], inferior longitudinal fasciculus [ILF], inferior fronto-occipital fasciculus [IFOF], uncinate fasciculus [UF], cingulum fasciculus [CF], and fornix [Fx]) in both hemispheres were identified and visualized. The ILF and IFOF were partly undetected in three profound SNHL participants. Compared to age- and gender-matched neurotypical controls (NC), decreased volumes, increased lengths, and high apparent diffusion coefficient (ADC) values without difference in fractional anisotropy (FA) values were identified in multiple types of fibers in the SNHL group. The impairment of long association fibers in SNHL may partly be related to the association of cognitive dysfunction with SNHL.

Longitudinal Disconnection Tractograms to Investigate the Functional Consequences of White Matter Damage: An Automated Pipeline.

BACKGROUND AND PURPOSE: Neurosurgical resection is one of the few opportunities researchers have to image the human brain pre- and postfocal damage. A major challenge associated with brains undergoing surgical resection is that they often do not fit brain templates most image-processing methodologies are based on. Manual intervention is required to reconcile the pathology, requiring time investment and introducing reproducibility concerns, and extreme cases must be excluded. METHODS: We propose an automatic longitudinal pipeline based on High Angular Resolution Diffusion Imaging acquisitions to facilitate a Pathway Lesion Symptom Mapping analysis relating focal white matter injury to functional deficits. This two-part approach includes (i) automatic segmentation of focal white matter injury from anisotropic power differences, and (ii) modeling disconnection using tractography on the single-subject level, which specifically identifies the disconnections associated with focal white matter damage. RESULTS: The advantages of this approach stem from (1) objective and automatic lesion segmentation and tractogram generation, (2) objective and precise segmentation of affected tissue likely to be associated with damage to long-range white matter pathways (defined by anisotropic power), (3) good performance even in the cases of anatomical distortions by use of nonlinear tensor-based registration, which aligns images using an approach sensitive to white matter microstructure. CONCLUSIONS: Mapping a system as variable and complex as the human brain requires sample sizes much larger than the current technology can support. This pipeline can be used to execute large-scale, sufficiently powered analyses by meeting the need for an automatic approach to objectively quantify white matter disconnection.

fMRI-Targeted High-Angular Resolution Diffusion MR Tractography to Identify Functional Language Tracts in Healthy Controls and Glioma Patients.

BACKGROUND: MR Tractography enables non-invasive preoperative depiction of language subcortical tracts, which is crucial for the presurgical work-up of brain tumors; however, it cannot evaluate the exact function of the fibers. PURPOSE: A systematic pipeline was developed to combine tractography reconstruction of language fiber bundles, based on anatomical landmarks (Anatomical-T), with language fMRI cortical activations. A fMRI-targeted Tractography (fMRI-T) was thus obtained, depicting the subsets of the anatomical tracts whose endpoints are located inside a fMRI activation. We hypothesized that fMRI-T could provide additional functional information regarding the subcortical structures, better reflecting the eloquent white matter structures identified intraoperatively. METHODS: Both Anatomical-T and fMRI-T of language fiber tracts were performed on 16 controls and preoperatively on 16 patients with left-hemisphere brain tumors, using a q-ball residual bootstrap algorithm based on High Angular Resolution Diffusion Imaging (HARDI) datasets (b = 3000 s/mm2; 60 directions); fMRI ROIs were obtained using picture naming, verbal fluency, and auditory verb generation tasks. In healthy controls, normalized MNI atlases of fMRI-T and Anatomical-T were obtained. In patients, the surgical resection of the tumor was pursued by identifying eloquent structures with intraoperative direct electrical stimulation mapping and extending surgery to the functional boundaries. Post-surgical MRI allowed to identify Anatomical-T and fMRI-T non-eloquent portions removed during the procedure. RESULTS: MNI Atlases showed that fMRI-T is a subset of Anatomical-T, and that different task-specific fMRI-T involve both shared subsets and task-specific subsets - e.g., verbal fluency fMRI-T strongly involves dorsal frontal tracts, consistently with the phonogical-articulatory features of this task. A quantitative analysis in patients revealed that Anatomical-T removed portions of AF-SLF and IFOF were significantly greater than verbal fluency fMRI-T ones, suggesting that fMRI-T is a more specific approach. In addition, qualitative analyses showed that fMRI-T AF-SLF and IFOF predict the exact functional limits of resection with increased specificity when compared to Anatomical-T counterparts, especially the superior frontal portion of IFOF, in a subcohort of patients. CONCLUSION: These results suggest that performing fMRI-T in addition to the 'classic' Anatomical-T may be useful in a preoperative setting to identify the 'high-risk subsets' that should be spared during the surgical procedure.

Tractography and the connectome in neurosurgical treatment of gliomas: the premise, the progress, and the potential.

The ability of diffusion tensor MRI to detect the preferential diffusion of water in cerebral white matter tracts enables neurosurgeons to noninvasively visualize the relationship of lesions to functional neural pathways. Although viewed as a research tool in its infancy, diffusion tractography has evolved into a neurosurgical tool with applications in glioma surgery that are enhanced by evolutions in crossing fiber visualization, edema correction, and automated tract identification. In this paper the current literature supporting the use of tractography in brain tumor surgery is summarized, highlighting important clinical studies on the application of diffusion tensor imaging (DTI) for preoperative planning of glioma resection, and risk assessment to analyze postoperative outcomes. The key methods of tractography in current practice and crucial white matter fiber bundles are summarized. After a review of the physical basis of DTI and post-DTI tractography, the authors discuss the methodologies with which to adapt DT image processing for surgical planning, as well as the potential of connectomic imaging to facilitate a network approach to oncofunctional optimization in glioma surgery.

Identifying and characterizing projections from the subthalamic nucleus to the cerebellum in humans.

A connection between the subthalamic nucleus (STN) and the cerebellum which has been shown to exist in non-human primates, was recently identified in humans. However, its anatomical features, network properties and function have yet to be elucidated in humans. In the present study, we quantified the STN-cerebellum pathway in humans and explored its function based on structural observations. Anatomical features and asymmetry index (AI) were explored using high definition fiber tractography data of 30 individuals from the Massachusetts General Hospital - Human Connectome Project adult diffusion database. Pearson's correlation analysis was performed to determine the interrelationship between the subdivisions of the STN-cerebellum and the global cortical-STN connections. The pathway was visualized bilaterally in all the subjects. Typically, after setting out from the STN, the STN-cerebellum projections incorporated into the nearby corticopontine tracts, passing through the cerebral peduncle, mediated by the pontine nucleus and then connecting in two opposite directions to join the bilateral middle cerebellar peduncle. On the group averaged level, 78.03% and 62.54% of fibers from the right and left STN respectively, distributed to Crus I in the cerebellum, part of the remaining fibers projected to Crus II, with most of the fibers crossing contralaterally. According to the AI evaluation, 60% of the participants were right STN dominant, 23% were left STN dominant, and 17% were relatively symmetric. Pearson's correlation analysis further indicated that the number of pathways from mesial Brodmann area 8 to the STN (hyperdirect pathway associated with decision making) was positively correlated with the number of fibers from the right STN to Crus I. The insertion and termination, the right-side dominance, and the positive correlation with the hyperdirect pathway all suggest that the STN-cerebellum pathway might be involved in decision-making processes.

Projections of Brodmann Area 6 to the Pyramidal Tract in Humans: Quantifications Using High Angular Resolution Data.

Primate studies indicate that the pyramidal tract (PyT) could originate from Brodmann area (BA) 6. However, in humans, the accurate origin of PyT from BA 6 is still uncertain owing to difficulties in visualizing anatomical features such as the fanning shape at the corona radiata and multiple crossings at the semioval centrum. High angular-resolution diffusion imaging (HARDI) could reliably replicate these anatomical features. We explored the origin of the human PyT from BA 6 using HARDI. With HARDI data of 30 adults from the Massachusetts General Hospital-Human Connectome Project (MGH-HCP) database and the HCP 1021 template (average of 1021 HCP diffusion data), we visualized the PyT at the 30-averaged group level and the 1021 large-sample level and validated the observations in each of the individuals. Endpoints of the fibers within each subregion were quantified. PyT fibers originating from the BA 6 were consistently visualized in all images. Specifically, the bilateral supplementary motor area (SMA) and dorsal premotor area (dPMA) were consistently found to contribute to the PyT. PyT fibers from BA 6 and those from BA 4 exhibited a twisting topology. The PyT contains fibers originating from the SMA and dPMA in BA 6. Infarction of these regions or aging would result in incomplete provision of information to the PyT and concomitant decreases in motor planning and coordination abilities.

Construction of brain structural connectivity network using a novel integrated algorithm based on ensemble average propagator.

An important task for neuroscience is to accurately construct structural connectivity network of human brain. Tractography constructed based on high angular resolution diffusion imaging (HARDI) provides valuable information of human brain structural connections. Existing algorithms, mainly categorized as deterministic or probabilistic, come with inherent limitations (e.g., fiber direction uncertainty induced by noise, or anatomically unreasonable connections and heavy computational cost). In this study, a novel integrated algorithm was proposed to construct brain structural connectivity network by incorporating the deterministic path planning and probabilistic connection strength estimation, based on ensemble average propagator (EAP). We first estimated EAPs from multi-shell samples using the spherical polar Fourier imaging (SPFI), and then extracted diffusion orientations coinciding with neural fiber tracts. Only under angular constraints, the deterministic path planning algorithm was subsequently used to find all reasonable pathways between pairwise white matter (WM) voxels in different regions of interest (ROIs). Consequently, a train of consecutive WM voxels along each of the identified pathways was determined, and the connection strength of these pathways was computed by integrating their EAP alignment over a solid angle. The connection strength of a pair of WM voxels was assigned as the connection strength with the largest connection possibility. Finally, the connection strength between two ROIs was calculated as the sum of all the connection probabilities of each pair of WM voxels in the ROIs. A comparison against voxel-graph based probabilistic tractography method was performed on Fibercup phantom dataset, and the results demonstrated that the proposed method can produce better structural connection and is more computationally economical. Lastly, three datasets from Human Connectome Project (HCP) S1200 group were tested and their structural connectivity networks were constructed for topological analysis. The results showed great consistency in network metrics with previous WM network studies in healthy adults.

Effects of Brain Parcellation on the Characterization of Topological Deterioration in Alzheimer's Disease.

Alzheimer's disease (AD) causes the progressive deterioration of neural connections, disrupting structural connectivity (SC) networks within the brain. Graph-based analyses of SC networks have shown that topological properties can reveal the course of AD propagation. Different whole-brain parcellation schemes have been developed to define the nodes of these SC networks, although it remains unclear which scheme can best describe the AD-related deterioration of SC networks. In this study, four whole-brain parcellation schemes with different numbers of parcels were used to define SC network nodes. SC networks were constructed based on high angular resolution diffusion imaging (HARDI) tractography for a mixed cohort that includes 20 normal controls (NC), 20 early mild cognitive impairment (EMCI), 20 late mild cognitive impairment (LMCI), and 20 AD patients, from the Alzheimer's Disease Neuroimaging Initiative. Parcellation schemes investigated in this study include the OASIS-TRT-20 (62 regions), AAL (116 regions), HCP-MMP (180 regions), and Gordon-rsfMRI (333 regions), which have all been widely used for the construction of brain structural or functional connectivity networks. Topological characteristics of the SC networks, including the network strength, global efficiency, clustering coefficient, rich-club, characteristic path length, k-core, rich-club coefficient, and modularity, were fully investigated at the network level. Statistical analyses were performed on these metrics using Kruskal-Wallis tests to examine the group differences that were apparent at different stages of AD progression. Results suggest that the HCP-MMP scheme is the most robust and sensitive to AD progression, while the OASIS-TRT-20 scheme is sensitive to group differences in network strength, global efficiency, k-core, and rich-club coefficient at k-levels from 18 and 39. With the exception of the rich-club and modularity coefficients, AAL could not significantly identify group differences on other topological metrics. Further, the Gordon-rsfMRI atlas only significantly differentiates the groups on network strength, characteristic path length, k-core, and rich-club coefficient. Results show that the topological examination of SC networks with different parcellation schemes can provide important complementary AD-related information and thus contribute to a more accurate and earlier diagnosis of AD.

Characterizing the role of the structural connectome in seizure dynamics.

How does the human brain's structural scaffold give rise to its intricate functional dynamics? This is a central question in translational neuroscience that is particularly relevant to epilepsy, a disorder affecting over 50 million subjects worldwide. Treatment for medication-resistant focal epilepsy is often structural-through surgery or laser ablation-but structural targets, particularly in patients without clear lesions, are largely based on functional mapping via intracranial EEG. Unfortunately, the relationship between structural and functional connectivity in the seizing brain is poorly understood. In this study, we quantify structure-function coupling, specifically between white matter connections and intracranial EEG, across pre-ictal and ictal periods in 45 seizures from nine patients with unilateral drug-resistant focal epilepsy. We use high angular resolution diffusion imaging (HARDI) tractography to construct structural connectivity networks and correlate these networks with time-varying broadband and frequency-specific functional networks derived from coregistered intracranial EEG. Across all frequency bands, we find significant increases in structure-function coupling from pre-ictal to ictal periods. We demonstrate that short-range structural connections are primarily responsible for this increase in coupling. Finally, we find that spatiotemporal patterns of structure-function coupling are highly stereotyped for each patient. These results suggest that seizures harness the underlying structural connectome as they propagate. Mapping the relationship between structural and functional connectivity in epilepsy may inform new therapies to halt seizure spread, and pave the way for targeted patient-specific interventions.