Diffusion MRI head motion correction methods are highly accurate but impacted by denoising and sampling scheme

Head motion correction is particularly challenging in diffusion-weighted MRI (dMRI) scans due to the dramatic changes in image contrast at different gradient strengths and directions. Head motion correction is typically performed using a Gaussian Process model implemented in FSL's Eddy. Recently, the 3dSHORE-based SHORELine method was introduced that does not require shell-based acquisitions, but it has not been previously benchmarked. Here we perform a comprehensive evaluation of both methods on realistic simulations of a software fiber phantom that provides known ground-truth head motion. We demonstrate that both methods perform remarkably well, but that performance can be impacted by sampling scheme and the extent of head motion and the denoising strategy applied before head motion correction. Furthermore, we find Eddy benefits from denoising the data first with MP-PCA. In sum, we provide the most extensive known benchmarking of dMRI head motion correction, together with extensive simulation data and a reproducible workflow. PRACTITIONER POINTS: Both Eddy and SHORELine head motion correction methods performed quite well on a large variety of simulated data. Denoising with MP-PCA can improve head motion correction performance when Eddy is used. SHORELine effectively corrects motion in non-shelled diffusion spectrum imaging data.

Enhanced white matter fiber tract of the cortical visual system in visual artists: implications for creativity

Introduction

This study aimed to examine the white matter characteristics of visual artists (VAs) in terms of visual creativity and the structural connectivity within the cortical visual system.

Methods

Diffusion spectrum imaging was utilized to examine the changes in white matter within the cortical visual system of a group of VAs (n = 25) in comparison to a group of healthy controls matched for age and education (n = 24). To assess the integrity of white matter and its relationship with visual creativity, we conducted a comprehensive analysis using region-based and track-specific tractographic examinations.

Results

Our study uncovered that VAs demonstrated increased normalized quantitative anisotropy in specific brain regions, including the right inferior temporal gyrus and right lateral occipital gyrus, along with the corresponding white matter fiber tracts connecting these regions. These enhancements within the cortical visual system were also found to be correlated with measures of visual creativity obtained through psychological assessments.

Discussion

The noted enhancement in the white matter within the cortical visual system of VAs, along with its association with visual creativity, is consistent with earlier research demonstrating heightened functional connectivity in the same system among VAs. Our study's findings suggest a link between the visual creativity of VAs and structural alterations within the brain's visual system.

Research Progress in Diffusion Spectrum Imaging

Studies have demonstrated that many regions in the human brain include multidirectional fiber tracts, in which the diffusion of water molecules within image voxels does not follow a Gaussian distribution. Therefore, the conventional diffusion tensor imaging (DTI) that hypothesizes a single fiber orientation within a voxel is intrinsically incapable of revealing the complex microstructures of brain tissues. Diffusion spectrum imaging (DSI) employs a pulse sequence with different b-values along multiple gradient directions to sample the diffusion information of water molecules in the entire q-space and then quantitatively estimates the diffusion profile using a probability density function with a high angular resolution. Studies have suggested that DSI can reliably observe the multidirectional fibers within each voxel and allow fiber tracking along different directions, which can improve fiber reconstruction reflecting the true but complicated brain structures that were not observed in the previous DTI studies. Moreover, with increasing angular resolution, DSI is able to reveal new neuroimaging biomarkers used for disease diagnosis and the prediction of disorder progression. However, so far, this method has not been used widely in clinical studies, due to its overly long scanning time and difficult post-processing. Within this context, the current paper aims to conduct a comprehensive review of DSI research, including the fundamental principles, methodology, and application progress of DSI tractography. By summarizing the DSI studies in recent years, we propose potential solutions towards the existing problem in the methodology and applications of DSI technology as follows: (1) using compressed sensing to undersample data and to reconstruct the diffusion signal may be an efficient and promising method for reducing scanning time; (2) the probability density function includes more information than the orientation distribution function, and it should be extended in application studies; and (3) large-sample study is encouraged to confirm the reliability and reproducibility of findings in clinical diseases. These findings may help deepen the understanding of the DSI method and promote its development in clinical applications.

The role of corticospinal and extrapyramidal pathways in motor impairment after stroke

Anisotropy of descending motor pathways has repeatedly been linked to the severity of motor impairment following stroke-related damage to the corticospinal tract. Despite promising findings consistently tying anisotropy of the ipsilesional corticospinal tract to motor outcome, anisotropy is not yet utilized as a biomarker for motor recovery in clinical practice as several methodological constraints hinder a conclusive understanding of degenerative processes in the ipsilesional corticospinal tract and compensatory roles of other descending motor pathways. These constraints include estimating anisotropy in voxels with multiple fibre directions, sampling biases and confounds due to ageing-related atrophy. The present study addressed these issues by combining diffusion spectrum imaging with a novel compartmentwise analysis approach differentiating voxels with one dominant fibre direction (one-directional voxels) from voxels with multiple fibre directions. Compartmentwise anisotropy for bihemispheric corticospinal and extrapyramidal tracts was compared between 25 chronic stroke patients, 22 healthy age-matched controls, and 24 healthy young controls and its associations with motor performance of the upper and lower limbs were assessed. Our results provide direct evidence for Wallerian degeneration along the entire length of the ipsilesional corticospinal tract reflected by decreased anisotropy in descending fibres compared with age-matched controls, while ageing-related atrophy was observed more ubiquitously across compartments. Anisotropy of descending ipsilesional corticospinal tract voxels showed highly robust correlations with various aspects of upper and lower limb motor impairment, highlighting the behavioural relevance of Wallerian degeneration. Moreover, anisotropy measures of two-directional voxels within bihemispheric rubrospinal and reticulospinal tracts were linked to lower limb deficits, while anisotropy of two-directional contralesional rubrospinal voxels explained gross motor performance of the affected hand. Of note, the relevant extrapyramidal structures contained fibres crossing the midline, fibres potentially mitigating output from brain stem nuclei, and fibres transferring signals between the extrapyramidal system and the cerebellum. Thus, specific parts of extrapyramidal pathways seem to compensate for impaired gross arm and leg movements incurred through stroke-related corticospinal tract lesions, while fine motor control of the paretic hand critically relies on ipsilesional corticospinal tract integrity. Importantly, our findings suggest that the extrapyramidal system may serve as a compensatory structural reserve independent of post-stroke reorganization of extrapyramidal tracts. In summary, compartment-specific anisotropy of ipsilesional corticospinal tract and extrapyramidal tracts explained distinct aspects of motor impairment, with both systems representing different pathophysiological mechanisms contributing to motor control post-stroke. Considering both systems in concert may help to develop diffusion imaging biomarkers for specific motor functions after stroke.

Differences in white matter segments in autistic males, non-autistic siblings, and non-autistic participants: An intermediate phenotype approach

LAY ABSTRACT

White matter is the neural pathway that connects neurons in different brain regions. Although research has shown white matter differences between autistic and non-autistic people, little is known about the properties of white matter in non-autistic siblings. In addition, past studies often focused on the whole neural tracts; it is unclear where differences exist in specific segments of the tracts. This study identified neural segments that differed between autistic people, their non-autistic siblings, and the age- and non-autistic people. We found altered segments within the tracts connected to anterior brain regions corresponding to several higher cognitive functions (e.g. executive functions) in autistic people and non-autistic siblings. Segments connecting to regions for social cognition and Theory of Mind were altered only in autistic people, explaining a large portion of autistic traits and may serve as neuroimaging markers. Segments within the tracts associated with fewer autistic traits or connecting brain regions for diverse highly integrated functions showed compensatory increases in the microstructural properties in non-autistic siblings. Our findings suggest that differential white matter segments that are shared between autistic people and non-autistic siblings may serve as potential "intermediate phenotypes"-biological or neuropsychological characteristics in the causal link between genetics and symptoms-of autism. These findings shed light on a promising neuroimaging model to refine the intermediate phenotype of autism which may facilitate further identification of the genetic and biological bases of autism. Future research exploring links between compensatory segments and neurocognitive strengths in non-autistic siblings may help understand brain adaptation to autism.

Quantitative Parameters of Diffusion Spectrum Imaging: HER2 Status Prediction in Patients With Breast Cancer

Objective

To explore the value of quantitative parameters derived from diffusion spectrum imaging (DSI) in preoperatively predicting human epidermal growth factor receptor 2 (HER2) status in patients with breast cancer.

Methods

In this prospective study, 114 and 56 female patients with invasive ductal carcinoma were consecutively included in a derivation cohort and an independent validation cohort, respectively. Each patient was categorized into HER2-positive or HER2-negative groups based on the pathologic result. All patients underwent DSI and conventional MRI including dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI). The tumor size, type of the time-signal intensity curve (TIC) from DCE-MRI, apparent diffusion coefficient (ADC) from DWI, and quantitative parameters derived from DSI, including diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) of primary tumors, were measured and compared between the HER2-positive and HER2-negative groups in the derivation cohort. Univariable and multivariable logistic regression analyses were used to determine the potential independent predictors of HER2 status. The discriminative ability of quantitative parameters was assessed by receiver operating characteristic (ROC) curve analyses and validated in the independent cohort.

Results

In the derivation cohort, the tumor size, TIC type, and ADC values did not differ between the HER2-positive and HER2-negative groups (p = 0.126–0.961). DSI quantitative parameters including axial kurtosis of DKI (DKI_AK), non-Gaussianity (MAP_NG), axial non-Gaussianity (MAP_NG_Ax), radial non-Gaussianity (MAP_NG_Rad), return-to-origin probability (MAP_RTOP), return-to-axis probability of MAP (MAP_RTAP), and intracellular volume fraction of NODDI (NODDI_ICVF) were lower in the HER2-positive group than in the HER2-negative group (p ≤ 0.001–0.035). DSI quantitative parameters including radial diffusivity (DTI_RD), mean diffusivity of DTI (DTI_MD), mean squared diffusion (MAP_MSD), and q-space inverse variance of MAP (MAP_QIV) were higher in the HER2-positive group than in the HER2-negative group (p = 0.016–0.049). The ROC analysis showed that the area under the curve (AUC) of ADC was 0.632 and 0.568, respectively, in the derivation and validation cohorts. The AUC values of DSI quantitative parameters ranged from 0.628 to 0.700 and from 0.673 to 0.721, respectively, in the derivation and validation cohorts. Logistic regression analysis showed that only NODDI_ICVF was an independent predictor of HER2 status (p = 0.001), with an AUC of 0.700 and 0.721, respectively, in the derivation and validation cohorts.

Conclusions

DSI could be helpful for preoperative prediction of HER2, but DSI alone may not be sufficient in predicting HER2 status preoperatively in patients with breast cancer.

Trigeminal Nerve White Matter Fiber Abnormalities in Primary Trigeminal Neuralgia: A Diffusion Spectrum Imaging Study

OBJECTIVE

Diffusion spectrum imaging (DSI) was used to quantitatively study the changes in the trigeminal cistern segment in patients with trigeminal neuralgia (TN) and to further explore the value of acquiring DSI data from patients with TN.

METHODS

To achieve high-resolution fiber tracking, 60 patients with TN and 35 healthy controls (HCs) were scanned with conventional magnetic resonance imaging (MRI) and DSI. The patients and the members of the control group were compared within and between groups. The correlations between quantitative parameters of DSI and the visual analog scale (VAS), and symptom duration and responsible vessel types were analyzed.

RESULTS

Compared with unaffected side of patients in the TN group, the affected side showed significantly decreased quantitative anisotropy (QA) (p < 0.001), fractional anisotropy (FA) (p = 0.001), and general FA (GFA) (p < 0.001). The unaffected side exhibited significantly decreased QA (p + 0.001), FA (p = 0.001), and GFA (p < 0.001) and significantly increased axial diffusivity (AD) (p = 0.036) compared with the affected side of patients in the TN group and the average values of HCs. There were significantly decreased QA (p = 0.046) and FA (p = 0.008) between the unaffected side of patients and the average values of HCs. GFA can evidently distinguish arteries, veins, and features of unaffected side in TN patients.

CONCLUSION

Using high-resolution fiber tracking technology, DSI can provide quantitative information that can be used to detect the integrity of trigeminal white matter in patients with TN and can improve the understanding of the disease mechanism.

Apathy is associated with white matter network disruption and specific cognitive deficits in Parkinson's disease

BACKGROUND

Apathy is common in Parkinson's disease (PD) but its underlying white matter (WM) architecture is not well understood. Moreover, how apathy affects cognitive functions in PD remains unclear. We investigated apathy-related WM network alterations and the impact of apathy on cognition in the context of PD.

METHODS

Apathetic PD patients (aPD), non-apathetic PD patients (naPD), and matched healthy controls (HCs) underwent brain scans and clinical assessment. Graph-theoretical and network-based analyses were used for group comparisons of WM features derived from diffusion spectrum imaging (DSI). Path analysis was used to determine the direct and indirect effects of apathy and other correlates on different cognitive functions.

RESULTS

The aPD group was impaired on neural integration measured by global efficiency (p = 0.009) and characteristic path length (p = 0.04), executive function (p < 0.001), episodic memory (p < 0.001) and visuospatial ability (p = 0.02), and had reduced connectivity between the bilateral parietal lobes and between the putamen and temporal regions (p < 0.05). In PD, executive function was directly impacted by apathy and motor severity and indirectly influenced by depression; episodic memory was directly and indirectly impacted by apathy and depression, respectively; conversely, visuospatial ability was not related to any of these factors. Neural integration, though being marginally correlated with apathy, was not associated with cognition.

CONCLUSIONS

Our results suggest compromised neural integration and reduced structural connectivity in aPD. Apathy, depression, and motor severity showed distinct impacts on different cognitive functions with apathy being the most influential determinant of cognition in PD.

Three-dimensional anatomy of the anterior commissure: A tractography and anatomical study

The anterior commissure (AC) is one of the main commissural fibers of the brain. Commissural fibers are involved in bilateral integration and coordination of any normal brain activity. The AC is an important interhemispheric structure which forms a bidirectional communication channel between the frontal, temporal, parietal and occipital lobes bilaterally. In this article, we focus on describing the morphology, relations, and distribution of the AC through diffusion spectrum imaging (DSI) DSI-based fiber tracking. Tractographies were compared with gross anatomical dissection of the anterior commissure of adult's brains. Our study suggests that the AC found by tracking methods is bigger in comparison to the one found by dissection. In summary, the tractography added extensions to the main AC structure.

White-Matter Neuroanatomical Predictors of Aphasic Verb Retrieval

Background: Current neurocognitive models of language function have been primarily built from evidence regarding object naming, and their hypothesized white-matter circuit mechanisms tend to be coarse grained. Methods: In this cross-sectional, observational study, we used novel correlational tractography to assess the white-matter circuit mechanism behind verb retrieval, measured through action picture-naming performance in adults with chronic aphasia. Results: The analysis identified tracts implicated in current neurocognitive dual-stream models of language function, including the left inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, and arcuate fasciculus. However, the majority of tracts associated with verb retrieval were not ones included in dual-stream models of language function. Instead, they were projection pathways that connect frontal and parietal cortices to subcortical regions associated with motor functions, including the left corticothalamic pathway, frontopontine tract, parietopontine tract, corticostriatal pathway, and corticospinal tract. Conclusions: These results highlight that corticosubcortical projection pathways implicated in motor functions may be importantly related to language function. This finding is consistent with grounded accounts of cognition and may furthermore inform neurocognitive models. Impact statement This study suggests that in addition to traditional dual-stream language fiber tracts, the integrity of projection pathways that connect frontal and parietal cortices to subcortical motor regions may be critically associated with verb-retrieval impairments in adults with aphasia. This finding challenges neurological models of language function.

White Matter Connectivity in Incident Mild Cognitive Impairment: A Diffusion Spectrum Imaging Study of World Trade Center Responders at Midlife

Background:

Individuals who participated in response efforts at the World Trade Center (WTC) following 9/11/2001 are experiencing elevated incidence of mild cognitive impairment (MCI) at midlife.

Objective:

We hypothesized that white matter connectivity measured using diffusion spectrum imaging (DSI) would be restructured in WTC responders with MCI versus cognitively unimpaired responders.

Methods:

Twenty responders (mean age 56; 10 MCI/10 unimpaired) recruited from an epidemiological study were characterized using NIA-AA criteria alongside controls matched on demographics (age/sex/occupation/race/education). Axial DSI was acquired on a 3T Siemen’s Biograph mMR scanner (12-channel head coil) using a multi-band diffusion sequence. Connectometry examined whole-brain tract-level differences in white matter integrity. Fractional anisotropy (FA), mean diffusivity (MD), and quantified anisotropy were extracted for region of interest (ROI) analyses using the Desikan-Killiany atlas.

Results:

Connectometry identified both increased and decreased connectivity within regions of the brains of responders with MCI identified in the corticothalamic pathway and cortico-striatal pathway that survived adjustment for multiple comparisons. MCI was also associated with higher FA values in five ROIs including in the rostral anterior cingulate; lower MD values in four ROIs including the left rostral anterior cingulate; and higher MD values in the right inferior circular insula. Analyses by cognitive domain revealed nominal associations in domains of response speed, verbal learning, verbal retention, and visuospatial learning.

Conclusions:

WTC responders with MCI at midlife showed early signs of neurodegeneration characterized by both increased and decreased white matter diffusivity in regions commonly affected by early-onset Alzheimer’s disease.

Integrity of the Prefronto-striato-thalamo-prefrontal Loop Predicts Tai Chi Chuan Training Effects on Cognitive Task-switching in Middle-aged and Older Adults

Tai Chi Chuan (TCC) exercise has been shown to improve cognitive task-switching performance in older adults, but the extent of this positive effect varies among individuals. Past research also shows that brain white matter integrity could predict behavioral gains of cognitive and motor learning. Therefore, in this randomized controlled trial (NCT02270320), we examined whether baseline integrity of three target white matter tract groups was predictive of task-switching improvement after 12-week TCC training in middle-aged and older adults. Thirty-eight eligible participants were randomly assigned to a TCC group (n = 19) and a control group (n = 19). Cognitive task-switching and physical performances were collected before and after training. Brain diffusion spectrum MR images were acquired before training and the general fractional anisotropy (GFA) of each target white matter tract group was calculated to indicate baseline white matter integrity of that group. Correlation and regression analyses between these GFAs and post-training task-switching improvement were analyzed using adjusted p-values. After 12 weeks, significant task-switching and physical performance improvements were found only in the TCC group. Moreover, higher baseline GFA of the prefronto-striato-thalamo-prefrontal loop fibers (r = −0.63, p = 0.009), but not of the prefronto-parietal/occipital (r = −0.55, p = 0.026) and callosal (r = −0.35, p = 0.189) fiber groups, was associated with greater reductions of task-switching errors after the TCC training. Multiple regression analysis revealed that baseline GFA of the prefronto-striato-thalamo-prefrontal loop fibers was the only independent white matter integrity predictor of task-switching error reductions after TCC training (β = −0.620, adjusted R² change = 0.265, p = 0.009). These findings not only highlight the important role of baseline integrity of the prefronto-striatal circuits in influencing the extent of positive cognitive task-switching effects from short-term TCC training, but also implicate that preserving good white matter integrity in the aging process may be crucial in order to gain the best cognitive effects of exercise interventions.

Diffusion Spectrum Imaging of Corticospinal Tracts in Idiopathic Normal Pressure Hydrocephalus

Purpose: The purpose of this study was to measure the diffusion spectrum imaging (DSI) parameters of corticospinal tracts (CSTs) and evaluate diffusional changes in CSTs in patients with idiopathic normal pressure hydrocephalus (iNPH) by DSI. Methods: Twenty-three iNPH patients and twenty-one healthy controls (HCs) were involved in this study. Brain DSI data for all participants were collected through the same MR scanning procedure. The diffusion parameters measured and analyzed included quantitative anisotropy (QA), the isotropic diffusion component (ISO), general fractional anisotropy (GFA), fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) of corticospinal tracts. Results: The QA and ISO values of corticospinal tracts in iNPH patients were significantly lower than those in HCs (P_LQA = 0.008, P_RQA = 0.016, P_LISO = 0.024, P_RISO = 0.016). The mean MD, AD, and RD values in iNPH patients were significantly higher than those in HCs (P_MD = 0.032, P_AD = 0.032, P_RD = 0.048,). No significant differences in GFA and FA values were noted between iNPH patients and HCs. Conclusion: Decreased QA and ISO values of corticospinal tracts were found in iNPH patients. Quantitative CST evaluation using DSI may lead to information that can improve the present understanding of the disease mechanism.

Structural white matter connectometry of word production in aphasia: an observational study

While current dual-steam neurocognitive models of language function have coalesced around the view that distinct neuroanatomical networks subserve semantic and phonological processing, respectively, the specific white matter components of these networks remain a matter of debate. To inform this debate, we investigated relationships between structural white matter connectivity and word production in a cross-sectional study of 42 participants with aphasia due to unilateral left hemisphere stroke. Specifically, we reconstructed a local connectome matrix for each participant from diffusion spectrum imaging data and regressed these matrices on indices of semantic and phonological ability derived from their responses to a picture-naming test and a computational model of word production. These connectometry analyses indicated that both dorsally located (arcuate fasciculus) and ventrally located (inferior frontal-occipital, uncinate, and middle longitudinal fasciculi) tracts were associated with semantic ability, while associations with phonological ability were more dorsally situated, including the arcuate and middle longitudinal fasciculi. Associations with limbic pathways including the posterior cingulum bundle and the fornix were also found. All analyses controlled for total lesion volume and all results showing positive associations obtained false discovery rates < 0.05. These results challenge dual-stream accounts that deny a role for the arcuate fasciculus in semantic processing, and for ventral-stream pathways in language production. They also illuminate limbic contributions to both semantic and phonological processing for word production.

Comparison of q-Space Reconstruction Methods for Undersampled Diffusion Spectrum Imaging Data

PURPOSE

To compare different q-space reconstruction methods for undersampled diffusion spectrum imaging data.

MATERIALS AND METHODS

We compared the quality of three methods: Mean Apparent Propagator (MAP); Compressed Sensing using Identity (CSI) and Compressed Sensing using Dictionary (CSD) with simulated data and in vivo acquisitions. We used retrospective undersampling so that the fully sampled reconstruction could be used as ground truth. We used the normalized mean squared error (NMSE) and the Pearson's correlation coefficient as reconstruction quality indices. Additionally, we evaluated two propagator-based diffusion indices: mean squared displacement and return to zero probability. We also did a visual analysis around the centrum semiovale.

RESULTS

All methods had reconstruction errors below 5% with low undersampling factors and with a wide range of noise levels. However, the CSD method had at least 1-2% lower NMSE than the other reconstruction methods at higher noise levels. MAP was the second-best method when using a sufficiently high number of q-space samples. MAP reconstruction showed better propagator-based diffusion indices for in vivo acquisitions. With undersampling factors greater than 4, MAP and CSI have noticeably more reconstruction error than CSD.

CONCLUSION

Undersampled data were best reconstructed by means of CSD in simulations and in vivo. MAP was more accurate in the extraction of propagator-based indices, particularly for in vivo data.

White Matter Abnormalities of Auditory Neural Pathway in Sudden Sensorineural Hearing Loss Using Diffusion Spectrum Imaging: Different Findings From Tinnitus

Sudden sensorineural hearing loss (SSNHL) is a complex and challenging emergency which requires evidence regarding its pathophysiological changes to guide the treatment. The aim of this study was to evaluate the white matter integrity of the auditory neural pathway in patients with unilateral SSNHL in acute stage by using diffusion spectrum imaging tractography. In the present study, 60 individuals with acute SSNHL (29 males, 50.7 ± 11.8 years) and 25 healthy controls (13 males, 45.2 ± 13.2 years) underwent diffusion spectrum imaging tractography and high resolution T1 structural examinations using a 3T magnetic resonance imaging system. The areas of the auditory neural pathway were defined as regions of interest (ROIs). The quantitative anisotropy (QA) and the generalized fractional anisotropy (GFA) were compared between the patients with unilateral SSNHL and controls in these ROIs. We further evaluated the correlation between the parameter values and hearing loss level. The mean pure tone audiometry of patients at the onset presentation was 63.2 ± 26.2 dB. The right-sided SSNHL was involved in 25 (41.7%) cases and the left-sided in 35 (58.3%) cases. The QA values in the contralateral medial geniculate body, the bilateral anterior corona radiata and the anterior limb of internal capsule were significantly reduced in SSNHL patients compared to controls. In addition, the decrease QA value of the contralateral medial geniculate body was related to the increase severity of disease, even after controlling potential confounding factors. The present study demonstrated that patients with SSNHL exhibited altered integrity of white matter in the auditory neural pathway. Furthermore, the decreased QA values in the contralateral medial geniculate body might predict the severity of this disease. In the present study, tinnitus has not been found to effect in brain area obviously.

SHORE-based detection and imputation of dropout in diffusion MRI

PURPOSE

In diffusion MRI, dropout refers to a strong attenuation of the measured signal that is caused by bulk motion during the diffusion encoding. When left uncorrected, dropout will be erroneously interpreted as high diffusivity in the affected direction. We present a method to automatically detect dropout, and to replace the affected measurements with imputed values.

METHODS

Signal dropout is detected by deriving an outlier score from a simple harmonic oscillator-based reconstruction and estimation (SHORE) fit of all measurements. The outlier score is defined to detect measurements that are substantially lower than predicted by SHORE in a relative sense, while being less sensitive to measurement noise in cases of weak baseline signal. A second SHORE fit is based on detected inliers only, and its predictions are used to replace outliers.

RESULTS

Our method is shown to reliably detect and accurately impute dropout in simulated data, and to achieve plausible results in corrupted in vivo dMRI measurements. Computational effort is much lower than with previously proposed alternatives.

CONCLUSIONS

Deriving a suitable outlier score from SHORE results in a fast and accurate method for detection and imputation of dropout in diffusion MRI. It requires measurements with multiple b values (such as multi-shell or DSI), but is independent from the models used for analysis (such as DKI, NODDI, deconvolution, etc.).

Comparison of basis functions and q-space sampling schemes for robust compressed sensing reconstruction accelerating diffusion spectrum imaging

Time constraints placed on magnetic resonance imaging often restrict the application of advanced diffusion MRI (dMRI) protocols in clinical practice and in high throughput research studies. Therefore, acquisition strategies for accelerated dMRI have been investigated to allow for the collection of versatile and high quality imaging data, even if stringent scan time limits are imposed. Diffusion spectrum imaging (DSI), an advanced acquisition strategy that allows for a high resolution of intra-voxel microstructure, can be sufficiently accelerated by means of compressed sensing (CS) theory. CS theory describes a framework for the efficient collection of fewer samples of a data set than conventionally required followed by robust reconstruction to recover the full data set from sparse measurements. For an accurate recovery of DSI data, a suitable acquisition scheme for sparse q-space sampling and the sensing and sparsifying bases for CS reconstruction need to be selected. In this work we explore three different types of q-space undersampling schemes and two frameworks for CS reconstruction based on either Fourier or SHORE basis functions. After CS recovery, diffusion and microstructural parameters and orientational information are estimated from the reconstructed data by means of state-of-the-art processing techniques for dMRI analysis. By means of simulation, diffusion phantom and in vivo DSI data, an isotropic distribution of q-space samples was found to be optimal for sparse DSI. The CS reconstruction results indicate superior performance of Fourier-based CS-DSI compared to the SHORE-based approach. Based on these findings we outline an experimental design for accelerated DSI and robust CS reconstruction of the sparse measurements that is suitable for the application within time-limited studies.

Cerebro-Cerebellar Pathways for Verbal Working Memory

The current study examined the structural and functional connectivity of the cerebro-cerebellar network of verbal working memory as proposed by Chen and Desmond (2005a). Diffusion spectrum imaging was employed to establish structural connectivity between cerebro-cerebellar regions co-activated during a verbal working memory task. The inferior frontal gyrus, inferior parietal lobule, pons, thalamus, superior cerebellum and inferior cerebellum were used as regions of interest to reconstruct and segment the contralateral white matter cerebro-cerebellar circuitry. The segmented pathways were examined further to establish the relationship between structural and effective connectivity as well as the relationship between structural connectivity and verbal working memory performance. No direct relationship between structural and effective connectivity was found but the results demonstrated that structural connectivity is indirectly related to effective connectivity as DCM models that resembled more closely with underlying white matter pathways had a higher degree of model inference confidence. Additionally, it was demonstrated that the structural connectivity of the ponto-cerebellar tract was associated with individual differences in response time for verbal working memory. The findings of the study contribute to further our understanding of the relationship between structural and functional connectivity and the impact of variability in verbal working memory performance.

Simultaneous NODDI and GFA parameter map generation from subsampled q-space imaging using deep learning

PURPOSE

To develop a robust multidimensional deep-learning based method to simultaneously generate accurate neurite orientation dispersion and density imaging (NODDI) and generalized fractional anisotropy (GFA) parameter maps from undersampled q-space datasets for use in stroke imaging.

METHODS

Traditional diffusion spectrum imaging (DSI) capable of producing accurate NODDI and GFA parameter maps requires hundreds of q-space samples which renders the scan time clinically untenable. A convolutional neural network (CNN) was trained to generated NODDI and GFA parameter maps simultaneously from 10× undersampled q-space data. A total of 48 DSI scans from 15 stroke patients and 14 normal subjects were acquired for training, validating, and testing this method. The proposed network was compared to previously proposed voxel-wise machine learning based approaches for q-space imaging. Network-generated images were used to predict stroke functional outcome measures.

RESULTS

The proposed network achieves significant performance advantages compared to previously proposed machine learning approaches, showing significant improvements across image quality metrics. Generating these parameter maps using CNNs also comes with the computational benefits of only needing to generate and train a single network instead of multiple networks for each parameter type. Post-stroke outcome prediction metrics do not appreciably change when using images generated from this proposed technique. Over three test participants, the predicted stroke functional outcome scores were within 1-6% of the clinical evaluations.

CONCLUSIONS

Estimates of NODDI and GFA parameters estimated simultaneously with a deep learning network from highly undersampled q-space data were improved compared to other state-of-the-art methods providing a 10-fold reduction scan time compared to conventional methods.

Compressed Sensing Diffusion Spectrum Imaging for Accelerated Diffusion Microstructure MRI in Long-Term Population Imaging

Mapping non-invasively the complex microstructural architecture of the living human brain, diffusion magnetic resonance imaging (dMRI) is one of the core imaging modalities in current population studies. For the application in longitudinal population imaging, the dMRI protocol should deliver reliable data with maximum potential for future analysis. With the recent introduction of novel MRI hardware, advanced dMRI acquisition strategies can be applied within reasonable scan time. In this work we conducted a pilot study based on the requirements for high resolution dMRI in a long-term and high throughput population study. The key question was: can diffusion spectrum imaging accelerated by compressed sensing theory (CS-DSI) be used as an advanced imaging protocol for microstructure dMRI in a long-term population imaging study? As a minimum requirement we expected a high level of agreement of several diffusion metrics derived from both CS-DSI and a 3-shell high angular resolution diffusion imaging (HARDI) acquisition, an established imaging strategy used in other population studies. A wide spectrum of state-of-the-art diffusion processing and analysis techniques was applied to the pilot study data including quantitative diffusion and microstructural parameter mapping, fiber orientation estimation and white matter fiber tracking. When considering diffusion weighted images up to the same maximum diffusion weighting for both protocols, group analysis across 20 subjects indicates that CS-DSI performs comparable to 3-shell HARDI in the estimation of diffusion and microstructural parameters. Further, both protocols provide similar results in the estimation of fiber orientations and for local fiber tracking. CS-DSI provides high radial resolution while maintaining high angular resolution and it is well-suited for analysis strategies that require high b-value acquisitions, such as CHARMED modeling and biomarkers from the diffusion propagator.

Human Thalamic-Prefrontal Peduncle Connectivity Revealed by Diffusion Spectrum Imaging Fiber Tracking

The thalamic-prefrontal peduncle (TPP) is a large bundle connecting the thalamus and prefrontal cortex. The definitive structure and function of the TPP are still controversial. To investigate the connectivity and segmentation patterns of the TPP, we employed diffusion spectrum imaging with generalized q-sampling reconstruction to perform both subject-specific and template-based analyses. Our results confirmed the trajectory and spatial relationship of the TPP in the human brain and identified the connection areas in the prefrontal cortex. The TPP-connecting areas identified based on Brodmann areas (BAs) were BAs 8–11 and 45–47. Based on the automated anatomical atlas, these areas were the medial superior frontal gyrus, superior frontal gyrus, middle frontal gyrus, pars triangularis, pars orbitalis, anterior orbital gyrus, and lateral orbital gyrus. In addition, we identified the TPP connection areas in the thalamus, including the anterior and medial nuclei, and the lateral dorsal/lateral posterior nuclei. TPP fibers connected the thalamus with the ipsilateral prefrontal BAs 11, 47, 10, 46, 45, 9, and 8 seriatim from medial to lateral, layer by layer. Our results provide further details of the thalamic-prefrontal peduncle structure, and may aid future studies and a better understanding of the functional roles of the TPP in the human brain.

The Superior Fronto-Occipital Fasciculus in the Human Brain Revealed by Diffusion Spectrum Imaging Tractography: An Anatomical Reality or a Methodological Artifact?

The existence of the superior fronto-occipital fasciculus (SFOF) in the human brain remains controversial. The aim of the present study was to clarify the existence, course, and terminations of the SFOF. High angular diffusion spectrum imaging (DSI) analysis was performed on six healthy adults and on a template of 842 subjects from the Human Connectome Project. To verify tractography results, we performed fiber microdissections of four post-mortem human brains. Based on DSI tractography, we reconstructed the SFOF in the subjects and the template from the Human Connectome Project that originated from the rostral and medial parts of the superior and middle frontal gyri. By tractography, we found that the fibers formed a compact fascicle at the level of the anterior horn of the lateral ventricle coursing above the head of caudate nucleus, medial to the corona radiate and under the corpus callosum (CC), and terminated at the parietal region via the lower part of the caudate nucleus. We consider that this fiber bundle observed by tractography is the SFOF, although it terminates mainly at the parietal region, rather than occipital lobe. By contrast, we were unable to identify a fiber bundle corresponding to the SFOF in our fiber dissection study. Although we did not provide definite evidence of the SFOF in the human brain, these findings may be useful for future studies in this field.

Individualized prediction of schizophrenia based on the whole-brain pattern of altered white matter tract integrity

BACKGROUND

A schizophrenia diagnosis relies on characteristic symptoms identified by trained physicians, and is thus prone to subjectivity. This study developed a procedure for the individualized prediction of schizophrenia based on whole-brain patterns of altered white matter tract integrity.

METHODS

The study comprised training (108 patients and 144 controls) and testing (60 patients and 60 controls) groups. Male and female participants were comparable in each group and were analyzed separately. All participants underwent diffusion spectrum imaging of the head, and the data were analyzed using the tract-based automatic analysis method to generate a standardized two-dimensional array of white matter tract integrity, called the connectogram. Unique patterns in the connectogram that most accurately identified schizophrenia were systematically reviewed in the training group. Then, the diagnostic performance of the patterns was individually verified in the testing group by using receiver-operating characteristic curve analysis.

RESULTS

The performance was high in men (accuracy = 0.85) and satisfactory in women (accuracy = 0.75). In men, the pattern was located in discrete fiber tracts, as has been consistently reported in the literature; by contrast, the pattern was widespread over all tracts in women. These distinct patterns suggest that there is a higher variability in the microstructural alterations in female patients than in male patients.

CONCLUSIONS

The individualized prediction of schizophrenia is feasible based on the different whole-brain patterns of tract integrity. The optimal masks and their corresponding regions in the fiber tracts could serve as potential imaging biomarkers for schizophrenia. Hum Brain Mapp 39:575-587, 2018. © 2017 Wiley Periodicals, Inc.

Altered white-matter integrity in unaffected siblings of probands with autism spectrum disorders

Despite the evidence of altered white-matter tract property in individuals with autism spectrum disorder (ASD), little is known about their unaffected siblings. This study aimed to investigate white-matter integrity in unaffected siblings of ASD probands. Thirty-nine unaffected siblings (mean age 15.6 ± 6.0 years; 27 males, 69.2%) and 39 typically developing controls (TDC) (14.2 ± 5.6 years; 26 males, 66.7%) were assessed with diffusion spectrum images and neuropsychological tests. Using the tract-based automatic analysis and the threshold-free cluster weighted (TFCW) scores, we searched for the segments among 76 tracts with the largest difference over the entire brain compared to TDC. Tract integrity was quantified by calculating the mean generalized fractional anisotropy (mGFA) values of the segments with the largest difference in TFCW scores. Unaffected siblings showed reduced mGFA in the bilateral frontal aslant tracts, the right superior longitudinal fasciculus 2 (SLF2), the frontostriatal tracts from the right dorsolateral and left ventrolateral prefrontal cortices, the thalamic radiations of the left ventral and the right dorsal thalamus, the callosal fibers of the splenium, and the increased mGFA of the callosal fibers of the precuneus and the left inferior longitudinal fasciculus. Among these, reduced right SLF2 mGFA was associated with social awareness deficits; impaired frontostriatal tract was associated with internalizing problems, while right frontal aslant tract integrity was associated with visual memory deficits. In conclusion, unaffected siblings showed the aberrant integrity of several white-matter tracts, which were correlated with clinical symptoms and neurocognitive dysfunction. The altered tract integrity could be further examined in the probands with ASD. Hum Brain Mapp 38:6053-6067, 2017. © 2017 Wiley Periodicals, Inc.

In vivo visualization of connections among revised Papez circuit hubs using full q-space diffusion spectrum imaging tractography

Structural connections among the hubs of the revised Papez circuit remain to be elucidated in the human brain. As the original Papez circuit failed to explain functional imaging findings, a more detailed investigation is needed to delineate connections among the circuit's key hubs. Here we acquired diffusion spectrum imaging (DSI) from eight normal subjects and used data from the Human Connectome Project (HCP) to elucidate connections among hubs in the retrosplenial gyrus, hippocampus, mammillary bodies, and anterior thalamic nuclei. Our results show that the ventral hippocampal commissure (VHC) was visualized in all eight individual DSI datasets, as well as in the DSI and HCP group datasets, but a strictly defined VHC was only visualized in one individual dataset. Thalamic fibers were observed to connect with both the posterior cingulate cortex (PCC) and retrosplenial cortex (RSC). The RSC was mainly responsible for direct hippocampal connections, while the PCC was not. This indicates that the RSC and PCC represent separate functional hubs in humans, as also shown by previous primate axonal tracing studies and functional magnetic resonance imaging observations.

Toward an electrophysiological "sweet spot" for deep brain stimulation in the subthalamic nucleus

Enhanced beta-band activity recorded in patients suffering from Parkinson's Disease (PD) has been described as a potential physiomarker for disease severity. Beta power is suppressed by Levodopa intake and STN deep brain stimulation (DBS) and correlates with disease severity across patients. The aim of the present study was to explore the promising signature of the physiomarker in the spatial domain. Based on local field potential data acquired from 54 patients undergoing STN-DBS, power values within alpha, beta, low beta, and high beta bands were calculated. Values were projected into common stereotactic space after DBS lead localization. Recorded beta power values were significantly higher at posterior and dorsal lead positions, as well as in active compared with inactive pairs. The peak of activity in the beta band was situated within the sensorimotor functional zone of the nucleus. In contrast, higher alpha activity was found in a more ventromedial region, potentially corresponding to associative or premotor functional zones of the STN. Beta- and alpha-power peaks were then used as seeds in a fiber tracking experiment. Here, the beta-site received more input from primary motor cortex whereas the alpha-site was more strongly connected to premotor and prefrontal areas. The results summarize predominant spatial locations of frequency signatures recorded in STN-DBS patients in a probabilistic fashion. The site of predominant beta-activity may serve as an electrophysiologically determined target for optimal outcome in STN-DBS for PD in the future. Hum Brain Mapp 38:3377-3390, 2017. © 2017 Wiley Periodicals, Inc.

Accelerated radial diffusion spectrum imaging using a multi-echo stimulated echo diffusion sequence

PURPOSE

Diffusion spectrum imaging (DSI) provides us non-invasively and robustly with anatomical details of brain microstructure. To achieve sufficient angular resolution, DSI requires a large number of q-space samples, leading to long acquisition times. This need is mitigated here by combining the beneficial properties of Radial q-space sampling for DSI with a Multi-Echo Stimulated Echo Sequence (MESTIM).

METHODS

Full 2D k-spaces for each of several q-space samples, along the same radially outward line in q-space, are acquired in one readout train with one spin and three stimulated echoes. RF flip angles are carefully chosen to distribute spin magnetization over the echoes and the DSI reconstruction is adapted to account for differences in diffusion time among echoes.

RESULTS

Individual datasets and bootstrapped reproducibility analysis demonstrate image quality and SNR of the more-than-twofold-accelerated RDSI MESTIM sequence. Orientation distribution functions (ODF) and tractography results benefit from the longer diffusion times of the latter echoes in the echo train.

CONCLUSION

A MESTIM sequence can be used to shorten RDSI acquisition times significantly without loss of image or ODF quality. Further acceleration is possible by combination with simultaneous multi-slice techniques. Magn Reson Med 79:306-316, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Q-space truncation and sampling in diffusion spectrum imaging

PURPOSE

To characterize the q-space truncation and sampling on the spin-displacement probability density function (PDF) in diffusion spectrum imaging (DSI).

METHODS

DSI data were acquired using the MGH-USC connectome scanner (Gmax  = 300 mT/m) with bmax  = 30,000 s/mm2 , 17 × 17 × 17, 15 × 15 × 15 and 11 × 11 × 11 grids in ex vivo human brains and bmax  = 10,000 s/mm2 , 11 × 11 × 11 grid in vivo. An additional in vivo scan using bmax =7,000 s/mm2 , 11 × 11 × 11 grid was performed with a derated gradient strength of 40 mT/m. PDFs and orientation distribution functions (ODFs) were reconstructed with different q-space filtering and PDF integration lengths, and from down-sampled data by factors of two and three.

RESULTS

Both ex vivo and in vivo data showed Gibbs ringing in PDFs, which becomes the main source of artifact in the subsequently reconstructed ODFs. For down-sampled data, PDFs interfere with the first replicas or their ringing, leading to obscured orientations in ODFs.

CONCLUSION

The minimum required q-space sampling density corresponds to a field-of-view approximately equal to twice the mean displacement distance (MDD) of the tissue. The 11 × 11 × 11 grid is suitable for both ex vivo and in vivo DSI experiments. To minimize the effects of Gibbs ringing, ODFs should be reconstructed from unfiltered q-space data with the integration length over the PDF constrained to around the MDD. Magn Reson Med 76:1750-1763, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

Subcomponents and Connectivity of the Inferior Fronto-Occipital Fasciculus Revealed by Diffusion Spectrum Imaging Fiber Tracking

The definitive structure and functional role of the inferior fronto-occipital fasciculus (IFOF) are still controversial. In this study, we aimed to investigate the connectivity, asymmetry, and segmentation patterns of this bundle. High angular diffusion spectrum imaging (DSI) analysis was performed on 10 healthy adults and a 90-subject DSI template (NTU-90 Atlas). In addition, a new tractography approach based on the anatomic subregions and two regions of interest (ROI) was evaluated for the fiber reconstructions. More widespread anterior-posterior connections than previous “standard” definition of the IFOF were found. This distinct pathway demonstrated a greater inter-subjects connective variability with a maximum of 40% overlap in its central part. The statistical results revealed no asymmetry between the left and right hemispheres and no significant differences existed in distributions of the IFOF according to sex. In addition, five subcomponents within the IFOF were identified according to the frontal areas of originations. As the subcomponents passed through the anterior floor of the external capsule, the fibers radiated to the posterior terminations. The most common connection patterns of the subcomponents were as follows: IFOF-I, from frontal polar cortex to occipital pole, inferior occipital lobe, middle occipital lobe, superior occipital lobe, and pericalcarine; IFOF-II, from orbito-frontal cortex to occipital pole, inferior occipital lobe, middle occipital lobe, superior occipital lobe, and pericalcarine; IFOF-III, from inferior frontal gyrus to inferior occipital lobe, middle occipital lobe, superior occipital lobe, occipital pole, and pericalcarine; IFOF-IV, from middle frontal gyrus to occipital pole, and inferior occipital lobe; IFOF-V, from superior frontal gyrus to occipital pole, inferior occipital lobe, and middle occipital lobe. Our work demonstrates the feasibility of high resolution diffusion tensor tractography with sufficient sensitivity to elucidate more anatomical details of the IFOF. And we provides a new framework for subdividing the IFOF for better understanding its functional role in the human brain.

Disorder-Specific Alteration in White Matter Structural Property in Adults With Autism Spectrum Disorder Relative to Adults With ADHD and Adult Controls

OBJECTIVE

Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are not only often comorbid but also overlapped in behavioral and cognitive abnormalities. Little is known about whether these shared phenotypes are based on common or different underlying neuropathologies. Therefore, this study aims to examine the disorder-specific alterations in white matter (WM) structural property.

METHOD

The three comparison groups included 23 male adults with ASD (21.4 ± 3.1 years), 32 male adults with ADHD (23.4 ± 3.3 years), and 29 age-matched healthy male controls (22.4 ± 3.3 years). After acquisition of the diffusion spectrum imaging (DSI), whole brain tractography was reconstructed by a tract-based automatic analysis. Generalized fractional anisotropy (GFA) values were computed to indicate tract-specific WM property with adjusted P value < 0.05 for false discovery rate correction.

RESULTS

Post hoc analyses revealed that men with ASD exhibited significant lower GFA values than men with ADHD and male controls in six identified fiber tracts: the right arcuate fasciculus, right cingulum (hippocampal part), anterior commissure, and three callosal fibers (ventrolateral prefrontal cortex part, precentral part, superior temporal part). There was no significant difference in the GFA values of any of the fiber tracts between men with ADHD and controls. In men with ASD, the GFA values of the right arcuate fasciculus and right cingulum (hippocampal part) were negatively associated with autistic social-deficit symptoms, and the anterior commissure GFA value was positively correlated with intelligence.

CONCLUSIONS

This study highlights the disorder-specific alteration of the microstructural property of WM tracts in male adults with ASD. Hum Brain Mapp 38:384-395, 2017. © 2016 Wiley Periodicals, Inc.

Converting Multi-Shell and Diffusion Spectrum Imaging to High Angular Resolution Diffusion Imaging

Multi-shell and diffusion spectrum imaging (DSI) are becoming increasingly popular methods of acquiring diffusion MRI data in a research context. However, single-shell acquisitions, such as diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI), still remain the most common acquisition schemes in practice. Here we tested whether multi-shell and DSI data have conversion flexibility to be interpolated into corresponding HARDI data. We acquired multi-shell and DSI data on both a phantom and in vivo human tissue and converted them to HARDI. The correlation and difference between their diffusion signals, anisotropy values, diffusivity measurements, fiber orientations, connectivity matrices, and network measures were examined. Our analysis result showed that the diffusion signals, anisotropy, diffusivity, and connectivity matrix of the HARDI converted from multi-shell and DSI were highly correlated with those of the HARDI acquired on the MR scanner, with correlation coefficients around 0.8~0.9. The average angular error between converted and original HARDI was 20.7° at voxels with signal-to-noise ratios greater than 5. The network topology measures had less than 2% difference, whereas the average nodal measures had a percentage difference around 4~7%. In general, multi-shell and DSI acquisitions can be converted to their corresponding single-shell HARDI with high fidelity. This supports multi-shell and DSI acquisitions over HARDI acquisition as the scheme of choice for diffusion acquisitions.

Segmentation of the Cingulum Bundle in the Human Brain: A New Perspective Based on DSI Tractography and Fiber Dissection Study

The cingulum bundle (CB) is a critical white matter fiber tract in the brain, which forms connections between the frontal lobe, parietal lobe and temporal lobe. In non-human primates, the CB is actually divided into distinct subcomponents on the basis of corticocortical connections. However, at present, no study has verified similar distinct subdivisions in the human brain. In this study, we reconstructed these distinct subdivisions in the human brain, and determined their exact cortical connections using high definition fiber tracking (HDFT) technique on 10 healthy adults and a 488-subject template from the Human Connectome Project (HCP-488). Fiber dissections were performed to verify tractography results. Five CB segments were identified. CB-I ran from the subrostral areas to the precuneus and splenium, encircling the corpus callosum (CC). CB-II arched around the splenium and extended anteriorly above the CC to the medial aspect of the superior frontal gyrus (SFG). CB-III connected the superior parietal lobule (SPL) and precuneus with the medial aspect of the SFG. CB-IV was a relatively minor subcomponent from the SPL and precuneus to the frontal region. CB-V, the para-hippocampal cingulum, stemmed from the medial temporal lobe and fanned out to the occipital lobes. Our findings not only provide a more accurate and detailed description on the associated architecture of the subcomponents within the CB, but also offer new insights into the functional role of the CB in the human brain.

Organization of cortico-cortical pathways supporting memory retrieval across subregions of the left ventrolateral prefrontal cortex

Functional magnetic resonance imaging (fMRI) evidence indicates that different subregions of ventrolateral prefrontal cortex (VLPFC) participate in distinct cortical networks. These networks have been shown to support separable cognitive functions: anterior VLPFC [inferior frontal gyrus (IFG) pars orbitalis] functionally correlates with a ventral fronto-temporal network associated with top-down influences on memory retrieval, while mid-VLPFC (IFG pars triangularis) functionally correlates with a dorsal fronto-parietal network associated with postretrieval control processes. However, it is not known to what extent subregional differences in network affiliation and function are driven by differences in the organization of underlying white matter pathways. We used high-angular-resolution diffusion spectrum imaging and functional connectivity analysis in unanesthetized humans to address whether the organization of white matter connectivity differs between subregions of VLPFC. Our results demonstrate a ventral-dorsal division within IFG. Ventral IFG as a whole connects broadly to lateral temporal cortex. Although several different individual white matter tracts form connections between ventral IFG and lateral temporal cortex, functional connectivity analysis of fMRI data indicates that these are part of the same ventral functional network. By contrast, across subdivisions, dorsal IFG was connected with the midfrontal gyrus and correlated as a separate dorsal functional network. These qualitative differences in white matter organization within larger macroanatomical subregions of VLPFC support prior functional distinctions among these regions observed in task-based and functional connectivity fMRI studies. These results are consistent with the proposal that anatomical connectivity is a crucial determinant of systems-level functional organization of frontal cortex and the brain in general.

Clarifying Human White Matter

Progress in magnetic resonance imaging (MRI) now makes it possible to identify the major white matter tracts in the living human brain. These tracts are important because they carry many of the signals communicated between different brain regions. MRI methods coupled with biophysical modeling can measure the tissue properties and structural features of the tracts that impact our ability to think, feel, and perceive. This review describes the fundamental ideas of the MRI methods used to identify the major white matter tracts in the living human brain.

Different neural substrates for executive functions in youths with ADHD: a diffusion spectrum imaging tractography study

BACKGROUND

The relationship between white-matter tracts and executive functions (EF) in attention deficit hyperactivity disorder (ADHD) has not been well studied and previous studies mainly focused on frontostriatal (FS) tracts. The authors explored the microstructural property of several fibre tracts hypothesized to be involved in EF, to correlate their microstructural property with EF, and to explore whether such associations differ between ADHD and typically developing (TD) youths.

METHOD

We assessed 45 youths with ADHD and 45 individually matched TD youths with a computerized test battery for multiple dimensions of EF. From magnetic resonance imaging, FS tract, superior longitudinal fasciculus (SLF), arcuate fasciculus (AF) and cingulum bundle (CB) were reconstructed by diffusion spectrum imaging tractography. The generalized fractional anisotropy (GFA) values of white-matter tracts were computed to present microstructural property of each tract.

RESULTS

We found lower GFA in the left FS tract, left SLF, left AF and right CB, and poorer performance in set-shifting, sustained attention, cognitive inhibition and visuospatial planning in ADHD than TD. The ADHD and TD groups demonstrated different association patterns between EF and fibre tract microstructural property. Most of the EF were associated with microstructural integrity of the FS tract and CB in TD youths, while with that of the FS tract, SLF and AF in youths with ADHD.

CONCLUSIONS

Our findings support that the SLF, AF and CB also involve in a wide range of EF and that the main fibre tracts involved in EF are different in youths with ADHD.

Radial q-space sampling for DSI

PURPOSE

Diffusion spectrum imaging (DSI) has been shown to be an effective tool for noninvasively depicting the anatomical details of brain microstructure. Existing implementations of DSI sample the diffusion encoding space using a rectangular grid. Here we present a different implementation of DSI whereby a radially symmetric q-space sampling scheme for DSI is used to improve the angular resolution and accuracy of the reconstructed orientation distribution functions.

METHODS

Q-space is sampled by acquiring several q-space samples along a number of radial lines. Each of these radial lines in q-space is analytically connected to a value of the orientation distribution functions at the same angular location by the Fourier slice theorem.

RESULTS

Computer simulations and in vivo brain results demonstrate that radial diffusion spectrum imaging correctly estimates the orientation distribution functions when moderately high b-values (4000 s/mm2) and number of q-space samples (236) are used.

CONCLUSION

The nominal angular resolution of radial diffusion spectrum imaging depends on the number of radial lines used in the sampling scheme, and only weakly on the maximum b-value. In addition, the radial analytical reconstruction reduces truncation artifacts which affect Cartesian reconstructions. Hence, a radial acquisition of q-space can be favorable for DSI. Magn Reson Med 76:769-780, 2016. © 2015 Wiley Periodicals, Inc.

NTU-DSI-122: A diffusion spectrum imaging template with high anatomical matching to the ICBM-152 space

A diffusion-weighted (DW) template in a standard coordinate system is often necessary for the analysis of white matter (WM) structures using DW images. Although several DW templates have been constructed in the ICBM-152 space, a template for diffusion spectrum imaging (DSI) is still lacking. In this study, we developed a DSI template in the ICBM-152 space from 122 healthy adults. This high quality template, NTU-DSI-122, was built through incorporating the macroscopic anatomical information using high-resolution T1 -weighted images and the microscopic structural information obtained from DSI datasets. Two evaluations were conducted to examine the quality of NTU-DSI-122. The first evaluation examined the anatomical consistency of NTU-DSI-122 in matching to the ICBM-152 coordinate system. The results showed that this template matched to the ICBM-152 templates very well across the whole brain, not only in the deep white matter regions as other DW templates but also in the superficial white matter regions. In the second evaluation, a large number of independent diffusion tensor imaging (DTI) datasets were registered to the DTI template derived from NTU-DSI-122. The examination was performed by quantifying the anatomical consistency among the registered DTI datasets. The results showed that using NTU-DSI-122 as the registration template the registered DTI datasets can achieve high anatomical alignment. Both evaluations demonstrate that NTU-DSI-122 is a useful high quality DW template. Therefore, NTU-DSI-122 can serve as a representative DSI dataset for a healthy adult population, and will be of potential value for brain research and clinical applications. The NTU-DSI-122 template is available at http://www.nitrc.org/projects/ntu-dsi-122/.

Automatic whole brain tract-based analysis using predefined tracts in a diffusion spectrum imaging template and an accurate registration strategy

Automated tract-based analysis of diffusion MRI is an important tool for investigating tract integrity of the cerebral white matter. Current template-based automatic analyses still lack a comprehensive list of tract atlas and an accurate registration method. In this study, tract-based automatic analysis (TBAA) was developed to meet the demands. Seventy-six major white matter tracts were reconstructed on a high-quality diffusion spectrum imaging (DSI) template, and an advanced two-step registration strategy was proposed by incorporating anatomical information of the gray matter from T1-weighted images in addition to microstructural information of the white matter from diffusion-weighted images. The automatic analysis was achieved by establishing a transformation between the DSI template and DSI dataset of the subject derived from the registration strategy. The tract coordinates in the template were transformed to native space in the individual's DSI dataset, and the microstructural properties of major tract bundles were sampled stepwise along the tract coordinates of the subject's DSI dataset. In a validation study of eight well-known tracts, our results showed that TBAA had high geometric agreement with manual tracts in both deep and superficial parts but significantly smaller measurement variability than manual method in functional difference. Additionally, the feasibility of the method was demonstrated by showing tracts with altered microstructural properties in patients with schizophrenia. Fifteen major tract bundles were found to have significant differences after controlling the family-wise error rate. In conclusion, the proposed TBAA method is potentially useful in brain-wise investigations of white matter tracts, particularly for a large cohort study.

Altered Cortical Thickness and Tract Integrity of the Mirror Neuron System and Associated Social Communication in Autism Spectrum Disorder

Previous studies using neural activity recording and neuroimaging techniques have reported functional deficits in the mirror neuron system (MNS) for individuals with autism spectrum disorder (ASD). However, a few studies focusing on gray and white matter structures of the MNS have yielded inconsistent results. The current study recruited adolescents and young adults with ASD (aged 15-26 years) and age-matched typically developing (TD) controls (aged 14-25 years). The cortical thickness (CT) and microstructural integrity of the tracts connecting the regions forming the classical MNS were investigated. High-resolution T1-weighted imaging and diffusion spectrum imaging were performed to quantify the CT and tract integrity, respectively. The structural covariance of the CT of the MNS regions revealed a weaker coordination of the MNS network in ASD. A strong correlation was found between the integrity of the right frontoparietal tracts and the social communication subscores measured by the Chinese version of the Social Communication Questionnaire. The results showed that there were no significant mean differences in the CTs and tract integrity between the ASD and TD groups, but revealed a moderate or even reverse age effect on the frontal MNS structures in ASD. In conclusion, aberrant structural coordination may be an underlying factor affecting the function of the MNS in ASD patients. The association between the right frontoparietal tracts and social communication performance implies a neural correlate of communication processing in the autistic brain. This study provides evidence of abnormal MNS structures and their influence on social communication in individuals with ASD.

Fractional anisotropy changes in Alzheimer's disease depend on the underlying fiber tract architecture: a multiparametric DTI study using joint independent component analysis

Diffusion tensor imaging (DTI) allows the simultaneous measurement of several diffusion indices that provide complementary information on the substrate of white matter alterations in neurodegenerative diseases. These indices include fractional anisotropy (FA) as measure of fiber tract integrity, and the mode of anisotropy (Mode) reflecting differences in the shape of the diffusion tensor. We used a multivariate approach based on joint independent component analysis of FA and Mode in a large sample of 138 subjects with Alzheimer's disease (AD) dementia, 37 subjects with cerebrospinal fluid biomarker positive mild cognitive impairment (MCI-AD), and 153 healthy elderly controls from the European DTI Study on Dementia to comprehensively study alterations of microstructural white matter integrity in AD dementia and predementia AD. We found a parallel decrease of FA and Mode in intracortically projecting fiber tracts, and a parallel increase of FA and Mode in the corticospinal tract in AD patients compared to controls. Subjects with MCI-AD showed a similar, but spatially more restricted pattern of diffusion changes. Our findings suggest an early axonal degeneration in intracortical projecting fiber tracts in dementia and predementia stages of AD. An increase of Mode, parallel to an increase of FA, in the corticospinal tract suggests a more linear shape of diffusion due to loss of crossing fibers along relatively preserved cortico-petal and cortico-fugal fiber tracts in AD. Supporting this interpretation, we found three populations of fiber tracts, namely cortico-petal and cortico-fugal, commissural, and intrahemispherically projecting fiber tracts, in the peak area of parallel FA and Mode increase.

Altered integrity of the right arcuate fasciculus as a trait marker of schizophrenia: a sibling study using tractography-based analysis of the whole brain

Trait markers of schizophrenia aid the dissection of the heterogeneous phenotypes into distinct subtypes and facilitate the genetic underpinning of the disease. The microstructural integrity of the white matter tracts could serve as a trait marker of schizophrenia, and tractography-based analysis (TBA) is the current method of choice. Manual tractography is time-consuming and limits the analysis to preselected fiber tracts. Here, we sought to identify a trait marker of schizophrenia from among 74 fiber tracts across the whole brain using a novel automatic TBA method. Thirty-one patients with schizophrenia, 31 unaffected siblings and 31 healthy controls were recruited to undergo diffusion spectrum magnetic resonance imaging at 3T. Generalized fractional anisotropy (GFA), an index reflecting tract integrity, was computed for each tract and compared among the three groups. Ten tracts were found to exhibit significant differences between the groups with a linear, stepwise order from controls to siblings to patients; they included the right arcuate fasciculus, bilateral fornices, bilateral auditory tracts, left optic radiation, the genu of the corpus callosum, and the corpus callosum to the bilateral dorsolateral prefrontal cortices, bilateral temporal poles, and bilateral hippocampi. Posthoc between-group analyses revealed that the GFA of the right arcuate fasciculus was significantly decreased in both the patients and unaffected siblings compared to the controls. Furthermore, the GFA of the right arcuate fasciculus exhibited a trend toward positive symptom scores. In conclusion, the right arcuate fasciculus may be a candidate trait marker and deserves further study to verify any genetic association.

Characterizing the connectome in schizophrenia with diffusion spectrum imaging

Schizophrenia is a complex psychiatric disorder characterized by disabling symptoms and cognitive deficit. Recent neuroimaging findings suggest that large parts of the brain are affected by the disease, and that the capacity of functional integration between brain areas is decreased. In this study we questioned (i) which brain areas underlie the loss of network integration properties observed in the pathology, (ii) what is the topological role of the affected regions within the overall brain network and how this topological status might be altered in patients, and (iii) how white matter properties of tracts connecting affected regions may be disrupted. We acquired diffusion spectrum imaging (a technique sensitive to fiber crossing and slow diffusion compartment) data from 16 schizophrenia patients and 15 healthy controls, and investigated their weighted brain networks. The global connectivity analysis confirmed that patients present disrupted integration and segregation properties. The nodal analysis allowed identifying a distributed set of brain nodes affected in the pathology, including hubs and peripheral areas. To characterize the topological role of this affected core, we investigated the brain network shortest paths layout, and quantified the network damage after targeted attack toward the affected core. The centrality of the affected core was compromised in patients. Moreover the connectivity strength within the affected core, quantified with generalized fractional anisotropy and apparent diffusion coefficient, was altered in patients. Taken together, these findings suggest that the structural alterations and topological decentralization of the affected core might be major mechanisms underlying the schizophrenia dysconnectivity disorder.

Use of diffusion spectrum imaging in preliminary longitudinal evaluation of amyotrophic lateral sclerosis: development of an imaging biomarker

Previous diffusion tensor imaging (DTI) studies have shown white matter pathology in amyotrophic lateral sclerosis (ALS), predominantly in the motor pathways. Further these studies have shown that DTI can be used longitudinally to track pathology over time, making white matter pathology a candidate as an outcome measure in future trials. DTI has demonstrated application in group studies, however its derived indices, for example fractional anisotropy, are susceptible to partial volume effects, making its role questionable in examining individual progression. We hypothesize that changes in the white matter are present in ALS beyond the motor tracts, and that the affected pathways and associated pattern of disease progression can be tracked longitudinally using automated diffusion connectometry analysis. Connectometry analysis is based on diffusion spectrum imaging and overcomes the limitations of a conventional tractography approach and DTI. The identified affected white matter tracts can then be assessed in a targeted fashion using High definition fiber tractography (a novel white matter MR imaging technique). Changes in quantitative and qualitative markers over time could then be correlated with clinical progression. We illustrate these principles toward developing an imaging biomarker for demonstrating individual progression, by presenting results for five ALS patients, including with longitudinal data in two. Preliminary analysis demonstrated a number of changes bilaterally and asymmetrically in motoric and extramotoric white matter pathways. Further the limbic system was also affected possibly explaining the cognitive symptoms in ALS. In the two longitudinal subjects, the white matter changes were less extensive at baseline, although there was evidence of disease progression in a frontal pattern with a relatively spared postcentral gyrus, consistent with the known pathology in ALS.

Comparison of sampling strategies and sparsifying transforms to improve compressed sensing diffusion spectrum imaging

PURPOSE

Diffusion Spectrum Imaging enables to reconstruct the ensemble average propagator (EAP) at the expense of having to acquire a large number of measurements. Compressive sensing offers an efficient way to decrease the required number of measurements. The purpose of this work is to perform a thorough experimental comparison of three sampling strategies and six sparsifying transforms to show their impact when applied to accelerate compressive sensing-diffusion spectrum imaging.

METHODS

We propose a novel sampling scheme that assures uniform angular and random radial q-space samples. We also compare and implement six discrete sparse representations of the EAP and thoroughly evaluate them on synthetic and real data using metrics from the full EAP, kurtosis, and orientation distribution function.

RESULTS

The discrete wavelet transform with Cohen-Daubechies-Feauveau 9/7 wavelets and uniform angular sampling in combination with random radial sampling showed to be better than other tested techniques to accurately reconstruct the EAP and its features.

CONCLUSION

It is important to jointly optimize the sampling scheme and the sparsifying transform to obtain accelerated compressive sensing-diffusion spectrum imaging. Experiments on synthetic and real human brain data show that one can robustly recover both radial and angular EAP features while undersampling the acquisition to 64 measurements (undersampling factor of 4).

Decline of fiber tract integrity over the adult age range: a diffusion spectrum imaging study

PURPOSE

We applied a novel diffusion spectrum imaging (DSI) acquisition to determine associations between aging and subcortical fiber tract integrity.

MATERIALS AND METHODS

We studied 35 cognitively healthy subjects (17 women), spanning the adult age range between 23 and 77 years, using anatomical MRI and a novel DSI acquisition scheme at 3 Tesla. The study was approved by the local institutional review board. DSI data were analyzed using tractography and complementary voxel-based analysis of generalized fractional anisotropy (GFA) maps. We determined the effects of age on generalized fractional anisotropy in selected fiber tracts as well as in a whole brain voxel-based analysis. For comparison, we studied the effects of age on regional gray and white matter volumes.

RESULTS

We found a significant reduction in anterior corpus callosum fiber tract integrity with age (P < 0.001), as well as significant GFA reduction throughout the subcortical white matter (P < 0.05, false discovery rate [FDR] corrected). GFA decline was accompanied by significant gray matter atrophy in frontal and temporal association cortex (P < 0.05, FDR corrected).

CONCLUSION

Our data suggest that normal aging leads to a regionally specific decline in fiber tract integrity. DSI may become a useful biomarker in healthy and pathological aging.

Fast dictionary-based reconstruction for diffusion spectrum imaging

Diffusion spectrum imaging reveals detailed local diffusion properties at the expense of substantially long imaging times. It is possible to accelerate acquisition by undersampling in q-space, followed by image reconstruction that exploits prior knowledge on the diffusion probability density functions (pdfs). Previously proposed methods impose this prior in the form of sparsity under wavelet and total variation transforms, or under adaptive dictionaries that are trained on example datasets to maximize the sparsity of the representation. These compressed sensing (CS) methods require full-brain processing times on the order of hours using MATLAB running on a workstation. This work presents two dictionary-based reconstruction techniques that use analytical solutions, and are two orders of magnitude faster than the previously proposed dictionary-based CS approach. The first method generates a dictionary from the training data using principal component analysis (PCA), and performs the reconstruction in the PCA space. The second proposed method applies reconstruction using pseudoinverse with Tikhonov regularization with respect to a dictionary. This dictionary can either be obtained using the K-SVD algorithm, or it can simply be the training dataset of pdfs without any training. All of the proposed methods achieve reconstruction times on the order of seconds per imaging slice, and have reconstruction quality comparable to that of dictionary-based CS algorithm.

Auditory tracts identified with combined fMRI and diffusion tractography

The auditory tracts in the human brain connect the inferior colliculus (IC) and medial geniculate body (MGB) to various components of the auditory cortex (AC). While in non-human primates and in humans, the auditory system is differentiated in core, belt and parabelt areas, the correspondence between these areas and anatomical landmarks on the human superior temporal gyri is not straightforward, and at present not completely understood. However it is not controversial that there is a hierarchical organization of auditory stimuli processing in the auditory system. The aims of this study were to demonstrate that it is possible to non-invasively and robustly identify auditory projections between the auditory thalamus/brainstem and different functional levels of auditory analysis in the cortex of human subjects in vivo combining functional magnetic resonance imaging (fMRI) with diffusion MRI, and to investigate the possibility of differentiating between different components of the auditory pathways (e.g. projections to areas responsible for sound, pitch and melody processing). We hypothesized that the major limitation in the identification of the auditory pathways is the known problem of crossing fibres and addressed this issue acquiring DTI with b-values higher than commonly used and adopting a multi-fibre ball-and-stick analysis model combined with probabilistic tractography. Fourteen healthy subjects were studied. Auditory areas were localized functionally using an established hierarchical pitch processing fMRI paradigm. Together fMRI and diffusion MRI allowed the successful identification of tracts connecting IC with AC in 64 to 86% of hemispheres and left sound areas with homologous areas in the right hemisphere in 86% of hemispheres. The identified tracts corresponded closely with a three-dimensional stereotaxic atlas based on postmortem data. The findings have both neuroscientific and clinical implications for delineation of the human auditory system in vivo.

The effects of music on brain functional networks: a network analysis

The human brain can dynamically adapt to the changing surroundings. To explore this issue, we adopted graph theoretical tools to examine changes in electroencephalography (EEG) functional networks while listening to music. Three different excerpts of Chinese Guqin music were played to 16 non-musician subjects. For the main frequency intervals, synchronizations between all pair-wise combinations of EEG electrodes were evaluated with phase lag index (PLI). Then, weighted connectivity networks were created and their organizations were characterized in terms of an average clustering coefficient and characteristic path length. We found an enhanced synchronization level in the alpha2 band during music listening. Music perception showed a decrease of both normalized clustering coefficient and path length in the alpha2 band. Moreover, differences in network measures were not observed between musical excerpts. These experimental results demonstrate an increase of functional connectivity as well as a more random network structure in the alpha2 band during music perception. The present study offers support for the effects of music on human brain functional networks with a trend toward a more efficient but less economical architecture.

Computation of diffusion function measures in q-space using magnetic resonance hybrid diffusion imaging

The distribution of water diffusion in biological tissues may be estimated by a 3-D Fourier transform (FT) of diffusion-weighted measurements in q-space. In this study, methods for estimating diffusion spectrum measures (the zero-displacement probability, the mean-squared displacement, and the orientation distribution function) directly from the q-space signals are described. These methods were evaluated using both computer simulations and hybrid diffusion imaging (HYDI) measurements on a human brain. The HYDI method obtains diffusion-weighted measurements on concentric spheres in q-space. Monte Carlo computer simulations were performed to investigate effects of noise, q-space truncation, and sampling interval on the measures. This new direct computation approach reduces HYDI data processing time and image artifacts arising from 3-D FT and regridding interpolation. In addition, it is less sensitive to the noise and q-space truncation effects than conventional approach. Although this study focused on data using the HYDI scheme, this computation approach may be applied to other diffusion sampling schemes including Cartesian diffusion spectrum imaging.