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Brain age prediction models using diffusion magnetic resonance imaging (dMRI) and machine learning techniques enable individual assessment of brain aging status in healthy people and patients with brain disorders. However, dMRI data are notorious for high intersite variability, prohibiting direct application of a model to the datasets obtained from other sites. In this study, we generalized the dMRI-based brain age model to different dMRI datasets acquired under different imaging conditions. Specifically, we adopted a transfer learning approach to achieve domain adaptation. To evaluate the performance of transferred models, brain age prediction models were constructed using a large dMRI dataset as the source domain, and the models were transferred to three target domains with distinct acquisition scenarios. The experiments were performed to investigate (1) the tuning data size needed to achieve satisfactory performance for brain age prediction, (2) the feature types suitable for different dMRI acquisition scenarios, and (3) performance of the transfer learning approach compared with the statistical covariate approach. By tuning the models with relatively small data size and certain feature types, optimal transferred models were obtained with significantly improved prediction performance in all three target cohorts (p ​< ​0.001). The mean absolute error of the predicted age was reduced from 13.89 to 4.78 years in Cohort 1, 8.34 to 5.35 years in Cohort 2, and 8.74 to 5.64 years in Cohort 3. The test–retest reliability of the transferred model was verified using dMRI data acquired at two timepoints (intraclass correlation coefficient ​= ​0.950). Clinical sensitivity of the brain age prediction model was investigated by estimating the brain age in patients with schizophrenia. The prediction made by the transferred model was not significantly different from that made by the reference model. Both models predicted significant brain aging in patients with schizophrenia as compared with healthy controls (p ​< ​0.001); the predicted age difference of the transferred model was 4.63 and 0.26 years for patients and controls, respectively, and that of the reference model was 4.39 and −0.09 years, respectively. In conclusion, transfer learning approach is an efficient way to generalize the dMRI-based brain age prediction model. Appropriate transfer learning approach and suitable tuning data size should be chosen according to different dMRI acquisition scenarios.

Numerous studies have reported that long‐term musical training can affect brain functionality and induce structural alterations in the brain. Singing is a form of vocal musical expression with an unparalleled capacity for communicating emotion; however, there has been relatively little research on neuroplasticity at the network level in vocalists (i.e., noninstrumental musicians). Our objective in this study was to elucidate changes in the neural network architecture following long‐term training in the musical arts. We employed a framework based on graph theory to depict the connectivity and efficiency of structural networks in the brain, based on diffusion‐weighted images obtained from 35 vocalists, 27 pianists, and 33 nonmusicians. Our results revealed that musical training (both voice and piano) could enhance connectivity among emotion‐related regions of the brain, such as the amygdala. We also discovered that voice training reshaped the architecture of experience‐dependent networks, such as those involved in vocal motor control, sensory feedback, and language processing. It appears that vocal‐related changes in areas such as the insula, paracentral lobule, supramarginal gyrus, and putamen are associated with functional segregation, multisensory integration, and enhanced network interconnectivity. These results suggest that long‐term musical training can strengthen or prune white matter connectivity networks in an experience‐dependent manner. Musical training (both voice and piano) could enhance connectivity among emotion‐related regions of the brain, such as the amygdala. Voice training reshaped the architecture of experience‐dependent networks, such as those involved in vocal motor control, sensory feedback, and language processing. Long‐term musical training can strengthen or prune white matter connectivity networks in an experience‐dependent manner.

The neural substrate for clinical symptoms and neuropsychological performance in individuals with attention-deficit/hyperactivity disorder (ADHD) has rarely been studied and has yielded inconsistent results. We sought to compare the microstructural property of fibre tracts associated with the prefrontal cortex and its association with ADHD symptoms and a wide range of attention performance in youth with ADHD and healthy controls. We assessed youths with ADHD and age-, sex-, handedness-, coil- and intelligence-matched controls using the Conners’ Continuous Performance Test (CCPT) for attention performance and MRI. The 10 target tracts, including the bilateral frontostriatal tracts (caudate to dorsolateral prefrontal cortex, ventrolateral prefrontal cortex and orbitofrontal cortex), superior longitudinal fasciculus (SLF) and cingulum bundle were reconstructed using diffusion spectrum imaging tractography. We computed generalized fractional anisotropy (GFA) values to indicate tract-specific microstructural property. We included 50 youths with ADHD and 50 healthy controls in our study. Youths with ADHD had lower GFA in the left frontostriatal tracts, bilateral SLF and right cingulum bundle and performed worse in the CCPT than controls. Furthermore, alteration of the right SLF GFA was most significantly associated with the clinical symptom of inattention in youths with ADHD. Finally, youths with ADHD had differential association patterns of the 10 fibre tract GFA values with attention performance compared with controls. Ten of the youths with ADHD were treated with methylphenidate, which may have long-term effects on microstructural property. Our study highlights the importance of the SLF, cingulum bundle and frontostriatal tracts for clinical symptoms and attention performance in youths with ADHD and demonstrates the involvement of different fibre tracts in attention performance in these individuals.

We present a high angular resolution brain atlas constructed by averaging 90 diffusion spectrum imaging (DSI) datasets in the ICBM-152 space. The spatial normalization of the diffusion information was conducted by a novel q-space diffeomorphic reconstruction method, which reconstructed the spin distribution function (SDF) in the ICBM-152 space from the diffusion MR signals. The performance of this method was examined by a simulation study modeling nonlinear transformation. The result showed that the reconstructed SDFs can resolve crossing fibers and that the accumulated quantitative anisotropy can reveal the relative ratio of the fiber populations. In the in vivo study, the SDF of the constructed atlas was shown to resolve crossing fiber orientations. Further, fiber tracking showed that the atlas can be used to present the pathways of fiber bundles, and the termination locations of the fibers can provide anatomical localization of the connected cortical regions. This high angular resolution brain atlas may facilitate future connectome research on the complex structure of the human brain. ► A diffusion MRI brain atlas is constructed from 90 DSI datasets. ► Q-space diffeomorphic reconstruction is used to conduct the normalization. ► Our method can preserve crossing fiber orientations after spatial normalization. ► The NTU-90 atlas can provide quantitative anisotropy mapping. ► The NTU-90 atlas can resolve individual fiber bundles in crossing regions.

Diffusion MRI tractography has emerged as a useful and popular tool for mapping connections between brain regions. In this study, we examined the performance of quantitative anisotropy (QA) in facilitating deterministic fiber tracking. Two phantom studies were conducted. The first phantom study examined the susceptibility of fractional anisotropy (FA), generalized factional anisotropy (GFA), and QA to various partial volume effects. The second phantom study examined the spatial resolution of the FA-aided, GFA-aided, and QA-aided tractographies. An in vivo study was conducted to track the arcuate fasciculus, and two neurosurgeons blind to the acquisition and analysis settings were invited to identify false tracks. The performance of QA in assisting fiber tracking was compared with FA, GFA, and anatomical information from T1-weighted images. Our first phantom study showed that QA is less sensitive to the partial volume effects of crossing fibers and free water, suggesting that it is a robust index. The second phantom study showed that the QA-aided tractography has better resolution than the FA-aided and GFA-aided tractography. Our in vivo study further showed that the QA-aided tractography outperforms the FA-aided, GFA-aided, and anatomy-aided tractographies. In the shell scheme (HARDI), the FA-aided, GFA-aided, and anatomy-aided tractographies have 30.7%, 32.6%, and 24.45% of the false tracks, respectively, while the QA-aided tractography has 16.2%. In the grid scheme (DSI), the FA-aided, GFA-aided, and anatomy-aided tractographies have 12.3%, 9.0%, and 10.93% of the false tracks, respectively, while the QA-aided tractography has 4.43%. The QA-aided deterministic fiber tracking may assist fiber tracking studies and facilitate the advancement of human connectomics.

A diffusion deconvolution method is proposed to apply deconvolution to the diffusion orientation distribution function (dODF) and calculate the fiber orientation distribution function (fODF), which is defined as the orientation distribution of the fiber spin density. The dODF can be obtained from q-space imaging methods such as q-ball imaging (QBI), diffusion spectrum imaging (DSI), and generalized q-sampling imaging (GQI), and thus the method can be applied to various diffusion sampling schemes. A phantom study was conducted to compare the angular resolution of the fODF with the dODF, and the in vivo datasets were acquired using single-shell, two-shell, and grid sampling schemes, which were then reconstructed by QBI, GQI, and DSI, respectively. The phantom study showed that the fODF significantly improved the angular resolution over the dODF at 45- and 60-degree crossing angles. The in vivo study showed consistent fODF regardless of the applied sampling schemes and reconstruction methods, and the ability to resolve crossing fibers was improved in reduced sampling condition. The fiber spin density obtained from deconvolution showed a higher contrast-to-noise ratio than the fractional anisotropy (FA) mapping, and further application on tractography showed that the fiber spin density can be used to determine the termination of fiber tracts. In conclusion, the proposed deconvolution method is generally applicable to different q-space imaging methods. The calculated fODF improves the angular resolution and also provides a quantitative index of fiber spin density to refine fiber tracking. ► The phantom study showed significant improvement in angular resolution. ► The in vivo study showed improvement in angular resolution and fiber tracking. ► The in vivo study showed consistent fODF regardless of the diffusion sampling schemes. ► The fiber spin density showed a higher contrast-to-noise ratio than FA.

In this paper, we propose a registration-based algorithm to correct various distortions or artefacts (DACO) commonly observed in diffusion-weighted (DW) magnetic resonance images (MRI). The registration in DACO is accomplished by means of a pseudo b0 image, which is synthesized from the anatomical images such as T1-weighted image or T2-weighted image, and a pseudo diffusion MRI (dMRI) data, which is derived from the Gaussian model of diffusion tensor imaging (DTI) or the Hermite model of mean apparent propagator (MAP)-MRI. DACO corrects (1) the susceptibility-induced distortions and (2) the misalignment between the dMRI data and anatomical images by registering the real b0 image to the pseudo b0 image, and corrects (3) the eddy current-induced distortions and (4) the head motions by registering each image in the real dMRI data to the corresponding image in the pseudo dMRI data. DACO estimates the models of artefacts simultaneously in an iterative and interleaved manner. The mathematical formulation of the models and the estimation procedures are detailed in this paper. Using the human connectome project (HCP) data the evaluation shows that DACO could estimate the model parameters accurately. Furthermore, the evaluation conducted on the real human data acquired from clinical MRI scanners reveals that the method could reduce the artefacts effectively. The DACO method leverages the anatomical image, which is routinely acquired in clinical practice, to correct the artefacts, omitting the additional acquisitions needed to conduct the algorithm. Therefore, our method should be beneficial to most dMRI data, particularly to those acquired without field maps or reverse phase-encoding images.

Fiber orientation is the key information in diffusion tractography. Several deconvolution methods have been proposed to obtain fiber orientations by estimating a fiber orientation distribution function (ODF). However, the L 2 regularization used in deconvolution often leads to false fibers that compromise the specificity of the results. To address this problem, we propose a method called diffusion decomposition, which obtains a sparse solution of fiber ODF by decomposing the diffusion ODF obtained from q-ball imaging (QBI), diffusion spectrum imaging (DSI), or generalized q-sampling imaging (GQI). A simulation study, a phantom study, and an in-vivo study were conducted to examine the performance of diffusion decomposition. The simulation study showed that diffusion decomposition was more accurate than both constrained spherical deconvolution and ball-and-sticks model. The phantom study showed that the angular error of diffusion decomposition was significantly lower than those of constrained spherical deconvolution at 30° crossing and ball-and-sticks model at 60° crossing. The in-vivo study showed that diffusion decomposition can be applied to QBI, DSI, or GQI, and the resolved fiber orientations were consistent regardless of the diffusion sampling schemes and diffusion reconstruction methods. The performance of diffusion decomposition was further demonstrated by resolving crossing fibers on a 30-direction QBI dataset and a 40-direction DSI dataset. In conclusion, diffusion decomposition can improve angular resolution and resolve crossing fibers in datasets with low SNR and substantially reduced number of diffusion encoding directions. These advantages may be valuable for human connectome studies and clinical research.

Parkinson’s disease (PD) is the second most common age-related neurodegenerative disease with cardinal motor symptoms. In addition to motor symptoms, PD is a heterogeneous disease accompanied by many non-motor symptoms that dominate the clinical manifestations in different stages or subtypes of PD, such as cognitive impairments. The heterogeneity of PD suggests widespread brain structural changes, and axonal involvement appears to be critical to the pathophysiology of PD. As α-synuclein pathology has been suggested to cause axonal changes followed by neuronal degeneration, diffusion tensor imaging (DTI) as an in vivo imaging technique emerges to characterize early detectable white matter changes due to PD. Here, we reviewed the past 5-year literature to show how DTI has helped identify axonal abnormalities at different PD stages or in different PD subtypes and atypical parkinsonism. We also showed the recent clinical utilities of DTI tractography in interventional treatments such as deep brain stimulation (DBS). Mounting evidence supported by multisite DTI data suggests that DTI along with the advanced analytic methods, can delineate dynamic pathophysiological processes from the early to late PD stages and differentiate distinct structural networks affected in PD and other parkinsonism syndromes. It indicates that DTI, along with recent advanced analytic methods, can assist future interventional studies in optimizing treatments for PD patients with different clinical conditions and risk profiles.

Mapping complex crossing fibers using diffusion MRI techniques requires adequate angular precision and accuracy. Beyond diffusion tensor imaging (DTI), high angular resolution sampling schemes such as diffusion spectrum imaging (DSI) and q-ball imaging (QBI) were proposed to resolve crossing fibers. These schemes require hundreds of data approximately five to ten times more than DTI, offsetting their clinical feasibility. To facilitate its clinical application, optimum values of highest diffusion sensitivity (bmax) must be investigated under the constraint of scan time and gradient performance. In this study, simulation of human data sets and a following verification experiment were performed to investigate the optimum bmax of DSI and QBI. Four sampling schemes, two with high sampling number, i.e., DSI515 and QBI493, and two with low sampling number, i.e., DSI203 and QBI253, were compared. Deviation angle and angular dispersion were used to evaluate the precision and accuracy among different bmax of each scheme. The results indicated that the optimum bmax was a trade-off between SNR and angular resolution. At their own optimum bmax, the reduced sampling schemes yielded angular precision and accuracy comparable to the high sampling schemes. On our current 3 T system, the optimum bmax (s/mm 2) were 6500 for DSI515, 4000 for DSI203, 3000 for QBI493 and 2500 for QBI253. DSI was incrementally more accurate than QBI, but required a greater demand for gradient performance. In conclusion, our systematic study of optimum bmax in different sampling schemes and the consideration derived wherein could be helpful to determine optimum sampling schemes in other MRI systems.