White matter microstructure alterations of the posterior limb of internal capsule in first-episode drug naive schizophrenia patients.

Objectives: Previous studies have shown that microstructural alterations in white matter (WM) could contribute to the symptom manifestation and support the dysconnectivity hypothesis in schizophrenia patients. These alterations were pervasive, non-specific, and reported inconsistently across the literature. This study aimed to specifically investigate the microstructure alterations of the posterior limb of the internal capsule (PLIC) in first-episode, drug-naive schizophrenia patients.

Whole brain atlas-based diffusion kurtosis imaging parameters for the evaluation of multiple cognitive-related brain microstructure injuries after radiotherapy in lung cancer patients with brain metastasis.

Background: Whole brain radiation therapy (WBRT) can cause cognitive dysfunctions in lung cancer patients with brain metastasis (BM). Diffusion kurtosis imaging (DKI) can detect brain microstructural alterations sensitivly. We aimed to identify the potential of DKI parameters for early radiation-induced brain injury and investigate the association between microstructure changes and neurocognitive function (NCF) decline.

Increased Free Water in the Putamen in Idiopathic REM Sleep Behavior Disorder.

Background: It has been suggested that the loss of nigrostriatal dopaminergic axon terminals occurs before the loss of dopaminergic neurons in the substantia nigra (SN) in Parkinson's disease (PD). This study aimed to use free-water imaging to evaluate microstructural changes in the dorsoposterior putamen (DPP) of idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) patients, which is considered a prodromal stage of synucleinopathies.

Methods: Free water values in the DPP, dorsoanterior putamen (DAP), and posterior SN were compared between the healthy controls (n = 48), iRBD (n = 43) and PD (n = 47) patients.

Free-Water Imaging of the Substantia Nigra in GBA Pathogenic Variant Carriers.

Background: Pathogenic variants in the glucocerebrosidase gene (GBA) have been identified as the most common genetic risk factor for Parkinson's disease (PD). However, the features of substantia nigra damage in GBA pathogenic variant carriers remain unclear.

Objective: We aimed to evaluate the microstructural changes in the substantia nigra in non-manifesting GBA pathogenic variant carriers (GBA-NMC) and PD patients with GBA pathogenic variant (GBA-PD) with free-water imaging.

Optimized three-dimensional ultrashort echo time: Magnetic resonance fingerprinting for myelin tissue fraction mapping.

Quantitative assessment of brain myelination has gained attention for both research and diagnosis of neurological diseases. However, conventional pulse sequences cannot directly acquire the myelin-proton signals due to its extremely short T2 and T2* values. To obtain the myelin-proton signals, dedicated short T2 acquisition techniques, such as ultrashort echo time (UTE) imaging, have been introduced.

Quantitative Susceptibility Mapping and Free Water Imaging of Substantia Nigra in Parkinson's Disease.

Background: The utility of imaging methods to detect iron content in the substantia nigra pars compacta (SNc) and free water imaging in the posterior substantia nigra (pSN) has the potential to be imaging markers for the detection of Parkinson's disease (PD).

Objective: This study aimed to compare the discriminative power of above methods, and whether the combination can improve the diagnostic potential of PD.

Methods: Quantitative susceptibility mapping (QSM) and diffusion-weighted data were obtained from 41 healthy controls (HC), 37 patients with idiopathic REM sleep behavior disorder (RBD), and 65 patients with PD.

Increased Free Water in the Substantia Nigra in Asymptomatic LRRK2 G2019S Mutation Carriers.

Background: The alteration of substantia nigra (SN) degeneration in populations at risk of Parkinson's disease (PD) is unclear.

Objective: We investigated free water (FW) values in the posterior SN (pSN) in asymptomatic LRRK2 G2019S mutation carriers.

Methods: We analyzed diffusion imaging data from 28 asymptomatic LRRK2 G2019S mutation carriers and 30 healthy controls (HCs), whereas 11 asymptomatic LRRK2 G2019S carriers and 11 HCs were followed up.

A deep learning-based multisite neuroimage harmonization framework established with a traveling-subject dataset.

The accumulation of multisite large-sample MRI datasets collected during large brain research projects in the last decade has provided critical resources for understanding the neurobiological mechanisms underlying cognitive functions and brain disorders. However, the significant site effects observed in imaging data and their derived structural and functional features have prevented the derivation of consistent findings across multiple studies. The development of harmonization methods that can effectively eliminate complex site effects while maintaining biological characteristics in neuroimaging data has become a vital and urgent requirement for multisite imaging studies.

Convolutional neural network optimizes the application of diffusion kurtosis imaging in Parkinson's disease.

Objectives: The literature regarding the use of diffusion-tensor imaging-derived metrics in the evaluation of Parkinson's disease (PD) is controversial. This study attempted to assess the feasibility of a deep-learning-based method for detecting alterations in diffusion kurtosis measurements associated with PD.

Methods: A total of 68 patients with PD and 77 healthy controls were scanned using scanner-A (3 T Skyra) (DATASET-1).

Reproducibility of volume and asymmetry measurements of hippocampus, amygdala, and entorhinal cortex on traveling volunteers: a multisite MP2RAGE prospective study.

Background: Multisite studies can considerably increase the pool of normally aging individuals with neurodegenerative disorders and thereby expedite the associated research. Understanding the reproducibility of the parameters of related brain structures-including the hippocampus, amygdala, and entorhinal cortex-in multisite studies is crucial in determining the impact of healthy aging or neurodegenerative diseases.

Purpose: To estimate the reproducibility of the fascinating structures by automatic (FreeSurfer) and manual segmentation methods in a well-controlled multisite dataset.

Deep learning-based method for reducing residual motion effects in diffusion parameter estimation.

Purpose: Conventional motion-correction techniques for diffusion MRI can introduce motion-level-dependent bias in derived metrics. To address this challenge, a deep learning-based technique was developed to minimize such residual motion effects.

Methods: The data-rejection approach was adopted in which motion-corrupted data are discarded before model-fitting.

Evaluation of the diffusion MRI white matter tract integrity model using myelin histology and Monte-Carlo simulations.

Quantitative evaluation of brain myelination has drawn considerable attention. Conventional diffusion-based magnetic resonance imaging models, including diffusion tensor imaging and diffusion kurtosis imaging (DKI), have been used to infer the microstructure and its changes in neurological diseases. White matter tract integrity (WMTI) was proposed as a biophysical model to relate the DKI-derived metrics to the underlying microstructure.

A deep learning-based method for improving reliability of multicenter diffusion kurtosis imaging with varied acquisition protocols.

Multicenter magnetic resonance imaging is gaining more popularity in large-sample projects. Since both varying hardware and software across different centers cause unavoidable data heterogeneity across centers, its impact on reliability in study outcomes has also drawn much attention recently. One fundamental issue arises in how to derive model parameters reliably from image data of varying quality.

Multicenter dataset of multi-shell diffusion MRI in healthy traveling adults with identical settings.

Multicenter diffusion magnetic resonance imaging (MRI) has drawn great attention recently due to the expanding need for large-scale brain imaging studies, whereas the variability in MRI scanners and data acquisition tends to confound reliable individual-based analysis of diffusion measures. In addition, a growing number of multi-shell diffusion models have been shown with the potential to generate various estimates of physio-pathological information, yet their reliability and reproducibility in multicenter studies remain to be assessed. In this article, we describe a multi-shell diffusion dataset collected from three traveling subjects with identical acquisition settings in ten imaging centers.

MTE-NODDI: Multi-TE NODDI for disentangling non-T2-weighted signal fractions from compartment-specific T2 relaxation times.

Neurite orientation dispersion and density imaging (NODDI) has become a popular diffusion MRI technique for investigating microstructural alternations during brain development, maturation and aging in health and disease. However, the NODDI model of diffusion does not explicitly account for compartment-specific T2 relaxation and its model parameters are usually estimated from data acquired with a single echo time (TE). Thus, the NODDI-derived measures, such as the intra-neurite signal fraction, also known as the neurite density index, could be T2-weighted and TE-dependent.

Fast learning of fiber orientation distribution function for MR tractography using convolutional neural network.

Purpose: In diffusion-weighted magnetic resonance imaging (DW-MRI), the fiber orientation distribution function (fODF) is of great importance for solving complex fiber configurations to achieve reliable tractography throughout the brain, which ultimately facilitates the understanding of brain connectivity and exploration of neurological dysfunction. Recently, multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) method has been explored for reconstructing full fODFs. To achieve a reliable fitting, similar to other model-based approaches, a large number of diffusion measurements is typically required for MSMT-CSD method.

Reproducibility of multi-shell diffusion tractography on traveling subjects: A multicenter study prospective.

Reproducibility of multicenter diffusion magnetic resonance imaging has drawn more attention recently due to rapidly increasing need for large-size brain imaging studies. Advanced multi-shell diffusion models are recommended for their potentials to provide variety of physio-pathological information. While previous studies have investigated the consistency of single-shell diffusion acquisition from various hardware and protocols, a well-controlled study with multi-shell acquisition would be necessary to understand the inherent factors of reproducibility from new complexity of such acquisition protocol.

Effect of myelin water exchange on DTI-derived parameters in diffusion MRI: Elucidation of TE dependence.

Purpose: Water exchange exists between different neuronal compartments of brain tissue but is often ignored in most diffusion models. The goal of the current study was to demonstrate the dependence of diffusion measurements on echo time (TE) in the human brain and to investigate the underlying effects of myelin water exchange.

Methods: Five healthy subjects were examined with single-shot pulsed-gradient spin-echo echo-planar imaging with fixed duration (δ) and separation (Δ) of diffusion gradient pulses and a set of varying TEs.