Diffusion Tensor Imaging Features Research Articles

Multi-modality radiomics of conventional T1 weighted and diffusion tensor imaging for differentiating Parkinson’s disease motor subtypes in early-stages

This study aimed to develop and validate a multi-modality radiomics approach using T1-weighted and diffusion tensor imaging (DTI) to differentiate Parkinson's disease (PD) motor subtypes, specifically tremor-dominant (TD) and postural instability gait difficulty (PIGD), in early disease stages. We analyzed T1-weighted and DTI scans from 140 early-stage PD patients (70 TD, 70 PIGD) and 70 healthy controls from the Parkinson's Progression Markers Initiative database. Radiomics features were extracted from 16 brain regions of interest. After harmonization and feature selection, four machine learning classifiers were trained and evaluated for both three-class (HC vs TD vs PIGD) and binary (TD vs PIGD) classification tasks. The light gradient boosting machine (LGBM) classifier demonstrated the best overall performance. For the three-class classification, LGBM achieved an accuracy of 85% and an area under the receiver operating characteristic curve (AUC) of 0.94 using combined T1 and DTI features. In the binary classification task, LGBM reached an accuracy of 95% and AUC of 0.95. Key discriminative features were identified in the Thalamus, Amygdala, Hippocampus, and Substantia Nigra for the three-group classification, and in the Pallidum, Amygdala, Hippocampus, and Accumbens for binary classification. The combined T1 + DTI approach consistently outperformed single-modality classifications, with DTI alone showing particularly low performance (AUC 0.55–0.62) in binary classification. The high accuracy and AUC values suggest that this approach could significantly improve early diagnosis and subtyping of PD. These findings have important implications for clinical management, potentially enabling more personalized treatment strategies based on early, accurate subtype identification.

Prediction of cognitive decline in Parkinson's disease based on MRI radiomics and clinical features: A multicenter study.

To develop and validate a multimodal combinatorial model based on whole-brain magnetic resonance imaging (MRI) radiomic features for predicting cognitive decline in patients with Parkinson's disease (PD). This study included a total of 222 PD patients with normal baseline cognition, of whom 68 had cognitive impairment during a 4-year follow-up period. All patients underwent MRI scans, and radiomic features were extracted from the whole-brain MRI images of the training set, and dimensionality reduction was performed to construct a radiomics model. Subsequently, Screening predictive factors for cognitive decline from clinical features and then combining those with a radiomics model to construct a multimodal combinatorial model for predicting cognitive decline in PD patients. Evaluate the performance of the comprehensive model using the receiver-operating characteristic curve, confusion matrix, F1 score, and survival curve. In addition, the quantitative characteristics of diffusion tensor imaging (DTI) from corpus callosum were selected from 52 PD patients to further validate the clinical efficacy of the model. The multimodal combinatorial model has good classification performance, with areas under the curve of 0.842, 0.829, and 0.860 in the training, test, and validation sets, respectively. Significant differences were observed in the number of cognitive decline PD patients and corpus callosum-related DTI parameters between the low-risk and high-risk groups distinguished by the model (p < 0.05). The survival curve analysis showed a statistically significant difference in the progression time of mild cognitive impairment between the low-risk and the high-risk groups. The building of a multimodal combinatorial model based on radiomic features from MRI can predict cognitive decline in PD patients, thus providing adaptive strategies for clinical practice.

Presence of Auditory Pathway Abnormalities in Children With Neurofibromatosis Type 1 With Brainstem Focal Areas of Abnormal Signal Intensity: Diffusion Tensor Imaging Features.

Background: To investigate whether there is a difference in mean diffusivity (MD) and fractional anisotropy (FA) values in the auditory pathways of neurofibromatosis type 1 patients with and without focal areas of abnormal signal intensity (FASI) compared to healthy controls by using diffusion tensor imaging (DTI). Methods: Patients were classified as group 1 with focal areas of abnormal signal intensity in the brainstem, group 2 without focal areas of abnormal signal intensity, and healthy control group 3 according to the MRI findings. Mean diffusivity and fractional anisotropy values of lateral lemniscus, inferior colliculus, corpus geniculatum mediale, Heschl gyrus, and brainstem were compared between groups. The correlation between mean diffusivity and fractional anisotropy values of auditory pathways and age was investigated. Results: There was a significant difference between group 1 and group 2 in terms of mean diffusivity and fractional anisotropy values at lateral lemniscus, inferior colliculus, corpus geniculatum mediale, and Heschl gyrus. Increased mean diffusivity and decreased fractional anisotropy values at brainstem were found in group 1. There was a significant difference between group 1 and group 3 in terms of mean diffusivity values at all auditory pathways. Fractional anisotropy values obtained from lateral lemniscus, inferior colliculus, and Heschl gyrus decreased in group 1 compared with group 3. There was a negative correlation between mean diffusivity values and positive correlation between fractional anisotropy values at lateral lemniscus, inferior colliculus, Heschl gyrus, and age. Conclusions: Our diffusion tensor imaging findings show that the neuronal integrity of the auditory pathways is affected in neurofibromatosis type 1 patients with brainstem focal areas of abnormal signal intensity. We think that the disappearance of brainstem focal areas of abnormal signal intensity associated with myelin repair and the regression of diffusion tensor imaging changes in the auditory pathways occur simultaneously with advancing age in patients with neurofibromatosis type 1.

White matter characteristics in children with cerebral palsy prior to selective dorsal rhizotomy: a multicenter diffusion tensor imaging study.

The aims of this study were to 1) assess and quantify white matter (WM) microstructural characteristics derived from diffusion tensor imaging (DTI) in children with cerebral palsy (CP) prior to selective dorsal rhizotomy (SDR), and 2) investigate potential associations between WM diffusion properties and gross motor function and spasticity in children with spastic CP who underwent SDR. This study is a multisite study based on DT images acquired prior to SDR as well as postoperative outcome data. DTI data collected from two sites were harmonized using the ComBat approach to minimize intersite scanner difference. The DTI abnormalities between children with spastic CP and controls were analyzed and correlated with the severity of impaired mobility based on the Gross Motor Function Classification System (GMFCS). The improvement in gross motor function and spasticity after SDR surgery was assessed utilizing the Gross Motor Function Measure-66 (GMFM-66), the Modified Tardieu Scale (MTS), and the modified Ashworth scale (MAS). Alterations in these outcome measures were quantified in association with DTI abnormalities. Significant DTI alterations, including lower fractional anisotropy (FA) in the genu of the corpus callosum (gCC) and higher mean diffusivity (MD) in the gCC and posterior limb of the internal capsule (PLIC), were found in children in the SDR group when compared with the age-matched control group (all p < 0.05). Greater DTI alterations (FA in gCC and MD in gCC and PLIC) were associated with lower mobility levels as determined based on GMFCS level (p < 0.05). The pre- to post-SDR improvement in motor function based on GMFM-66 was statistically significant (p = 0.006 and 0.002 at 6-month and 12-month follow-ups, respectively). The SDR efficacy was also identified as improving spasticity in lower-extremity muscle groups assessed with the MTS and MAS. Partial correlation analysis presented a significant association between pre- to post-SDR MTS alteration and DTI abnormalities. The findings in the present study provided initial quantitative evidence to establish the WM microstructural characteristics in children with spastic CP prior to SDR surgery. The study generated data for the association between baseline DTI characteristics and mobility in children with CP prior to SDR surgery. The study also demonstrated SDR efficacy in improving motor function and spasticity based on the GMFM-66, MTS, and MAS, respectively, in association with DTI data.

Diffusion Tensor Imaging for Characterizing Changes in Triple-Negative Breast Cancer During Neoadjuvant Systemic Therapy.

Assessment of treatment response in triple-negative breast cancer (TNBC) may guide individualized care for improved patient outcomes. Diffusion tensor imaging (DTI) measures tissue anisotropy and could be useful for characterizing changes in the tumors and adjacent fibroglandular tissue (FGT) of TNBC patients undergoing neoadjuvant systemic treatment (NAST). To evaluate the potential of DTI parameters for prediction of treatment response in TNBC patients undergoing NAST. Prospective. Eighty-six women (average age: 51 ± 11 years) with biopsy-proven clinical stage I-III TNBC who underwent NAST followed by definitive surgery. 47% of patients (40/86) had pathologic complete response (pCR). 3.0 T/reduced field of view single-shot echo-planar DTI sequence. Three MRI scans were acquired longitudinally (pre-treatment, after 2 cycles of NAST, and after 4 cycles of NAST). Eleven histogram features were extracted from DTI parameter maps of tumors, a peritumoral region (PTR), and FGT in the ipsilateral breast. DTI parameters included apparent diffusion coefficients and relative diffusion anisotropies. pCR status was determined at surgery. Longitudinal changes of DTI features were tested for discrimination of pCR using Mann-Whitney U test and area under the receiver operating characteristic curve (AUC). A P value <0.05 was considered statistically significant. 47% of patients (40/86) had pCR. DTI parameters assessed after 2 and 4 cycles of NAST were significantly different between pCR and non-pCR patients when compared between tumors, PTRs, and FGTs. The median surface/average anisotropy of the PTR, measured after 2 and 4 cycles of NAST, increased in pCR patients and decreased in non-pCR patients (AUC: 0.78; 0.027 ± 0.043 vs. -0.017 ± 0.042 mm2 /s). Quantitative DTI features from breast tumors and the peritumoral tissue may be useful for predicting the response to NAST in TNBC. 1 TECHNICAL EFFICACY: Stage 4.

Diffusion tensor imaging: survival analysis prediction in breast cancer patients.

We aimed to explore the performance of diffusion-tensor imaging (DTI) and apparent diffusion coefficient (ADC) parameters in evaluating disease-free survival (DFS) and overall survival (OS) in patients with invasive breast cancer. Atotal of 49women with invasive breast cancer who were diagnosed between 2017 and 2022 were included. All patients underwent breast magnetic resonance imaging (MRI) with DTI and diffusion-weighted imaging (DWI) features, with examiners blinded to the clinical data. Volume anisotropy (VA), fractional anisotropy (FA), and ADC values were measured to assess intratumoral measured heterogeneity. Correlations and differences in diffusion metrics according to OS and DFS status of the cases were analyzed. The discriminative ability of the quantitative findings was assessed by receiver operating characteristic (ROC) curve analyses and validated in the independent cohort. We evaluated patients with metastases (n = 13, 36.5%) and those without metastases (n = 36, 73.5%). Differences in the ADC, FA, and VA values were observed. The results of Cox regression survival analysis for all the patients included in the survival analysis revealed that DTI metrics contributed to the prediction of overall survival (OS) in the emerging models (p < 0.05). Both FA and VA were associated with OS (p = 0.037 and p = 0.038, respectively). However, ADC was not associated with OS (p = 0.177) or DFS (p = 0.252). To the best of our knowledge, this is the first study to assess the prognostic value of DTI-MRI in breast cancer with statistical survival analysis techniques. We believe that DTI measurements can be used as abiomarker for OS analysis in breast cancer given the available data.

The incidence of movement disorder increases with age and contrasts with subtle and limited neuroimaging abnormalities in argininosuccinic aciduria.

Argininosuccinate lyase (ASL) is integral to the urea cycle detoxifying neurotoxic ammonia and the nitric oxide (NO) biosynthesis cycle. Inherited ASL deficiency causes argininosuccinic aciduria (ASA), a rare disease with hyperammonemia and NO deficiency. Patients present with developmental delay, epilepsy and movement disorder, associated with NO-mediated downregulation of central catecholamine biosynthesis. A neurodegenerative phenotype has been proposed in ASA. To better characterise this neurodegenerative phenotype in ASA, we conducted a retrospective study in six paediatric and adult metabolic centres in the UK in 2022. We identified 60 patients and specifically looked for neurodegeneration-related symptoms: movement disorder such as ataxia, tremor and dystonia, hypotonia/fatigue and abnormal behaviour. We analysed neuroimaging with diffusion tensor imaging (DTI) magnetic resonance imaging (MRI) in an individual with ASA with movement disorders. We assessed conventional and DTI MRI alongside single photon emission computer tomography (SPECT) with dopamine analogue radionuclide 123 I-ioflupane, in Asl-deficient mice treated by hASL mRNA with normalised ureagenesis. Movement disorders in ASA appear in the second and third decades of life, becoming more prevalent with ageing and independent from the age of onset of hyperammonemia. Neuroimaging can show abnormal DTI features affecting both grey and white matter, preferentially basal ganglia. ASA mouse model with normalised ureagenesis did not recapitulate these DTI findings and showed normal 123 I-ioflupane SPECT and cerebral dopamine metabolomics. Altogether these findings support the pathophysiology of a late-onset movement disorder with cell-autonomous functional central catecholamine dysregulation but without or limited neurodegeneration of dopaminergic neurons, making these symptoms amenable to targeted therapy.

Predicting early Alzheimer’s disease from diffusion tensor imaging using machine learning techniques

AbstractBackgroundThe major current challenge in Alzheimer’s disease (AD) is the identification of individuals likely to develop dementia. Non‐invasive imaging techniques that reflect the integrity of the brain’s white matter are potential parameters to study the pathogenesis of AD. Diffusion tensor imaging (DTI) provides tools to analyze AD‐related brain changes despite the large amount of data generated. To process large amounts of data, computational methods capable of finding relationships among large and complex data sets are needed. In this context, machine learning (ML) techniques have been extended to address this issue. This study aimed to evaluate whether DTI features could predict future AD in non‐AD patients using ML techniques.MethodSixty‐two subjects, with mild cognitive impairment and healthy controls, were enrolled and followed for approximately eleven months. At the first assessment, MRI, neuropsychological tests, and cerebrospinal fluid analysis were performed. At the second assessment, ten had progressed to Alzheimer’s dementia. The clinical diagnosis was used to train the ML classification models. Five data sets were generated with different missing data strategies. Feature selection strategies measured by information gain were implemented and compared. Twenty ML algorithms distributed over several mathematical approaches were used in model training. A deep learning model was also implemented for comparison with ML results.ResultThe best model for predicting AD by DTI was Random Forest with kNN missing data imputation and features selected by information gain. This model achieved a test accuracy of 88.72%. The most relevant DTI tract that contributed to the prediction of the model was the fractional anisotropy (FA) of the cingulum ‐ hippocampus . When other categories of data, such as neuropsychological tests, were included in the model, the accuracy hardly changed (90.26%), suggesting that DTI alone is a good predictor of Alzheimer’s disease. When the deep learning model was run, the constructed neural network achieved 92.31% accuracy.ConclusionOur results suggest that DTI measures analyzed by ML models, such as Random Forest, may predict AD in non‐AD patients. Furthermore, DTI measure of cingulum ‐ hippocampus tract may be a sensitive marker of early AD pathology.

Prediction of Mild Cognitive Impairment Progression to Alzheimer’s Disease Based on Diffusion Tensor Imaging-Derived Diffusion Parameters: Construction and Validation of a Nomogram

Introduction: The aim of the study was to construct and validate a nomogram that combines diffusion tensor imaging (DTI) parameters and clinically relevant features for predicting the progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD). Method: A retrospective analysis was conducted on the MRI and clinical data of 121 MCI patients, of whom 32 progressed to AD during a 4-year follow-up period. The MCI patients were divided into training and validation sets at a ratio of 7:3. DTI features were extracted from MCI patient data in the training set, and their dimensionality was reduced to construct a radiomics signature (RS). Then, combining the RS with independent predictors of MCI disease progression, a joint model was constructed, and a nomogram was generated. Finally, the area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to evaluate the diagnostic and clinical efficacy of the nomogram based on the data from the validation set. Result: The AUCs of the RS in the training and validation sets were 0.81 and 0.84, with sensitivities of 0.87 and 0.78 and specificities of 0.71 and 0.81, respectively. Multiple logistic regression analysis showed that the RS, clinical dementia rating scale score, and Alzheimer's disease assessment scale score were the independent predictors of progression and were thus used to construct the nomogram. The AUCs of the nomogram in the training and validation sets were 0.89 and 0.91, respectively, with sensitivities of 0.78 and 0.89 and specificities of 0.90 and 0.88, respectively. DCA showed that the nomogram was the most valuable model for predicting the progression of MCI to AD and that it provided greater net benefits than other analysed models. Conclusion: Changes in white matter fibre bundles can serve as predictive imaging markers for MCI disease progression, and the combination of white matter DTI features and relevant clinical features can be used to construct a nomogram with important predictive value for MCI disease progression.

Structural connectome combining DTI features predicts postoperative language decline and its recovery in glioma patients.

A decline in language function is a common complication after glioma surgery, affecting patients' quality of life and survival. This study predicts the postoperative decline in language function and whether it can be recovered based on the preoperative white matter structural network. Eighty-one right-handed patients with glioma involving the left hemisphere were retrospectively included. Their language function was assessed using the Western Aphasia Battery before and 1 week and 3 months after surgery. Structural connectome combining DTI features was selected to predict postoperative language decline and recovery. Nested cross-validation was used to optimize the models, evaluate the prediction performance of the models, and identify the most predictive features. Five, seven, and seven features were finally selected as the predictive features in each model and used to establish predictive models for postoperative language decline (1 week after surgery), long-term language decline (3 months after surgery), and language recovery, respectively. The overall accuracy of the three models in nested cross-validation and overall area under the receiver operating characteristic curve were 0.840, 0.790, and 0.867, and 0.841, 0.778, and 0.901, respectively. We used machine learning algorithms to establish models to predict whether the language function of glioma patients will decline after surgery and whether postoperative language deficit can recover, which may help improve the development of treatment strategies. The difference in features in the non-language decline or the language recovery group may reflect the structural basis for the protection and compensation of language function in gliomas. Models can predict the postoperative language decline and whether it can recover in glioma patients, possibly improving the development of treatment strategies. The difference in selected features may reflect the structural basis for the protection and compensation of language function. • Structural connectome combining diffusion tensor imaging features predicted glioma patients' language decline after surgery. • Structural connectome combining diffusion tensor imaging features predicted language recovery of glioma patients with postoperative language disorder. • Diffusion tensor imaging and connectome features related to language function changes imply plastic brain regions and connections.

Alterations of cerebellar white matter integrity and associations with cognitive impairments in schizophrenia.

“Cognitive dysmetria” theory of schizophrenia (SZ) has highlighted that the cerebellum plays a critical role in understanding the pathogenesis and cognitive impairment in SZ. Despite some studies have reported the structural disruption of the cerebellum in SZ using whole brain approach, specific focus on the voxel-wise changes of cerebellar WM microstructure and its associations with cognition impairments in SZ were less investigated. To further explore the voxel-wise structural disruption of the cerebellum in SZ, the present study comprehensively examined volume and diffusion features of cerebellar white matter in SZ at the voxel level (42 SZ vs. 52 controls) and correlated the observed alterations with the cognitive impairments measured by MATRICS Consensus Cognitive Battery. Combing voxel-based morphometry (VBM) and diffusion tensor imaging (DTI) methods, we found, compared to healthy controls (HCs), SZ patients did not show significant alteration in voxel-level cerebellar white matter (WM) volume and tract-wise and skeletonized DTI features. In voxel-wise DTI features of cerebellar peduncles, compared to HCs, SZ patients showed decreased fractional anisotropy and increased radial diffusivity mainly located in left middle cerebellar peduncles (MCP) and inferior cerebellar peduncles (ICP). Interestingly, these alterations were correlated with overall composite and different cognitive domain (including processing speed, working memory, and attention vigilance) in HCs but not in SZ patients. The present findings suggested that the voxel-wise WM integrity analysis might be a more sensitive way to investigate the cerebellar structural abnormalities in SZ patients. Correlation results suggested that inferior and MCP may be a crucial neurobiological substrate of cognition impairments in SZ, thus adding the evidence for taking the cerebellum as a novel therapeutic target for cognitive impairments in SZ patients.

Development of a composite diffusion tensor imaging score correlating with short-term neurological status in neonatal hypoxic-ischemic encephalopathy.

Hypoxic–ischemic encephalopathy (HIE) is the most common cause of neonatal acquired brain injury. Although conventional MRI may predict neurodevelopmental outcomes, accurate prognostication remains difficult. As diffusion tensor imaging (DTI) may provide an additional diagnostic and prognostic value over conventional MRI, we aimed to develop a composite DTI (cDTI) score to relate to short-term neurological function. Sixty prospective neonates treated with therapeutic hypothermia (TH) for HIE were evaluated with DTI, with a voxel size of 1 × 1 × 2 mm. Fractional anisotropy (FA) and mean diffusivity (MD) from 100 neuroanatomical regions (FA/MD *100 = 200 DTI parameters in total) were quantified using an atlas-based image parcellation technique. A least absolute shrinkage and selection operator (LASSO) regression was applied to the DTI parameters to generate the cDTI score. Time to full oral nutrition [short-term oral feeding (STO) score] was used as a measure of short-term neurological function and was correlated with extracted DTI features. Seventeen DTI parameters were selected with LASSO and built into the final unbiased regression model. The selected factors included FA or MD values of the limbic structures, the corticospinal tract, and the frontotemporal cortices. While the cDTI score strongly correlated with the STO score (rho = 0.83, p = 2.8 × 10−16), it only weakly correlated with the Sarnat score (rho = 0.27, p = 0.035) and moderately with the NICHD-NRN neuroimaging score (rho = 0.43, p = 6.6 × 10−04). In contrast to the cDTI score, the NICHD-NRN score only moderately correlated with the STO score (rho = 0.37, p = 0.0037). Using a mixed-model analysis, interleukin-10 at admission to the NICU (p = 1.5 × 10−13) and tau protein at the end of TH/rewarming (p = 0.036) and after rewarming (p = 0.0015) were significantly associated with higher cDTI scores, suggesting that high cDTI scores were related to the intensity of the early inflammatory response and the severity of neuronal impairment after TH. In conclusion, a data-driven unbiased approach was applied to identify anatomical structures associated with some aspects of neurological function of HIE neonates after cooling and to build a cDTI score, which was correlated with the severity of short-term neurological functions.

Effect of Nursing Intervention on Self-Management and Quality of Life in Patients with Chronic Kidney Disease Evaluated by Renal Diffusion Tensor Imaging Features Using Image Registration Algorithm

The aim of this research was to explore the effect of nursing intervention on self-management and quality of life in patients with chronic kidney disease (CKD) by using the renal diffusion tensor imaging (DTI) feature of image registration algorithm. A total of 100 patients with CKD were randomly divided into experimental group (nursing maintenance guidance intervention) and control group (routine nursing), with 50 cases in each group. The image effect before and after registration, self-management behavior after 6 months, quality of life, DTI-related parameters, and renal function indicators were collected and analyzed. The results showed that the images were clearer than those before registration, the self-management ability in the control group (92.81 ± 19.32) was smaller than that in the experimental group (107.12 ± 18.78), the quality of life in the experimental group (121.47 ± 11.21) was greater than that in the control group (104.89 ± 12.11), and the corresponding magnetic resonance apparent diffusion coefficient (ADC) (2.54 ± 0.28) and fractional anisotropy (FA) (0.28 ± 0.07) in the cortex were greater than those in the control group (2.35 ± 0.21, 0.23 ± 0.04). The differences were statistically significant ( P &lt; 0.05 ). The corresponding ADC value (2.32 ± 0.22) and FA value (0.59 ± 0.02) of medulla were greater than those of the control group (2.12 ± 0.24 and 0.41 ± 0.17). The levels of serum creatinine (Scr) (μmol/L) (421.38 ± 42.78) and 24 h urinary protein (24 h-Upro) (mg/d) (1,836.7 ± 545.98) were lower than those of the control group, and the differences had statistical significance ( P &lt; 0.05 ). In summary, the registration algorithm can enhance the effect of image presentation and nursing intervention has positive significance for the self-management and quality of life of patients with CKD.

Cross-site harmonization of multi-shell diffusion MRI measures based on rotational invariant spherical harmonics (RISH)

Quantification methods based on the acquisition of diffusion magnetic resonance imaging (dMRI) with multiple diffusion weightings (e.g., multi-shell) are becoming increasingly applied to study the in-vivo brain. Compared to single-shell data for diffusion tensor imaging (DTI), multi-shell data allows to apply more complex models such as diffusion kurtosis imaging (DKI), which attempts to capture both diffusion hindrance and restriction effects, or biophysical models such as NODDI, which attempt to increase specificity by separating biophysical components. Because of the strong dependence of the dMRI signal on the measurement hardware, DKI and NODDI metrics show scanner and site differences, much like other dMRI metrics. These effects limit the implementation of multi-shell approaches in multicenter studies, which are needed to collect large sample sizes for robust analyses. Recently, a post-processing technique based on rotation invariant spherical harmonics (RISH) features was introduced to mitigate cross-scanner differences in DTI metrics. Unlike statistical harmonization methods, which require repeated application to every dMRI metric of choice, RISH harmonization is applied once on the raw data, and can be followed by any analysis. RISH features harmonization has been tested on DTI features but not its generalizability to harmonize multi-shell dMRI. In this work, we investigated whether performing the RISH features harmonization of multi-shell dMRI data removes cross-site differences in DKI and NODDI metrics while retaining longitudinal effects. To this end, 46 subjects underwent a longitudinal (up to 3 time points) two-shell dMRI protocol at 3 imaging sites. DKI and NODDI metrics were derived before and after harmonization and compared both at the whole brain level and at the voxel level. Then, the harmonization effects on cross-sectional and on longitudinal group differences were evaluated. RISH features averaged for each of the 3 sites exhibited prominent between-site differences in the frontal and posterior part of the brain. Statistically significant differences in fractional anisotropy, mean diffusivity and mean kurtosis were observed both at the whole brain and voxel level between all the acquisition sites before harmonization, but not after. The RISH method also proved effective to harmonize NODDI metrics, particularly in white matter. The RISH based harmonization maintained the magnitude and variance of longitudinal changes as compared to the non-harmonized data of all considered metrics. In conclusion, the application of RISH feature based harmonization to multi-shell dMRI data can be used to remove cross-site differences in DKI metrics and NODDI analyses, while retaining inherent relations between longitudinal acquisitions.

Identifying Depression in Parkinson's Disease by Using Combined Diffusion Tensor Imaging and Support Vector Machine.

ObjectiveTo investigate white matter microstructural alterations in Parkinson's disease (PD) patients with depression using the whole-brain diffusion tensor imaging (DTI) method and to explore the DTI–based machine learning model in identifying depressed PD (dPD).MethodsThe DTI data were collected from 37 patients with dPD and 35 patients with non-depressed PD (ndPD), and 25 healthy control (HC) subjects were collected as the reference. An atlas-based analysis method was used to compare fractional anisotropy (FA) and mean diffusivity (MD) among the three groups. A support vector machine (SVM) was trained to examine the probability of discriminating between dPD and ndPD.ResultsAs compared with ndPD, dPD group exhibited significantly decreased FA in the bilateral corticospinal tract, right cingulum (cingulate gyrus), left cingulum hippocampus, bilateral inferior longitudinal fasciculus, and bilateral superior longitudinal fasciculus, and increased MD in the right cingulum (cingulate gyrus) and left superior longitudinal fasciculus-temporal part. For discriminating between dPD and ndPD, the SVM model with DTI features exhibited an accuracy of 0.70 in the training set [area under the receiver operating characteristic curve (ROC) was 0.78] and an accuracy of 0.73 in the test set (area under the ROC was 0.71).ConclusionDepression in PD is associated with white matter microstructural alterations. The SVM machine learning model based on DTI parameters could be valuable for the individualized diagnosis of dPD.

Segmentation of Infant Brain Using Nonnegative Matrix Factorization

This study develops an atlas-based automated framework for segmenting infants’ brains from magnetic resonance imaging (MRI). For the accurate segmentation of different structures of an infant’s brain at the isointense age (6–12 months), our framework integrates features of diffusion tensor imaging (DTI) (e.g., the fractional anisotropy (FA)). A brain diffusion tensor (DT) image and its region map are considered samples of a Markov–Gibbs random field (MGRF) that jointly models visual appearance, shape, and spatial homogeneity of a goal structure. The visual appearance is modeled with an empirical distribution of the probability of the DTI features, fused by their nonnegative matrix factorization (NMF) and allocation to data clusters. Projecting an initial high-dimensional feature space onto a low-dimensional space of the significant fused features with the NMF allows for better separation of the goal structure and its background. The cluster centers in the latter space are determined at the training stage by the K-means clustering. In order to adapt to large infant brain inhomogeneities and segment the brain images more accurately, appearance descriptors of both the first-order and second-order are taken into account in the fused NMF feature space. Additionally, a second-order MGRF model is used to describe the appearance based on the voxel intensities and their pairwise spatial dependencies. An adaptive shape prior that is spatially variant is constructed from a training set of co-aligned images, forming an atlas database. Moreover, the spatial homogeneity of the shape is described with a spatially uniform 3D MGRF of the second-order for region labels. In vivo experiments on nine infant datasets showed promising results in terms of the accuracy, which was computed using three metrics: the 95-percentile modified Hausdorff distance (MHD), the Dice similarity coefficient (DSC), and the absolute volume difference (AVD). Both the quantitative and visual assessments confirm that integrating the proposed NMF-fused DTI feature and intensity MGRF models of visual appearance, the adaptive shape prior, and the shape homogeneity MGRF model is promising in segmenting the infant brain DTI.

Multimodal magnetic resonance neuroimaging measures characteristic of early cART-treated pediatric HIV: A feature selection approach.

Children with perinatally acquired HIV (CPHIV) have poor cognitive outcomes despite early combination antiretroviral therapy (cART). While CPHIV‐related brain alterations can be investigated separately using proton magnetic resonance spectroscopy (1H‐MRS), structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and functional MRI (fMRI), a set of multimodal MRI measures characteristic of children on cART has not been previously identified. We used the embedded feature selection of a logistic elastic‐net (EN) regularization to select neuroimaging measures that distinguish CPHIV from controls and measured their classification performance via the area under the receiver operating characteristic curve (AUC) using repeated cross validation. We also wished to establish whether combining MRI modalities improved the models. In single modality analysis, sMRI volumes performed best followed by DTI, whereas individual EN models on spectroscopic, gyrification, and cortical thickness measures showed no class discrimination capability. Adding DTI and 1H‐MRS in basal measures to sMRI volumes produced the highest classification performance validation accuracy=85%AUC=0.80. The best multimodal MRI set consisted of 22 DTI and sMRI volume features, which included reduced volumes of the bilateral globus pallidus and amygdala, as well as increased mean diffusivity (MD) and radial diffusivity (RD) in the right corticospinal tract in cART‐treated CPHIV. Consistent with previous studies of CPHIV, select subcortical volumes obtained from sMRI provide reasonable discrimination between CPHIV and controls. This may give insight into neuroimaging measures that are relevant in understanding the effects of HIV on the brain, thereby providing a starting point for evaluating their link with cognitive performance in CPHIV.

Diffusion Tensor Imaging Features of Watershed Segmentation Algorithm for Analysis of the Relationship between Depression and Brain Nerve Function of Patients with End-Stage Renal Disease.

The aim of this research was to explore the relationship between depression and brain nerve function in patients with end-stage renal disease (ESRD) and long-term maintenance hemodialysis (MHD) based on watershed segmentation algorithm using diffusion tensor imaging (DTI) technology. A total of 29 ESRD patients with depression who received MHD treatment in the hemodialysis center of hospital were included as the research subjects (case group). A total of 29 healthy volunteers were recruited as the control group, and a total of 29 ESRD patients with depression and brain lesions were recruited as the control group (HC group). Within 24 h after hemodialysis, the blood biochemical indexes were collected before this DTI examination. All participants completed the neuropsychological scale (MoCA, TMT A, DST, SAS, and SDS) test. The original DTI data of all subjects were collected and processed based on watershed segmentation algorithm, and the results of automatic segmentation according to the image were evaluated as DSC = 0.9446, MPA = 0.9352, and IOU = 0.8911. Finally, the average value of imaging brain neuropathy in patients with depression in the department of nephrology was obtained. The differences in neuropsychological scale scores (PSQI, MoCA, TMTA, DST, SAS, and SDS) between the two groups were statistically significant (P < 0.05). The differences of FA values in all the white matter partitions of Fu organs, except the cingulum of hippocampus (CgH) between the two groups, were statistically significant (P < 0.05). ESRD and DTI quantitative detection under the guidance of watershed segmentation algorithm in MHD patients showed that ESRD patients can be early identified, so as to carry out psychological nursing as soon as possible to reduce the occurrence of depression, and then protect the brain nerve to reduce brain neuropathy.

White matter brain aging in relationship to schizophrenia and its cognitive deficit

We hypothesized that cerebral white matter deficits in schizophrenia (SZ) are driven in part by accelerated white matter aging and are associated with cognitive deficits. We used a machine learning model to predict individual age from diffusion tensor imaging features and calculated the delta age (Δage) as the difference between predicted and chronological age. Through this approach, we translated multivariate white matter imaging features into an age-scaled metric and used it to test the temporal trends of accelerated aging-related white matter deficit in SZ and its association with the cognition. A feature selection procedure was first employed to choose fractional anisotropy values in 34 of 43 white fiber tracts. Using these features, a machine learning model was trained based on a training set consisted of 107 healthy controls (HC). The brain age of 166 SZs and 107 HCs in the testing set were calculated using this model. Then, we examined the SZ-HC group effect on Δage and whether this effect was moderated by chronological age using the regression spline model. The results showed that Δage was significantly elevated in the age > 30 group in patients (p < 0.001) but not in age ≤ 30 group (p = 0.364). Δage in patients was significantly and negatively associated with both working memory (β = −0.176, p = 0.007) and processing speed (β = −0.519, p = 0.035) while adjusting sex and chronological age. Overall, these findings indicate that the Δage is elevated in SZs and become significantly from the third decade of life; the increase of Δage in SZs is associated with the declined neurocognitive performance.

The Utility and Efficiency of Diffusion Tensor Imaging Values to Determine Epidermal Growth Factor Receptor Gene Mutation Status in Brain Metastasis from Lung Adenocarcinoma; A Preliminary Study.

This study aims to investigate the existence of any Diffusion Tensor Imaging (DTI) value differences in Brain Metastases (BM) due to lung adenocarcinoma based on the Epidermal Growth Factor Receptor (EGFR) gene mutation status. 17 patients with 32 solid intracranial metastatic lesions from lung adenocarcinoma were included prospectively. Patients were divided according to the EGFR mutation status as EGFR (+) (group 1, n:8) and EGFR wild type (group 2, n:9). The Fractional Anisotropy (FA), apparent diffusion coefficient (ADC), normalized ADC (nADC), Axial Diffusivity (AD), and Radial Diffusivity (RD) values were measured from the solid component of the metastatic lesions and nADC values were calculated. DTI values were compared between group 1 and group 2. The receiver-operating characteristic analysis was used to obtain cut-off values for the parameters presenting a statistical difference between the EGFR gene mutation-positive and wild type group. There were statistically significant differences in measured ADC, nADC, AD, and RD values between group 1 and group 2. The ADC, nADC, AD, and RD values were significantly lower in group 1. There was no significant difference in FA values between the two groups. Analysis by the ROC curve method revealed a cut-off value of ≤721 x 10-6 mm2/s for ADC (Sensitivity= 72.7, Specificity=85.7); ≤0.820 for nADC (Sensitivity=72.7, Specificity=90.5), ≤ 886 for AD (Sensitivity=81.8, Specificity=81.0), and ≤588 for RD (Sensitivity=63.6, Specificity=90.5) in differentiating EGFR mutation (+) group from wild type group. A combination of the decreased ADC, nADC, AD, and RD values in BM due to lung adenocarcinoma can be important for predicting the EGFR gene mutation status. DTI features of the brain metastases from lung adenocarcinoma may be utilized to provide insight into the EGFR mutation status and guide the clinicians for the initiation of targeted therapy.