Diffusion Tensor Research Articles

Combining functional, structural, and morphological networks for multimodal classification of developing autistic brains.

Accumulating neuroimaging evidence suggests that abnormal functional and structural brain connectivity plays a cardinal role in the pathophysiology of autism spectrum disorder (ASD). Here, we constructed brain networks of functional, structural, and morphological connectivity using data from functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and structural magnetic resonance imaging (sMRI), respectively. The neuroimaging data from a cohort of 50 individuals with ASD and 47 age-, gender- and handedness-matched TDC (age range: 5-18 years) were selected from the Autism Brain Image Data Exchange database. The combination of the fMRI, sMRI and DTI modalities connectivity features resulted in a classification accuracy of 82.69% for differentiating individuals with ASD from TDC. This accuracy surpassed that of any single modality or combination of fMRI and DTI modalities previously examined. Among the fMRI, sMRI and DTI modalities, the most distinguishing connectivity features were observed in the temporal, parietal, and occipital lobes from the DTI modality, the prefrontal and parietal lobes from the fMRI modality, and the temporal lobe from the sMRI modality. In addition, we also found that these distinguishing connectivity features can predict abnormal social interaction behaviours in ASD. These results highlight the complementary information provided by multimodal approaches, further emphasizing the pivotal role of multimodal connectivity patterns in unravelling the intricate mechanisms involved in the pathophysiology of ASD.

A review on learning-based algorithms for tractography and human brain white matter tracts recognition.

Human brain fiber tractography using diffusion magnetic resonance imaging is a crucial stage in mapping brain white matter structures, pre-surgical planning, and extracting connectivity patterns. Accurate and reliable tractography, by providing detailed geometric information about the position of neural pathways, minimizes the risk of damage during neurosurgical procedures. Both tractography itself and its post-processing steps such as bundle segmentation are usually used in these contexts. Many approaches have been put forward in the past decades and recently, multiple data-driven tractography algorithms and automatic segmentation pipelines have been proposed to address the limitations of traditional methods. Several of these recent methods are based on learning algorithms that have demonstrated promising results. In this study, in addition to introducing diffusion MRI datasets, we review learning-based algorithms such as conventional machine learning, deep learning, reinforcement learning and dictionary learning methods that have been used for white matter tract, nerve and pathway recognition as well as whole brain streamlines or whole brain tractogram creation. The contribution is to discuss both tractography and tract recognition methods, in addition to extending previous related reviews with most recent methods, covering architectures as well as network details, assess the efficiency of learning-based methods through a comprehensive comparison in this field, and finally demonstrate the important role of learning-based methods in tractography.

Comparison of two electroacupuncture regimens on symptoms and brain structures in breast cancer survivors: A randomized, controlled trial.

12119 Background: Although electroacupuncture (EA) has shown usefulness in managing neuropsychiatric symptoms in cancer survivors, a specific acupoint regimen has not been established. We conducted a randomized, controlled, patient- and assessor-blinded pilot trial to compare two EA regimens on neuropsychiatric symptoms and associated brain structural changes in breast cancer survivors. (Clinicaltrials.gov: NCT05283577). Methods: Breast cancer survivors who self-reported cognitive impairment, fatigue, insomnia, or psychological distress were randomized (1:1) to receive ten weekly therapeutic EA to target either neuropsychiatric-specific (nEA) or non-neuropsychiatric-specific (sham EA, sEA) acupoints. Outcomes were assessed using patient-reported outcomes (EORTC QLQ-C30, FACT-Cog, MFSI-SF), neurocognitive tests (CANTAB), and neuroimaging (measuring gray matter, white matter, cerebrospinal fluid, diffusion tensor metrics, and volume and mean intensity of the hippocampus) before and after treatment. We computed group-specific treatment effect sizes (Glass's Δ) adjusted for baseline variability using linear mixed models. A Pearson’s correlation analysis was performed between the neurocognitive scores and the imaging metrics. Multiple testing was controlled via the Benjamini-Hochberg method, with statistical significance set at P-adjusted < 0.05. Adverse events (AEs) were graded with CTCAE v5. Results: Thirty-five participants were recruited, of which five dropped out, leaving 30 (86%) completing all treatment sessions. The average (±SD) age was 58.2 ±12.2 years, with 66% non-Hispanic White, 77% holding a Bachelor’s degree or higher, 94% received systemic treatment and/or radiotherapy for cancer, 86% reporting ≥2 neuropsychiatric symptoms. Both groups showed statistically significant pre-post medium-to-large effect sizes in perceived cognitive function, fatigue, and quality of life. nEA group observed significant improvement in cognitive domains of attention (ES=0.708, P-adjusted=0.004), memory (ES=0.488, P-adjusted=0.026), and emotional functioning (ES=0.664, P-adjusted=0.004). Neuroimages showed greater gray matter volume change (P=0.0327) and post-treatment hippocampus mean intensity (P=0.0468) in nEA versus sEA. In the nEA group, correlations were observed between attention and gray matter volume (P=0.0198) and between executive function and hippocampus volume (P=0.0204). All AEs were grade 2 or lower: nEA participants reported pain (n=1) and bleeding (n=1), while sEA participants reported numbness (n=2), bruising (n=1), nausea (n=1), and redness (n=1). Conclusions: Ten weeks of electroacupuncture targeting neuropsychiatric-related acupoints, compared to sham acupoints, improves neuropsychiatric symptoms in breast cancer survivors, supported by clinically relevant structural brain changes. Clinical trial information: NCT05283577 .

A cross-sectional multicentre study of multishell diffusion MRI in multiple sclerosis.

A cross-sectional multicentre study of multishell diffusion MRI in multiple sclerosis.

Disrupted interhemispheric functional and structural connectivity in patients with major depressive disorder.

Disrupted interhemispheric functional and structural connectivity in patients with major depressive disorder.

Diffusion MRI tractography with along-tract profiling reveals subtle neurodevelopmental differences between moderate and late preterm infants.

Diffusion MRI tractography with along-tract profiling reveals subtle neurodevelopmental differences between moderate and late preterm infants.

Combining radiomics and connectomics in MRI studies of the human brain: A systematic literature review.

Combining radiomics and connectomics in MRI studies of the human brain: A systematic literature review.

Relation of Alzheimer's disease-related TDP-43 proteinopathy to metrics from diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI).

Relation of Alzheimer's disease-related TDP-43 proteinopathy to metrics from diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI).

Characterization of the glymphatic system and early-phase β-amyloid imaging in Alzheimer's disease: A simultaneous PET/MR study.

Characterization of the glymphatic system and early-phase β-amyloid imaging in Alzheimer's disease: A simultaneous PET/MR study.

Glymphatic system dysfunction in adult ADHD: Relationship to cognitive performance.

Glymphatic system dysfunction in adult ADHD: Relationship to cognitive performance.

Neuroimaging and machine learning in eating disorders: a systematic review

Abstract Purpose Eating disorders (EDs), including anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED), are complex psychiatric conditions with high morbidity and mortality. Neuroimaging and machine learning (ML) represent promising approaches to improve diagnosis, understand pathophysiological mechanisms, and predict treatment response. This systematic review aimed to evaluate the application of ML techniques to neuroimaging data in EDs. Methods Following PRISMA guidelines (PROSPERO registration: CRD42024628157), we systematically searched PubMed and APA PsycINFO for studies published between 2014 and 2024. Inclusion criteria encompassed human studies using neuroimaging and ML methods applied to AN, BN, or BED. Data extraction focused on study design, imaging modalities, ML techniques, and performance metrics. Quality was assessed using the GRADE framework and the ROBINS-I tool. Results Out of 185 records screened, 5 studies met the inclusion criteria. Most applied support vector machines (SVMs) or other supervised ML models to structural MRI or diffusion tensor imaging data. Cortical thickness alterations in AN and diffusion-based metrics effectively distinguished ED subtypes. However, all studies were observational, heterogeneous, and at moderate to serious risk of bias. Sample sizes were small, and external validation was lacking. Conclusion ML applied to neuroimaging shows potential for improving ED characterization and outcome prediction. Nevertheless, methodological limitations restrict generalizability. Future research should focus on larger, multicenter, and multimodal studies to enhance clinical applicability. Level of Evidence: Level IV, multiple observational studies with methodological heterogeneity and moderate to serious risk of bias.

The neuroanatomy of menopause.

The neuroanatomy of menopause.

Volumetric and microstructural changes in the Hippocampus and cingulum in children with west syndrome.

Volumetric and microstructural changes in the Hippocampus and cingulum in children with west syndrome.

Fiber connectivity density in subcortical stroke patients with behavioral dysexecutive symptoms.

Fiber connectivity density in subcortical stroke patients with behavioral dysexecutive symptoms.

Diffusion tensor imaging shows increased physis organization after growth hormone initiation in hypophosphatasia.

Hypophosphatasia is a rare heritable disorder of bone mineralization, with a spectrum of severity based on age of initial presentation. We describe the case of a 14-year-old boy with gene-confirmed inherited hypophosphatasia and growth hormone deficiency, who presented with short stature, unremarkable radiographs, and only minor physeal and metaphyseal changes on magnetic resonance (MR) imaging. Diffusion tensor imaging (DTI) before growth hormone initiation revealed abundant, non-parallel tracts in the physes and metaphyses with loss of the typical columnar organization. After 8months of growth hormone treatment, DTI scans revealed realigned, nearly parallel, longer physeal tracts; duplication of tract volume; and decreased and more typical fractional anisotropy values. DTI can thus visualize physeal tract changes over time, could be a more sensitive diagnostic technique in milder physeal abnormality cases, and may be a potential marker of growth hormone treatment response.

Anisotropic mechanical properties Quantification in skeletal muscle using magnetic resonance elastography and diffusion tensor imaging.

Anisotropic mechanical properties Quantification in skeletal muscle using magnetic resonance elastography and diffusion tensor imaging.

Clinical and neuroradiological differences in obsessive compulsive disorder with and without psychosis.

Clinical and neuroradiological differences in obsessive compulsive disorder with and without psychosis.

Associations of Bronchopulmonary Dysplasia and Infection with School-Age Brain Development in Children Born Preterm.

Associations of Bronchopulmonary Dysplasia and Infection with School-Age Brain Development in Children Born Preterm.

The effect of venlafaxine on language function in patients with subcortical aphasia.

The effect of venlafaxine on language function in patients with subcortical aphasia.

Brain network communication in remission: a comparative study of bipolar and unipolar depression.

Brain network communication in remission: a comparative study of bipolar and unipolar depression.