Diffusion Tensor Research Articles

Aberrant white matter structural connectivity of nucleus accumbens in patients with major depressive disorder: A probabilistic fibre tracing study.

Aberrant white matter structural connectivity of nucleus accumbens in patients with major depressive disorder: A probabilistic fibre tracing study.

Altered default mode network and glymphatic function in insomnia with depression: A multimodal MRI study.

Altered default mode network and glymphatic function in insomnia with depression: A multimodal MRI study.

Unraveling Hierarchical Brain Dysfunction in Major Depressive Disorder: A Multimodal Imaging and Transcriptomic Approach.

Major depressive disorder (MDD) is characterized by deficits in sensory processing and higher-order executive functions, reflecting dysfunction in the hierarchical organization of the brain. However, current methods for investigating brain hierarchy in MDD have not fully integrated multimodal data, and the underlying biological mechanisms remain poorly understood. We acquired diffusion tensor imaging and functional magnetic resonance imaging (fMRI) data from 100 participants with MDD and 77 healthy controls (HCs). The structural-decoupling index (SDI) was employed to quantify the hierarchical organization in MDD and HC. We identified intergroup differences in the hierarchical brain organization and explored the molecular mechanism related to significantly different brain regions by investigating genetic factors and their relationship with neurotransmitter receptors/transporters. Finally, 10-fold cross-validation was used to develop a support vector machine (SVM) classification model. Dysfunctional hierarchical organization in MDD was characterized by increased SDI in the bilateral somatosensory cortex, while decreased SDI was observed in the bilateral visual, prefrontal, and parietal cortices, as well as the left orbitofrontal cortex and temporal pole. Moreover, SDI alterations showed negative correlations with neurotransmitters, including 5-HT1a, 5-HT2a, D1, GABAa, SERT, and mGluR5. The SDI alteration-related genes were enriched in kinase binding. After 10-fold cross-validation, the SVM exhibited a mean accuracy of 0.767 (area under the curve = 0.972). Our research employed multimodal data to investigate hierarchical brain dysfunction in MDD and established its associations with neurotransmitters and transcriptome profiles. This approach may improve the understanding of the neural, biological, and molecular genetic underpinning of SDI in MDD.

Age-related change of glymphatic function in normative children assessed using diffusion tensor imaging-analysis along the perivascular space.

Age-related change of glymphatic function in normative children assessed using diffusion tensor imaging-analysis along the perivascular space.

Thalamic free water elevation and its association with glymphatic dysfunction in Parkinson's disease: A cross-sectional and longitudinal study.

Thalamic free water elevation and its association with glymphatic dysfunction in Parkinson's disease: A cross-sectional and longitudinal study.

Association of magnetic resonance imaging glymphatic function with gray matter volume loss and cognitive impairment in Alzheimer’s disease: a diffusion tensor image analysis along the perivascular space (DTI-ALPS) study

Association of magnetic resonance imaging glymphatic function with gray matter volume loss and cognitive impairment in Alzheimer’s disease: a diffusion tensor image analysis along the perivascular space (DTI-ALPS) study

Association between diffusion MRI-based measures of neurite microstructure and risk of Alzheimer's disease.

Association between diffusion MRI-based measures of neurite microstructure and risk of Alzheimer's disease.

Intraindividual variability in daily cognitive performance is associated with white matter organization of the superior longitudinal fasciculus in healthy adults.

Intraindividual variability in daily cognitive performance is associated with white matter organization of the superior longitudinal fasciculus in healthy adults.

Prediction of induction chemotherapy response in locoregionally advanced nasopharyngeal carcinoma based on three pretreatment non-Gaussian diffusion MRI models

ObjectivesTo explore the value of continuous-time random walk (CTRW), fractional order calculus (FROC), and stretched exponential model (SEM) in predicting for response to induction chemotherapy (IC) in nasopharyngeal carcinoma (NPC).MethodsThis prospective study included the NPC participants (n = 79) who underwent non-Gaussian (CTRW, FROC, and SEM) model from December 2023 to October 2024. Eight diffusion parameters, namely αCTRW, βCTRW, DmCTRW, βFROC, µFROC, DFROC, αSEM, and DDCSEM of the primary tumor, were derived from three diffusion models before treatment. These diffusion metrics were compared between the response and non-response groups, as defined by the RECIST 1.1 criteria. Univariate and multivariate logistic analysis was used to determine the optimal diffusion metrics and clinicopathologic variables for classifying the IC response. Predictive models were established using logistic regression. Receiver operating characteristic (ROC) curves were used to evaluate their predictive ability.ResultsParticipants enrolled in this study were classified into response group (n = 60) and non-response group (n = 19). Participants who responded well to IC had lower αCTRW and βCTRW values (p = 0.015, p = 0.011). αCTRW and βCTRW were independently associated with the response of chemotherapy in NPC (odds ratio [OR]: 0.444 [95% confidence interval [CI], 0.214–0.922], p = 0.029; 0.338 [95% CI, 0.139–0.822], p = 0.017). ROC analysis showed the predictive performance of αCTRW, βCTRW, and α+βCTRW values for response to IC (AUCs of 0.710, [95% CI, 0.597–0.806], 0.713 [95% CI, 0.600-0.809], and 0.829 [95% CI, 0.728–0.904], respectively) in NPC participants.ConclusionsThe developed model combining αCTRW and βCTRW showed good performance in predicting treatment response to IC in NPC.Relevance statementWe developed a logistic regression model based on pre-treatment non-Gaussian diffusion MRI parameters to reliably predict early response to induction chemotherapy in locally advanced nasopharyngeal carcinoma. This model may aid in personalizing treatment and minimizing unnecessary toxicity for non-responders.

White matter microstructure in language tracts in youth with perinatal stroke.

Perinatal strokes occur more commonly in the left hemisphere and often impact language areas, yet language disability only occurs in a small proportion of cases. Functional imaging studies investigating language processing have shown that perinatal stroke in the left hemisphere may result in contralesional shifts of activity, but none have investigated the structure of white matter connections in such altered language network conditions. Diffusion tensor imaging and neurite orientation dispersion and density imaging offer robust, microstructurally-sensitive metrics which can richly characterize tracts known to support language function. In a sample of 105 participants aged 6 to 19, 73 participants with perinatal stroke and 32 typically-developing controls, we applied these techniques to evaluate differences in microstructure of the arcuate fasciculus and uncinate fasciculus, two tracts classically associated with language, following perinatal stroke while controlling for age, sex, and lesion volume. We identified widespread differences in microstructure in both hemispheres for the arcuate and uncinate fasciculi in perinatal stroke participants compared to controls. Subtypes of perinatal stroke presented differently, with arterial ischemic stroke lesions showing more structural differences than periventricular infarction lesions. Differences between perinatal stroke subtypes were observed for both tracts in both hemispheres. Overall, we demonstrate that white matter microstructure of bilateral language networks is impacted by unilateral perinatal stroke. These bilateral differences in white matter structure after unilateral injury suggest that neuroplastic mechanisms may operate in both hemispheres during development with possible functional implications that could inform customized patient-centered rehabilitation strategies.

Multi-stage Alzheimer's disease diagnosis using Diffusion Tensor Imaging and Cost-Sensitive Machine Learning

Multi-stage Alzheimer's disease diagnosis using Diffusion Tensor Imaging and Cost-Sensitive Machine Learning

Research hotspots and trends in the application of diffusion tensor imaging in ischemic stroke: a bibliometric analysis (2003–2024)

BackgroundIschemic stroke (IS) is a limited ischemic necrosis or softening of brain tissue caused by impaired blood supply to the brain, ischemia, and hypoxia, ultimately leading to various neurological dysfunctions. In recent years, with the continuous development of imaging technology, diffusion tensor imaging (DTI) has made many advances in the field of IS. However, bibliometric analysis in this field is still lacking. This study aims to investigate the present status of study progress, research hotspots, and possible research trends in the application of DTI in IS through bibliometric analysis methods.MethodsThe Web of Science Core Collection (WoSCC) database was searched for relevant literature on DTI in IS from 2003 to 2024. Using VOSviewer, CiteSpace, and R package Bibliometrix to visualize and analyze countries, publications, authors, co-citations, and keywords.ResultsA total of 493 papers from 45 countries were incorporated. The number of DTI-related publications in IS has been increasing year by year. The three primary publishing nations are the United States, China, and the United Kingdom. Harvard University is the research institution with the most extensive of publications. STROKE is the most contributing and cited journal in the field. Markus, Hugh S is the author with the highest number of publications. Thomalla, G. is the author with the highest number of citations. The analysis of keywords and co-cited literature can suggest the primary research directions and trends in the field containing diffusion tensor imaging, ischemic stroke, white matter (WM), Wallerian degeneration (WD), corticospinal tract (CST), fractional anisotropy (FA), recovery and biomarker for motor function.ConclusionAccording to the bibliometric study's findings, this area is progressively gaining the attention of researchers and may become a research hotspot in IS. However, the current study still needs to strengthen cross-regional cooperation, and higher quality and broader research is still necessary to lay the foundation for reaching the consensus of various experts.

Prediction of Motor Recovery after Subacute Cerebral Infarction: Role of Corticocerebellar Pathway Integrity.

BackgroundThe cerebellar cortex has gradually become a promising therapeutic target for improving motor recovery post-cerebral infarction, potentially dependent on the structural integrity of motor-related corticocerebellar pathways (CCP). However, the relationship between the imaging markers of motor-related CCP and motor prognosis remains inadequately explored. Utilizing diffusion tensor imaging (DTI), this study aims to longitudinally assess the role of motor-related CCP in predicting motor recovery for both upper and lower extremities following cerebral infarction.MethodsTwenty-nine patients with right middle cerebral artery (MCA) infarction underwent 2 DTI scans 7 to 14 and 30 days after onset, and 29 age-sex matched controls received 1 scan. Fractional anisotropy (FA) values were measured for corticospinal tract (CST) and CCP (cortico-pontocerebellar tract, CPCT; dentate-thalamocortical tract, DTCT; dorsal-spinocerebellar tract, DSCT). Multivariate regression analyses were performed to examine the relationships between DTI parameters and Fugl-Meyer Assessment (FMA).ResultsCompared to the control group, FA and FA asymmetry index (FA-AI) of CST, DTCT, and DSCT on the affected side were significantly reduced. In the linear regression model, the decreased FA-AI of DTCT was a strong predictor for upper FMA (R2adj = 0.271, P = .022), while the FA-AI of DSCT independently predicted lower limb FMA (R2adj = 0.400, P = .019).ConclusionsIn patients of MCA infarction, FA-AI of motor-related CCP may be a valuable imaging indicator for predicting motor outcomes. The DTI-assessed structural integrity of the cerebellar ascending fiber tracts (DTCT and DSCT) may correlate with the motor recovery of the upper and lower extremities, respectively.

Deep Learning Spinal Cord Segmentation Based on B0 Reference for Diffusion Tensor Imaging Analysis in Cervical Spondylotic Myelopathy

Diffusion Tensor Imaging (DTI) is a crucial imaging technique for accurately assessing pathological changes in Cervical Spondylotic Myelopathy (CSM). However, the segmentation of spinal cord DTI images primarily relies on manual methods, which are labor-intensive and heavily dependent on the subjective experience of clinicians, and existing research on DTI automatic segmentation cannot fully satisfy clinical requirements. Thus, this poses significant challenges for DTI-assisted diagnostic decision-making. This study aimed to deliver AI-driven segmentation for spinal cord DTI. To achieve this goal, a comparison experiment of candidate input features was conducted, with the preliminary results confirming the effectiveness of applying a diffusion-free image (B0 image) for DTI segmentation. Furthermore, a deep-learning-based model, named SCS-Net (Spinal Cord Segmentation Network), was proposed accordingly. The model applies a classical U-shaped architecture with a lightweight feature extraction module, which can effectively alleviate the training data scarcity problem. The proposed method supports eight-region spinal cord segmentation, i.e., the lateral, dorsal, ventral, and gray matter areas on the left and right sides. To evaluate this method, 89 CSM patients from a single center were collected. The model demonstrated satisfactory accuracy for both general segmentation metrics (precision, recall, and Dice coefficient) and a DTI-specific feature index. In particular, the proposed model’s error rate for the DTI-specific feature index was evaluated as 5.32%, 10.14%, 7.37%, and 5.70% on the left side, and 4.60%, 9.60%, 8.74%, and 6.27% on the right side of the spinal cord, respectively, affirming the model’s consistent performance for radiological rationality. In conclusion, the proposed AI-driven segmentation model significantly reduces the dependence on DTI manual interpretation, providing a feasible solution that can improve potential diagnostic outcomes for patients.

From Tumor to Network: Functional Connectome Heterogeneity and Alterations in Brain Tumors—A Multimodal Neuroimaging Narrative Review

Intracranial tumors such as gliomas, meningiomas, and brain metastases induce complex alterations in brain function beyond their focal presence. Modern connectomic and neuroimaging approaches, including resting-state functional MRI (rs-fMRI) and diffusion MRI, have revealed that these tumors disrupt and reorganize large-scale brain networks in heterogeneous ways. In adult patients, diffuse gliomas infiltrate neural circuits, causing both local disconnections and widespread functional changes that often extend into structurally intact regions. Meningiomas and metastases, though typically well-circumscribed, can perturb networks via mass effect, edema, and diaschisis, sometimes provoking global “dysconnectivity” related to cognitive deficits. Therefore, this review synthesizes interdisciplinary evidence from neuroscience, oncology, and neuroimaging on how intracranial tumors disrupt functional brain connectivity pre- and post-surgery. We discuss how functional heterogeneity (i.e., differences in network involvement due to tumor type, location, and histo-molecular profile) manifests in connectomic analyses, from altered default mode and salience network activity to changes in structural–functional coupling. The clinical relevance of these network effects is examined, highlighting implications for pre-surgical planning, prognostication of neurocognitive outcomes, and post-operative recovery. Gliomas demonstrate remarkable functional plasticity, with network remodeling that may correlate with tumor genotype (e.g., IDH mutation), while meningioma-related edema and metastasis location modulate the extent of network disturbance. Finally, we explore future directions, including imaging-guided therapies and “network-aware” neurosurgical strategies that aim to preserve and restore brain connectivity. Understanding functional heterogeneity in brain tumors through a connectomic lens not only provides insights into the neuroscience of cancer but also informs more effective, personalized approaches to neuro-oncologic care.

Gait variability as a marker of white matter integrity in individuals with Down syndrome

INTRODUCTIONIndividuals with Down syndrome (DS) face a significant risk of neurodegeneration, and gait variability may serve as a clinical biomarker of neurological health. This longitudinal parent substudy aimed to explore relationships between gait, white matter (WM) integrity, and cognitive function in DS.METHODSThe associations were investigated between magnetic resonance imaging diffusion tensor imaging (DTI), cognition, and self‐paced gait data from 22 DS participants (mean age ± SD 37 ± 7.5 years).RESULTSDTI measures, such as lower fractional anisotropy (FA) and higher mean diffusivity, were correlated with greater step time variability but not normalized velocities. Lower cognitive scores on the Vineland Adaptive Behavior Composite, Dementia Questionnaire for People with Learning Disabilities, and Motor Skill subscale were correlated with FA.DISCUSSIONGait variability correlates with WM integrity and cognitive function in DS, suggesting that gait and DTI measures may serve as clinical markers of neurological decline.HighlightsGait variability linked to white matter integrity in individuals with Down syndrome (DS).Lower fractional anisotropy and higher mean diffusivity are associated with increased step time variability in DS.Cognitive decline is tied to white matter changes in motor‐related brain regions.Gait analysis alongside diffusion tensor imaging may aid in screening for cognitive impairment in DS.

Mapping the Olfactory Brain: A Systematic Review of Structural and Functional Magnetic Resonance Imaging Changes Following COVID-19 Smell Loss

Background: Olfactory dysfunction (OD)—including anosmia and hyposmia—is a common and often persistent outcome of viral infections. This systematic review consolidates findings from structural and functional MRI studies to explore how COVID-19 SARS-CoV-2-induced smell loss alters the brain. Considerable heterogeneity was observed across studies, influenced by differences in methodology, population characteristics, imaging timelines, and OD classification. Methods: Following PRISMA guidelines, we conducted a systematic search of PubMed/MEDLINE, Scopus, and Web of Science to identify MRI-based studies examining COVID-19’s SARS-CoV-2 OD. Twenty-four studies were included and categorized based on imaging focus: (1) olfactory bulb (OB), (2) olfactory sulcus (OS), (3) grey and white matter changes, (4) task-based brain activation, and (5) resting-state functional connectivity. Demographic and imaging data were extracted and analyzed accordingly. Results: Structural imaging revealed consistent reductions in olfactory bulb volume (OBV) and olfactory sulcus depth (OSD), especially among individuals with OD persisting beyond three months, suggestive of inflammation and neurodegeneration in olfactory-associated regions like the orbitofrontal cortex and thalamus. Functional MRI studies showed increased connectivity in early-stage OD within regions such as the piriform and orbitofrontal cortices, possibly reflecting compensatory activity. In contrast, prolonged OD was associated with reduced activation and diminished connectivity, indicating a decline in olfactory processing capacity. Disruptions in the default mode network (DMN) and limbic areas further point to secondary cognitive and emotional effects. Diffusion tensor imaging (DTI) findings—such as decreased fractional anisotropy (FA) and increased mean diffusivity (MD)—highlight white matter microstructural compromise in individuals with long-term OD. Conclusions: COVID-19’s SARS-CoV-2 olfactory dysfunction is associated with a range of cerebral alterations that evolve with the duration and severity of smell loss. Persistent dysfunction correlates with greater neural damage, underscoring the need for longitudinal neuroimaging studies to better understand recovery dynamics and guide therapeutic strategies.

Comparison of post-stroke white matter assessment using disconnectome-symptom mapping versus quantitative diffusion MRI.

Comparison of post-stroke white matter assessment using disconnectome-symptom mapping versus quantitative diffusion MRI.

Gait Recovery in Patients with Stroke with Severe Motor Damage: The Prognostic Role of Sensory Pathway Preservation.

Gait Recovery in Patients with Stroke with Severe Motor Damage: The Prognostic Role of Sensory Pathway Preservation.

Deep learning-based diffusion MRI tractography: Integrating spatial and anatomical information.

Deep learning-based diffusion MRI tractography: Integrating spatial and anatomical information.