Diffusion Tensor Imaging Data Research Articles

Diffusion tensor brain imaging at 0.55T: A feasibility study.

To investigate the feasibility of diffusion tensor brain imaging at 0.55T with comparisons against 3T. Diffusion tensor imaging data with 2 mm isotropic resolution was acquired on a cohort of five healthy subjects using both 0.55T and 3T scanners. The signal-to-noise ratio (SNR) of the 0.55T data was improved using a previous SNR-enhancing joint reconstruction method that jointly reconstructs the entire set of diffusion weighted images from k-space using shared-edge constraints. Quantitative diffusion tensor parameters were estimated and compared across field strengths. We also performed a test-retest assessment of repeatability at each field strength. After applying SNR-enhancing joint reconstruction, the diffusion tensor parameters obtained from 0.55T data were strongly correlated ( ) with those obtained from 3T data. Test-retest analysis showed that SNR-enhancing reconstruction improved the repeatability of the 0.55T diffusion tensor parameters. High-resolution in vivo diffusion MRI of the human brain is feasible at 0.55T when appropriate noise-mitigation strategies are applied.

Cerebellar Mutism Syndrome and Dentato-Thalamo-Cortical Tract Disruption in Diffusion Tractography Following Surgery for Medulloblastoma.

Background Cerebellar mutism syndrome (CMS), a complication following medulloblastoma surgery, has been linked to dentato-thalamo-cortical tract (DTCT) injury; the association of the degree of DTCT injury with severity of CMS-related symptoms has not been investigated. Purpose To investigate the association between severity of CMS-related symptoms and degree and patterns of DTCT injury with use of diffusion tensor imaging (DTI), and if laterality of injury influences neurologic symptoms. Materials and Methods This retrospective case-control study used prospectively collected clinical and DTI data on patients with medulloblastoma enrolled in a clinical trial (between July 2016 and February 2020) and healthy controls (between April and November 2017), matched with the age range of the participants with medulloblastoma. CMS was divided into types 1 (CMS1) and 2 (CMS2). Multivariable logistic regression was used to investigate the relationship between CMS likelihood and DTCT injury. Results Overall, 82 participants with medulloblastoma (mean age, 11.0 years ± 5.2 [SD]; 53 male) and 35 healthy controls (mean age, 18.0 years ± 3.06; 18 female) were included. In participants with medulloblastoma, DTCT was absent bilaterally (AB), absent on the right side (AR), absent on the left side (AL), or present bilaterally (PB), while it was PB in all healthy controls. Odds of having CMS were associated with higher degree of DTCT damage (AB, odds ratio = 272.7 [95% CI: 269.68, 275.75; P < .001]; AR, odds ratio = 14.40 [95% CI: 2.84, 101.48; P < .001]; and AL, odds ratio = 8.55 [95% CI: 1.15, 74.14; P < .001). Left (coefficient = -0.07, χ2 = 12.4, P < .001) and right (coefficient = -0.15, χ2 = 33.82, P < .001) DTCT volumes were negatively associated with the odds of CMS. More participants with medulloblastoma with AB showed CMS1; unilateral DTCT absence prevailed in CMS2. Lower DTCT volumes correlated with more severe ataxia. Unilateral DTCT injury caused ipsilateral dysmetria; AB caused symmetric dysmetria. PB indicated better neurologic outcome. Conclusion The severity of CMS-associated mutism, ataxia, and dysmetria was associated with DTCT damage severity. DTCT damage patterns differed between CMS1 and CMS2. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Dorigatti Soldatelli and Ertl-Wagner in this issue.

Diffusion-tensor-imaging 1-year-old and 2-year-old infant brain atlases with comprehensive gray and white matter labels.

Human infancy is marked by fastest postnatal brain structural changes. It also coincides with the onset of many neurodevelopmental disorders. Atlas-based automated structure labeling has been widely used for analyzing various neuroimaging data. However, the relatively large and nonlinear neuroanatomical differences between infant and adult brains can lead to significant offsets of the labeled structures in infant brains when adult brain atlas is used. Age-specific 1- and 2-year-old brain atlases covering all major gray and white matter (GM and WM) structures with diffusion tensor imaging (DTI) and structural MRI are critical for precision medicine for infant population yet have not been established. In this study, high-quality DTI and structural MRI data were obtained from 50 healthy children to build up three-dimensional age-specific 1- and 2-year-old brain templates and atlases. Age-specific templates include a single-subject template as well as two population-averaged templates from linear and nonlinear transformation, respectively. Each age-specific atlas consists of 124 comprehensively labeled major GM and WM structures, including 52 cerebral cortical, 10 deep GM, 40 WM, and 22 brainstem and cerebellar structures. When combined with appropriate registration methods, the established atlases can be used for highly accurate automatic labeling of any given infant brain MRI. We demonstrated that one can automatically and effectively delineate deep WM microstructural development from 3 to 38 months by using these age-specific atlases. These established 1- and 2-year-old infant brain DTI atlases can advance our understanding of typical brain development and serve as clinical anatomical references for brain disorders during infancy.

Diabetes-related clinical and microstructural white matter changes in patients with Alzheimer's disease.

Although there exists substantial epidemiological evidence indicating an elevated risk of dementia in individuals with diabetes, our understanding of the neuropathological underpinnings of the association between Type-2 diabetes mellitus (T2DM) and Alzheimer's disease (AD) remains unclear. This study aims to unveil the microstructural brain changes associated with T2DM in AD and identify the clinical variables contributing to these changes. In this retrospective study involving 64 patients with AD, 31 individuals had concurrent T2DM. The study involved a comparative analysis of diffusion tensor imaging (DTI) images and clinical features between patients with and without T2DM. The FSL FMRIB software library was used for comprehensive preprocessing and tractography analysis of DTI data. After eddy current correction, the "bedpost" model was utilized to model diffusion parameters. Linear regression analysis with a stepwise method was used to predict the clinical variables that could lead to microstructural white matter changes. We observed a significant impairment in the left superior longitudinal fasciculus (SLF) among patients with AD who also had T2DM. This impairment in patients with AD and T2DM was associated with an elevation in creatine levels. The white matter microstructure in the left SLF appears to be sensitive to the impairment of kidney function associated with T2DM in patients with AD. The emergence of AD in association with T2DM may be driven by mechanisms distinct from the typical AD pathology. Compromised renal function in AD could potentially contribute to impaired white matter integrity.

Brain functional magnetic resonance imaging in ICU patients who developed delirium

ObjectiveTo detect brain alterations in intensive care unit (ICU) patients who develop delirium using functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) and to determine their predictive value.MethodsFifty-two patients who were admitted to the ICU of the Affiliated Hospital of Zunyi Medical University between June 2021 and June 2022 were enrolled. Fifteen patients who were diagnosed with delirium by the Intensive Care Delirium Screening Checklist (ICDSC) after MRI were selected as the delirium group, and 15 healthy volunteers who were examined during the same period served as the control group. Both groups underwent fMRI and DTI. Quantitative fMRI and DTI data were compared between the two groups to detect abnormal structural and functional brain damage. The relationships between MRI outliers and clinical indicators in the delirium group were also assessed.ResultsDemographic characteristics and imaging indicators before delirium were not correlated with ICDSC scores after delirium. Compared with the healthy control group, the delirium group had significantly lower regional homogeneity (ReHo) values in the left caudate nucleus and frontal lobe on fMRI. The amplitude of the low-frequency fluctuations (ALFF) values of the delirium group were significantly increased in the hippocampus but significantly decreased in the frontal lobe. Compared with the healthy control group, the delirium group showed reduced mean diffusivity (MD) values, mainly in the right cerebellum and right middle temporal gyrus; reduced radial diffusivity (RD) values, mainly in the anterior cerebellum and right middle temporal gyrus; reduced fractional anisotropy (FA) values, only in the corpus callosum; and reduced axial diffusivity (AD) values, mainly in the anterior cerebellar lobe, right middle temporal gyrus, and left middle frontal gyrus on DTI. The statistical thresholds for quantitative DTI measurements were p &amp;lt; 0.005 at the voxel level and a cluster size &amp;gt; 5.ConclusionAbnormal resting-state brain activity in the left superior frontal gyrus and structural changes in the frontal lobe, temporal lobe, corpus callosum, hippocampus, and cerebellum were observed in ICU patients who developed delirium during hospitalization. Early-brain fMRI and DTI examinations are recommended for the prediction of delirium according to unique quantitative indicators to facilitate early intervention for critically ill patients, reduce the length of hospital stay, and improve patient prognosis.

A novel biomarker selection method using multimodal neuroimaging data.

Identifying biomarkers is essential to obtain the optimal therapeutic benefit while treating patients with late-life depression (LLD). We compare LLD patients with healthy controls (HC) using resting-state functional magnetic resonance and diffusion tensor imaging data to identify neuroimaging biomarkers that may be potentially associated with the underlying pathophysiology of LLD. We implement a Bayesian multimodal local false discovery rate approach for functional connectivity, borrowing strength from structural connectivity to identify disrupted functional connectivity of LLD compared to HC. In the Bayesian framework, we develop an algorithm to control the overall false discovery rate of our findings. We compare our findings with the literature and show that our approach can better detect some regions never discovered before for LLD patients. The Hub of our discovery related to various neurobehavioral disorders can be used to develop behavioral interventions to treat LLD patients who do not respond to antidepressants.

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing.

Neuroimaging biomarkers have shown high potential to map the disease processes in the application to neurodegenerative diseases (NDD), e.g., diffusion tensor imaging (DTI). For DTI, the implementation of a standardized scanning and analysis cascade in clinical trials has potential to be further optimized. Over the last few years, various approaches to improve DTI applications to NDD have been developed. The core issue of this review was to address considerations and limitations of DTI in NDD: we discuss suggestions for improvements of DTI applications to NDD. Based on this technical approach, a set of recommendations was proposed for a standardized DTI scan protocol and an analysis cascade of DTI data pre-and postprocessing and statistical analysis. In summary, considering advantages and limitations of the DTI in NDD we suggest improvements for a standardized framework for a DTI-based protocol to be applied to future imaging studies in NDD, towards the goal to proceed to establish DTI as a biomarker in clinical trials in neurodegeneration.

485 Investigating the Impact of Inflammation on White Matter Tracts using Diffusion Tensor Imaging that may Contribute to Motivational Deficits and Negative Symptoms in Patients with Schizophrenia

OBJECTIVES/GOALS: Previous research has linked inflammation to changes in brain reward circuitry and subsequent negative symptoms in patients with schizophrenia. This project aims to understand brain-immune interactions using diffusion tensor imaging (DTI) to investigate the impact of inflammatory markers on white matter (WM) tracts. METHODS/STUDY POPULATION: Patients with schizophrenia, ages 18 to 45, were recruited at Grady Hospital in Atlanta, GA. All subjects were stable outpatients and underwent extensive medical screening to rule out medical causes of acute inflammation. DTI data was collected from 39 participants on a 3-Tesla Siemens scanner. Blood was collected between 9-11AM for later assay of serum inflammatory markers. Negative symptoms were assessed using the Brief Negative Symptom Scale (BNSS). A diffusion tensor imaging model will be fitted with the data to generate well-known diffusion tensor measures (fractional anisotropy and mean diffusivity). Linear regression will be used to analyze the relationship between DTI measures and inflammation (C-Reactive Protein, CRP), controlling for possible confounders. RESULTS/ANTICIPATED RESULTS: The hypothesis of this proposal is that decreased microstructural integrity in WM tracts between the nucleus accumbens (NAc) and insula will be associated with increased inflammation, which in turn are associated with increased negative symptoms. Negative symptoms include deficits in motivation/pleasure as well as diminished expressivity, and are strongly associated with poor functional outcomes. Based on previous data from this sample demonstrating relationships between CRP and negative symptoms as well as CRP and fMRI functional connectivity between the NAc and insula, we anticipate results that demonstrate similar relationships with WM microstructural integrity, such as functional anisotropy and mean diffusivity. DISCUSSION/SIGNIFICANCE: Given the lack of treatment options for negative symptoms, this research will provide key data to further our understanding of the potential role of inflammation on neural circuits that underlie these symptoms, including WM integrity. This research also has the potential to inform future anti-inflammatory therapies for patients with schizophrenia.

The non-decussating and decussating trigeminothalamic tracts in humans: A combination of connectome-based tractography and histological validation.

Functional anatomical research proposed the existence of a bilateral trigeminal ascending system although the anatomy trajectories of the trigeminothalamic connections cranial to the pons remain largely elusive. This study therefore aimed to clarify the anatomical distributions of the trigeminothalamic connections in humans. Advanced deterministic tractography to an averaged template of diffusion tensor imaging data from 1065 subjects from the Human Connectome Project was used. Seedings masks were placed in Montreal Neurological Institute standard space by use of the BigBrain histological dataset. Waypoint masks of the sensory thalamus was obtained from the Brainnetome Atlas. Tractography results were validated by use of the BigBrain histological dataset and Polarized Light Imaging microscopy. The trigeminothalamic tract bifurcated into a decussating ventral and a non-decussating dorsal tract. The ventral and dorsal tracts ascended to the contralateral thalamus and ipsilateral thalamus and reflected the ventral trigeminothalamic tract and the dorsal trigeminothalamic tract, respectively. The projection of the ventral trigeminothalamic tract and the dorsal trigeminothalamic tract to both thalami confirm the existence of a bilateral trigeminothalamic system in humans. Because our study is strictly anatomical, no further conclusions can be drawn with regard to physiological functionality. Future research should explore if the dorsal trigeminothalamic tract and the ventral trigeminothalamic tract actually transmit signals from noxious stimuli, this offers potential in understanding and possibly treating neuropathology in the orofacial region.

Investigating Dyslexia through Diffusion Tensor Imaging across Ages: A Systematic Review.

Dyslexia is a neurodevelopmental disorder that presents a deficit in accuracy and/or fluency while reading or spelling that is not expected given the level of cognitive functioning. Research indicates brain structural changes mainly in the left hemisphere, comprising arcuate fasciculus (AF) and corona radiata (CR). The purpose of this systematic review is to better understand the possible methods for analyzing Diffusion Tensor Imaging (DTI) data while accounting for the characteristics of dyslexia in the last decade of the literature. Among 124 articles screened from PubMed and Scopus, 49 met inclusion criteria, focusing on dyslexia without neurological or psychiatric comorbidities. Article selection involved paired evaluation, with a third reviewer resolving discrepancies. The selected articles were analyzed using two topics: (1) a demographic and cognitive assessment of the sample and (2) DTI acquisition and analysis. Predominantly, studies centered on English-speaking children with reading difficulties, with preserved non-verbal intelligence, attention, and memory, and deficits in reading tests, rapid automatic naming, and phonological awareness. Structural differences were found mainly in the left AF in all ages and in the bilateral superior longitudinal fasciculus for readers-children and adults. A better understanding of structural brain changes of dyslexia and neuroadaptations can be a guide for future interventions.

Functional organization of the human corpus callosum unveiled with BOLD-fMRI gradients

Abstract Gradients capture the underlying functional organization of the brain. Cortical gradients have been well characterized, however very little is known about the underlying gradient of the white matter. Here, we proposed a functionally gradient mapping of the corpus callosum by using blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD-fMRI), which for the first time uncovered three distinct but stable spatial axes: posterior-anterior, dorsal-ventral, and left-right. The three spatial patterns were replicated in another independent cohort and robust across scanning conditions. We further associated the three gradient maps with brain anatomy, connectome, and task-related brain functions, by using structural magnetic resonance imaging, both resting-state and task fMRI, and diffusion tensor imaging data. The posterior-anterior gradient distribution of the corpus callosum showed a similar pattern with the cerebral cortex, gradually extending from the primary cortex to the transmodal cortex. The dorsal-ventral gradient distribution revealed an N-shaped pattern from the primary cortex to the higher-order cognitive cortex. The posterior-anterior and dorsal-ventral gradient maps were also associated with white-matter microstructures, such as fractional anisotropy and myelin water fraction. The left-right gradient showed an inverted V-shaped pattern, which delineated the inter-hemisphere separation. These findings provide fundamental insight into the functional organization of the human corpus callosum, unveiling potential patterns of functional interaction with the cerebral cortex and their associations with cognitive behaviors.

Sex-Specific Distributed White Matter Microarchitectural Alterations in Preadolescent Youths With Anxiety Disorders: A Mega-Analytic Study.

Anxiety disorders are among the most common psychiatric disorders in youths and emerge during childhood. This is also a period of rapid white matter (WM) development, which is critical for efficient neuronal communication. Previous work in preadolescent children with anxiety disorders demonstrated anxiety disorder-related reductions in WM microstructural integrity (fractional anisotropy [FA]) in the uncinate fasciculus (UF), the major WM tract facilitating prefrontal cortical-limbic structural connectivity. Importantly, this association was found only in boys with anxiety disorders. To confirm this finding and more comprehensively understand WM changes in childhood anxiety, this mega-analytic study characterizes WM alterations related to anxiety disorders and sex in the largest sample of preadolescent children to date. Diffusion tensor imaging data from published studies of preadolescent children with anxiety disorders and healthy volunteers (ages 8-12) (N=198) were combined with a new data set (N=97) for a total sample of 165 children with anxiety disorders and 132 healthy volunteers. Children with anxiety disorders met DSM-5 criteria for current generalized, separation, and/or social anxiety disorder. Analyses of tractography and voxel-wise data assessed between-group differences (anxiety disorder vs. healthy volunteer), effects of sex, and their interaction. Tract-based and voxel-wise analyses confirmed a significant reduction in UF FA in boys but not girls with anxiety disorders. Results also demonstrated other significant widespread anxiety disorder-related WM alterations specifically in boys, including in multiple commissural, association, projection, and brainstem regions. In addition to confirming male-specific anxiety disorder-related reductions in UF FA, the results demonstrate that anxiety disorders in boys and not girls are associated with broadly distributed WM alterations across the brain. These findings support further studies focused on understanding the extent to which WM alterations in boys with anxiety disorders are involved in pathophysiological processes that mediate anxiety disorders. The findings also suggest the possibility that WM microarchitecture could serve as a novel treatment target for childhood anxiety disorders.

Integration of whole-exome sequencing and structural neuroimaging analysis in major depressive disorder: a joint study

Major depressive disorder (MDD) is a common mental illness worldwide and is triggered by an intricate interplay between environmental and genetic factors. Although there are several studies on common variants in MDD, studies on rare variants are relatively limited. In addition, few studies have examined the genetic contributions to neurostructural alterations in MDD using whole-exome sequencing (WES). We performed WES in 367 patients with MDD and 161 healthy controls (HCs) to detect germline and copy number variations in the Korean population. Gene-based rare variants were analyzed to investigate the association between the genes and individuals, followed by neuroimaging-genetic analysis to explore the neural mechanisms underlying the genetic impact in 234 patients with MDD and 135 HCs using diffusion tensor imaging data. We identified 40 MDD-related genes and observed 95 recurrent regions of copy number variations. We also discovered a novel gene, FRMPD3, carrying rare variants that influence MDD. In addition, the single nucleotide polymorphism rs771995197 in the MUC6 gene was significantly associated with the integrity of widespread white matter tracts. Moreover, we identified 918 rare exonic missense variants in genes associated with MDD susceptibility. We postulate that rare variants of FRMPD3 may contribute significantly to MDD, with a mild penetration effect.

Empirical assessment of the assumptions of ComBat with diffusion tensor imaging.

Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that provides unique information about white matter microstructure in the brain but is susceptible to confounding effects introduced by scanner or acquisition differences. ComBat is a leading approach for addressing these site biases. However, despite its frequent use for harmonization, ComBat's robustness toward site dissimilarities and overall cohort size have not yet been evaluated in terms of DTI. As a baseline, we match participants from two sites to create a "silver standard" that simulates a cohort for multi-site harmonization. Across sites, we harmonize mean fractional anisotropy and mean diffusivity, calculated using participant DTI data, for the regions of interest defined by the JHU EVE-Type III atlas. We bootstrap 10 iterations at 19 levels of total sample size, 10 levels of sample size imbalance between sites, and 6 levels of mean age difference between sites to quantify (i), the linear regression coefficient of the relationship between FA and age; (ii), the ComBat-estimated site-shift; and (iii), the ComBat-estimated site-scaling. We characterize the reliability of ComBat by evaluating the root mean squared error in these three metrics and examine if there is a correlation between the reliability of ComBat and a violation of assumptions. ComBat remains well behaved for when and when the mean age difference is less than 4 years. The assumptions of the ComBat model regarding the normality of residual distributions are not violated as the model becomes unstable. Prior to harmonization of DTI data with ComBat, the input cohort should be examined for size and covariate distributions of each site. Direct assessment of residual distributions is less informative on stability than bootstrap analysis. We caution use ComBat of in situations that do not conform to the above thresholds.

Exploiting macro- and micro-structural brain changes for improved Parkinson’s disease classification from MRI data

Parkinson’s disease (PD) is the second most common neurodegenerative disease. Accurate PD diagnosis is crucial for effective treatment and prognosis but can be challenging, especially at early disease stages. This study aimed to develop and evaluate an explainable deep learning model for PD classification from multimodal neuroimaging data. The model was trained using one of the largest collections of T1-weighted and diffusion-tensor magnetic resonance imaging (MRI) datasets. A total of 1264 datasets from eight different studies were collected, including 611 PD patients and 653 healthy controls (HC). These datasets were pre-processed and non-linearly registered to the MNI PD25 atlas. Six imaging maps describing the macro- and micro-structural integrity of brain tissues complemented with age and sex parameters were used to train a convolutional neural network (CNN) to classify PD/HC subjects. Explainability of the model’s decision-making was achieved using SmoothGrad saliency maps, highlighting important brain regions. The CNN was trained using a 75%/10%/15% train/validation/test split stratified by diagnosis, sex, age, and study, achieving a ROC-AUC of 0.89, accuracy of 80.8%, specificity of 82.4%, and sensitivity of 79.1% on the test set. Saliency maps revealed that diffusion tensor imaging data, especially fractional anisotropy, was more important for the classification than T1-weighted data, highlighting subcortical regions such as the brainstem, thalamus, amygdala, hippocampus, and cortical areas. The proposed model, trained on a large multimodal MRI database, can classify PD patients and HC subjects with high accuracy and clinically reasonable explanations, suggesting that micro-structural brain changes play an essential role in the disease course.

Structural changes in spinal cord following optic neuritis: Insights from quantitative spinal MRI

ObjectivesPrevious studies have demonstrated that optic neuritis (ON) affects brain plasticity. However, whether ON affects the spinal cord remains unclear. We aimed to investigate the spinal cord changes in ON and their associations with disability. MethodsA total of 101 ON patients, and 41 healthy controls (HC) were retrospectively recruited. High-resolution imaging was conducted using a Magnetization Prepared Rapid Acquisition Gradient-Echo (MP-RAGE) sequence for T1-weighted images and an echo planar imaging (EPI) sequence for Diffusion Tensor Imaging (DTI) data collection. Additionally, patients' disability and cognitive impairment were evaluated using the Expanded Disability Status Scale (EDSS) and the Paced Auditory Serial Addition Test (PASAT), respectively. The quantitative spinal MRI was employed to examine the cross-sectional area (CSA) and diffusion indicators, with a specific focus on calculating the average values across the C2-C7 cervical spinal cord segments. CSA, fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were compared between groups. Correlation analyses were performed between CSA, diffusion indicators, and clinical variables. ResultsNo significant differences were found in CSA between ON patients and HCs. MD (p = 0.007) and RD (p = 0.018) were increased in ON patients compared with HCs, and AD was decreased in ON (p = 0.013). The AD values of the ON patients were significantly positively correlated with PASAT scores (r = 0.37, p < 0.001). ConclusionsThis study provided imaging evidence for DTI abnormalities in patients with ON. Spinal cord DTI can improve our knowledge of the path physiology of ON, and clinical progression.

Changes in Structural Neural Networks in the Recovery Process of Motor Paralysis after Stroke.

In recent years, neurorehabilitation has been actively used to treat motor paralysis after stroke. However, the impacts of rehabilitation on neural networks in the brain remain largely unknown. Therefore, we investigated changes in structural neural networks after rehabilitation therapy in patients who received a combination of low-frequency repetitive transcranial magnetic stimulation (LF-rTMS) and intensive occupational therapy (intensive-OT) as neurorehabilitation. Fugl-Meyer assessment (FMA) for upper extremity (FMA-UE) and Action Research Arm Test (ARAT), both of which reflected upper limb motor function, were conducted before and after rehabilitation therapy. At the same time, diffusion tensor imaging (DTI) and three-dimensional T1-weighted imaging (3D T1WI) were performed. After analyzing the structural connectome based on DTI data, measures related to connectivity in neural networks were calculated using graph theory. Rehabilitation therapy prompted a significant increase in connectivity with the isthmus of the cingulate gyrus in the ipsilesional hemisphere (p < 0.05) in patients with left-sided paralysis, as well as a significant decrease in connectivity with the ipsilesional postcentral gyrus (p < 0.05). These results indicate that LF-rTMS combined with intensive-OT may facilitate motor function recovery by enhancing the functional roles of networks in motor-related areas of the ipsilesional cerebral hemisphere.

White matter development and language abilities during infancy in autism spectrum disorder.

White matter (WM) fiber tract differences are present in autism spectrum disorder (ASD) and could be important markers of behavior. One of the earliest phenotypic differences in ASD are language atypicalities. Although language has been linked to WM in typical development, no work has evaluated this association in early ASD. Participants came from the Infant Brain Imaging Study and included 321 infant siblings of children with ASD at high likelihood (HL) for developing ASD; 70 HL infants were later diagnosed with ASD (HL-ASD), and 251 HL infants were not diagnosed with ASD (HL-Neg). A control sample of 140 low likelihood infants not diagnosed with ASD (LL-Neg) were also included. Infants contributed expressive language, receptive language, and diffusion tensor imaging data at 6-, 12-, and 24 months. Mixed effects regression models were conducted to evaluate associations between WM and language trajectories. Trajectories of microstructural changes in the right arcuate fasciculus were associated with expressive language development. HL-ASD infants demonstrated a different developmental pattern compared to the HL-Neg and LL-Neg groups, wherein the HL-ASD group exhibited a positive association between WM fractional anisotropy and language whereas HL-Neg and LL-Neg groups showed weak or no association. No other fiber tracts demonstrated significant associations with language. In conclusion, results indicated arcuate fasciculus WM is linked to language in early toddlerhood for autistic toddlers, with the strongest associations emerging around 24 months. To our knowledge, this is the first study to evaluate associations between language and WM development during the pre-symptomatic period in ASD.

Urban environment during pregnancy and childhood and white matter microstructure in preadolescence in two European birth cohorts

Growing evidence suggests that urban environment may influence cognition and behavior in children, but the underlying pollutant and neurobiological mechanisms are unclear. We evaluated the association of built environment and urban natural space indicators during pregnancy and childhood with brain white matter microstructure in preadolescents, and examined the potential mediating role of air pollution and road-traffic noise. We used data of the Generation R Study, a population-based birth cohort in Rotterdam, the Netherlands (n = 2725; 2002–2006) for the primary analyses. Replication of the main findings was attempted on an independent neuroimaging dataset from the PELAGIE birth cohort, France (n = 95; 2002–2006). We assessed exposures to 12 built environment and 4 urban natural spaces indicators from conception up to 9 years of age. We computed 2 white matter microstructure outcomes (i.e., average of fractional anisotropy (FA) and mean diffusivity (MD) from 12 white matte tracts) from diffusion tensor imaging data. Greater distance to the nearest major green space during pregnancy was associated with higher whole-brain FA (0.001 (95%CI 0.000; 0.002) per 7 m increase), and higher land use diversity during childhood was associated with lower whole-brain MD (−0.001 (95%CI -0.002; −0.000) per 0.12-point increase), with no evidence of mediation by air pollution nor road-traffic noise. Higher percentage of transport and lower surrounding greenness during pregnancy were associated with lower whole-brain FA, and road-traffic noise mediated 19% and 52% of these associations, respectively. We found estimates in the same direction in the PELAGIE cohort, although confidence intervals were larger and included the null. This study suggests an association between urban environment and white matter microstructure, mainly through road-traffic noise, indicating that greater access to green space nearby might promote white matter development.

Distinguishing schizophrenia and bipolar disorder through a Multiclass Classification model based on multimodal neuroimaging data

This study aimed to identify neural biomarkers for schizophrenia (SZ) and bipolar disorder (BP) by analyzing multimodal neuroimaging. Utilizing data from structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and resting-state functional magnetic resonance imaging (rs-fMRI), multiclass classification models were created for SZ, BP, and healthy controls (HC). A total of 113 participants (BP: 31, SZ: 39, and HC: 43) were recruited under strict enrollment control, from which 272, 200, and 1875 features were extracted from sMRI, DTI, and rs-fMRI data, respectively. A support vector machine (SVM) with recursive feature elimination (RFE) was employed to build the models using a one-against-one approach and leave-one-out cross-validation, achieving a classification accuracy of 70.8%. The most discriminative features were primarily from rs-fMRI, along with significant findings in sMRI and DTI. Key biomarkers identified included the increased thickness of the left cuneus cortex and decreased regional functional connectivity strength (rFCS) in the left supramarginal gyrus as shared indicators for BP and SZ. Additionally, decreased fractional anisotropy in the left superior fronto-occipital fasciculus was suggested as specific to BP, while decreased rFCS in the left inferior parietal area might serve as a specific biomarker for SZ. These findings underscore the potential of multimodal neuroimaging in distinguishing between BP and SZ and contribute to the understanding of their neural underpinnings.