Major White Matter Fiber Tracts Research Articles

Micro structural abnormalities of the Cingulum in children with focal epilepsy: DTI study

Abstract Aim of the work The aim of this study is to evaluate the microstructural alteration implicating the Cingulum (CG) as one of the major limbic system white matter fiber tracts; in children with focal epilepsy. Patients and methods Our study was carried out on twenty-one children with the clinical and EEG diagnosis of focal epilepsy and 22 control subjects who were age and sex matched to patient group in the study. All patients underwent an epilepsy specific MRI protocol. DTI and fiber tractography was performed to all subjects as well. Fractional anisotropy (FA), axial diffusivity, radial diffusivity (RD) and trace were calculated for CG. DTI metrics were compared between both hemispheres as well as between both groups patient group and control group. A p-value less than .05 was considered significant. Results Asymmetry in FA, RD, trace and AD values of the CG was demonstrated in patients with focal epilepsy between both hemispheres with significantly lower FA and higher diffusivity values reaching p < 0.001in CG in both hemispheres in patients with focal epilepsy when compared together regardless the seizure focus. Compared with controls, patients with left focal epilepsy demonstrated significant lower FA and higher diffusivity indices in both ipsilateral and contralateral fiber tracts as well p < 0.05. Patients with right focal epilepsy showed no significant asymmetry in different DTI metrics when compared to controls. Conclusion Reduction in FA values along with increased diffusivity measures in the CG reflects the microstructural changes taking place in children with focal epilepsy.

Symptom-Based Profiling and Multimodal Neuroimaging of a Large Preteenage Population Identifies Distinct Obsessive-Compulsive Disorder–like Subtypes With Neurocognitive Differences

BackgroundObsessive-compulsive disorder (OCD) is characterized by both internalizing (anxiety) and externalizing (compulsivity) symptoms. Currently, little is known about their interrelationships and their relative contributions to disease heterogeneity. Our goal is to resolve affective and cognitive symptom heterogeneity related to internalized and externalized symptom dimensions by determining subtypes of children with OCD symptoms, and to identify any corresponding neural differences. MethodsA total of 1269 children with OCD symptoms screened using the Child Behavior Checklist Obsessive-Compulsive Symptom scale and 3987 matched control subjects were obtained from the Adolescent Brain Cognitive Development (ABCD) Study. Consensus hierarchical clustering was used to cluster children with OCD symptoms into distinct subtypes. Ten neurocognitive task scores and 20 Child Behavior Checklist syndrome scales were used to characterize cognitive/behavioral differences. Gray matter volume, fractional anisotropy of major white matter fiber tracts, and functional connectivity among networks were used in case-control studies. ResultsWe identified two subgroups with contrasting patterns in internalized and externalized dimensions. Group 1 showed compulsive thoughts and repeated acts but relatively low anxiety symptoms, whereas group 2 exhibited higher anxiety and perfectionism and relatively low repetitive behavior. Only group 1 had significant cognitive impairments and gray matter volume reductions in the bilateral inferior parietal lobe, precentral gyrus, and precuneus gyrus, and had white matter tract fractional anisotropy reductions in the corticostriatal fasciculus. ConclusionsChildren with OCD symptoms are heterogeneous at the level of symptom clustering and its underlying neural basis. Two subgroups represent distinct patterns of externalizing and internalizing symptoms, suggesting that anxiety is not its major predisposing factor. These results may have implications for the nosology and treatment of preteenage OCD.

Corpus callosum integrity loss predicts cognitive impairment in Leukoaraiosis.

ObjectiveTo investigate regional white matter fibers loss in Leukoaraiosis (LA) and its relationship with cognitive impairments.MethodsFifty‐six participants with LA and 38 healthy controls underwent clinical evaluations and MR scans. Participants with LA were classified as cognitively normal (LA‐NC, n = 18), vascular cognitive impairment of none dementia (LA‐VCIND, n = 24), and vascular dementia (LA‐VaD, n = 14) by Mini‐Mental State Examination and Clinical Dementia Rating. Cognitive domains including visual‐spatial, naming, attention, language, abstraction, memory, and orientation were assessed. With the use of Tract‐based spatial statistics, mean fractional anisotropy (FA) of major white matter fiber tracts were compared between LA and controls and among LA groups with varying levels of cognitive impairments. Regression analyses were performed to evaluate relationships between FA values and cognitive performance.ResultsParticipants showed significant FA reduction in the corpus callosum (CC), bilateral corona radiata, anterior limb of the internal capsule, external capsule, posterior thalamic radiation, and superior longitudinal fasciculus compared to controls and across LA groups. The LA‐VaD group showed consistent damage in the body and genu of CC compared to the LA‐NC and LA‐VCIND groups. A positive correlation between visual‐spatial and FA reduction in right anterior corona radiates in LA‐VCIND and body of CC in LA‐ VaD.InterpretationWe found regional fiber loss in the CC across the cognitive spectrum in patients with LA and correlations between FA and visuospatial impairment in the anterior corona radiata in patients with LA‐VCIND and in the body of CC in patients with LA‐VaD.

Deep white matter analysis (DeepWMA): Fast and consistent tractography segmentation.

White matter tract segmentation, i.e. identifying tractography fibers (streamline trajectories) belonging to anatomically meaningful fiber tracts, is an essential step to enable tract quantification and visualization. In this study, we present a deep learning tractography segmentation method (DeepWMA) that allows fast and consistent identification of 54 major deep white matter fiber tracts from the whole brain. We create a large-scale training tractography dataset of 1 million labeled fiber samples, and we propose a novel 2D multi-channel feature descriptor (FiberMap) that encodes spatial coordinates of points along each fiber. We learn a convolutional neural network (CNN) fiber classification model based on FiberMap and obtain a high fiber classification accuracy of 90.99% on the training tractography data with ground truth fiber labels. Then, the method is evaluated on a test dataset of 597 diffusion MRI scans from six independently acquired populations across genders, the lifespan (1 day - 82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). We perform comparisons with two state-of-the-art tract segmentation methods. Experimental results show that our method obtains a highly consistent tract segmentation result, where on average over 99% of the fiber tracts are successfully identified across all subjects under study, most importantly, including neonates and patients with space-occupying brain tumors. We also demonstrate good generalization of the method to tractography data from multiple different fiber tracking methods. The proposed method leverages deep learning techniques and provides a fast and efficient tool for brain white matter segmentation in large diffusion MRI tractography datasets.

Characterizing white matter connectivity in Alzheimer's disease and mild cognitive impairment: An automated fiber quantification analysis with two independent datasets

Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive dementia. Diffusion tensor imaging (DTI) has been widely used to show structural integrity and delineate white matter (WM) degeneration in AD. The automated fiber quantification (AFQ) method is a fully automated approach that can rapidly and reliably identify major WM fiber tracts and evaluate WM properties. The main aim of this study was to assess WM integrity and abnormities in a cohort of patients with amnestic mild cognitive impairment (aMCI) and AD as well as normal controls (NCs). For this purpose, we first used AFQ to identify 20 major WM tracts and assessed WM integrity and abnormalities in a cohort of 120 subjects (39 NCs, 34 aMCI patients and 47 AD patients) in a discovery dataset and 122 subjects (43 NCs, 37 aMCI patients and 42 AD patients) in a replicated dataset. Pointwise differences along WM tracts were identified in the discovery dataset and simultaneously confirmed in the replicated dataset. Next, we investigated the utility of DTI measures along WM tracts as features to distinguish patients with AD from NCs via multilevel cross validation using a support vector machine. Correlation analysis revealed the identified microstructural WM alterations and classification output to be highly associated with cognitive ability in the patient groups, suggesting that they may be a robust biomarker of AD. This systematic study provides a pipeline to examine WM integrity and its potential clinical application in AD and may be useful for studying other neurological and psychiatric disorders.

Multimodal neuroimaging-based prediction of adult outcomes in childhood-onset ADHD using ensemble learning techniques.

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelopmental disorder, which is diagnosed using subjective symptom reports. Machine learning classifiers have been utilized to assist in the development of neuroimaging-based biomarkers for objective diagnosis of ADHD. However, existing basic model-based studies in ADHD report suboptimal classification performances and inconclusive results, mainly due to the limited flexibility for each type of basic classifier to appropriately handle multi-dimensional source features with varying properties. This study applied ensemble learning techniques (ELTs), a meta-algorithm that combine several basic machine learning models into one predictive model in order to decrease variance, bias, or improve predictions, in multimodal neuroimaging data collected from 72 young adults, including 36 probands (18 remitters and 18 persisters of childhood ADHD) and 36 group-matched controls. All currently available optimization strategies for ELTs (i.e., voting, bagging, boosting and stacking techniques) were tested in a pool of semifinal classification results generated by seven basic classifiers. The high-dimensional neuroimaging features for classification included regional cortical gray matter (GM) thickness and surface area, GM volume of subcortical structures, volume and fractional anisotropy of major white matter fiber tracts, pair-wise regional connectivity and global/nodal topological properties of the functional brain network for cue-evoked attention process. As a result, the bagging-based ELT with the base model of support vector machine achieved the best results, with significant improvement of the area under the receiver of operating characteristic curve (0.89 for ADHD vs. controls and 0.9 for ADHD persisters vs. remitters). Features of nodal efficiency in right inferior frontal gyrus, right middle frontal (MFG)-inferior parietal (IPL) functional connectivity, and right amygdala volume significantly contributed to accurate discrimination between ADHD probands and controls; higher nodal efficiency of right MFG greatly contributed to inattentive and hyperactive/impulsive symptom remission, while higher right MFG-IPL functional connectivity strongly linked to symptom persistence in adults with childhood ADHD. Considering their improved robustness than the commonly implemented basic classifiers, findings suggest that ELTs may have the potential to identify more reliable neurobiological markers for neurodevelopmental disorders.

Characterizing White Matter Connectivity in Alzheimer's Disease and Mild Cognitive Impairment: Automated Fiber Quantification Analysis with Two Independent Datasets

Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive dementia. Diffusion tensor imaging (DTI) has been widely used to show structural integrity and delineate white matter (WM) degeneration in AD. The automated fiber quantification (AFQ) method is a fully automated method that can rapidly and reliably identify major WM fiber tracts and evaluate white matter properties. The main aim of this study was to assess WM integrity and abnormities in a cohort of patients with amnestic mild cognitive impairment (aMCI) and AD as well as normal controls (NCs). For this purpose, we first used AFQ to identify 20 major WM tracts and assessed WM integrity and abnormities in a cohort of patients with 120 participants (39 NCs, 34 aMCI and 47 AD) in the discovery dataset, as well as 122 participants (43 NCs, 37aMCI and 42 AD) in the replicated dataset. Pointwise differences along the WM tracts were identified in the discovery dataset and simultaneously confirmed by the replicated dataset. Next, we investigated the utility of DTI measures along WM tracts as features to classify patients with AD from NCs with multilevel cross validations using a support vector machine. The correlation analysis revealed that the identified impaired microstructural WM alterations and classification output were highly associated with cognitive ability in the patient groups, indicating that this credible, robust potential early biomarker may be useful for clinical application in AD. This systematic study provides a pipeline to examine WM integrity and its potential clinical application in AD and may be useful in studying other neurological and psychiatric disorders. Funding: This work was partially supported by the National Key Research and Development Program of China (No. 2016YFC1305904), the National Natural Science Foundation of China (Grant Nos. 81871438, 81571062, 81471120), and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDB32020200). Declaration of Interest: The authors declare no conflicts of interest. Ethical Approval: This study was approved by the Medical Ethics Committee of the Chinese PLA General Hospital.

Aerobic Exercise and Cerebral White Matter Integrity in MCI Patients

Cerebral white matter (WM) represents the structural substrate of neuronal communications and is damaged in dementia patients. Aerobic exercise training (AET) may improve cerebral WM integrity in healthy older adults, but its effect in populations at risk for dementia remains unclear. PURPOSE: To determine the effect of AET on cerebral WM integrity in patients with amnesic mild cognitive impairment (MCI) METHODS: We conducted a 1-year, single-blinded, parallel randomized controlled trial of AET and stretching intervention programs in patients with MCI. At baseline and post intervention, diffusion tensor images (DTI) were acquired to estimate fractional anisotropy and mean diffusivity (MD) that are analyzed by tract-based spatial statistics (TBSS) and compared among the major WM fiber tracts. High-resolution T1-weighted images were also acquired to measure the volumes of cerebral WM and WM hypointensities. Maximal oxygen consumption (VO2max) was measured at pre and post intervention. RESULTS: Thirty-six MCI patients completed AET (n=16) or stretching (n=20) program with the baseline and post-intervention MRI scans. After intervention, participants in AET program improved VO2max while those in stretching group showed slight declines (time × treatment: P=0.008). The volumes of WM and WM hypointensities did not show treatment effects over time (time × treatment: P>0.05). However, TBSS analysis demonstrated that improvements of VO2max with AET are correlated with the reductions of MD among the major WM fiber tracts (Figure). CONCLUSIONS: In patients with amnesic MCI, AET did not improve cerebral WM volume and integrity between the intervention and control groups. However, individual improvements of VO2max were associated with the reductions of MD. These findings suggest that benefits of AET on cerebral WM integrity depend on the magnitude of cardiorespiratory fitness gains. This study was supported by the NIH (R01AG033106 and K99HL133449).Figure

Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification.

Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for diagnosing various neurodegenerative diseases, including Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Current studies mainly construct the FC networks between grey matter (GM) regions of the brain based on temporal co-variations of the blood oxygenation level-dependent (BOLD) signals, which reflects the synchronized neural activities. However, it was rarely investigated whether the FC detected within the white matter (WM) could provide useful information for diagnosis. Motivated by the recently proposed functional correlation tensors (FCT) computed from RS-fMRI and used to characterize the structured pattern of local FC in the WM, we propose in this article a novel MCI classification method based on the information conveyed by both the FC between the GM regions and that within the WM regions. Specifically, in the WM, the tensor-based metrics (e.g., fractional anisotropy [FA], similar to the metric calculated based on diffusion tensor imaging [DTI]) are first calculated based on the FCT and then summarized along each of the major WM fiber tracts connecting each pair of the brain GM regions. This could capture the functional information in the WM, in a similar network structure as the FC network constructed for the GM, based only on the same RS-fMRI data. Moreover, a sliding window approach is further used to partition the voxel-wise BOLD signal into multiple short overlapping segments. Then, both the FC and FCT between each pair of the brain regions can be calculated based on the BOLD signal segments in the GM and WM, respectively. In such a way, our method can generate dynamic FC and dynamic FCT to better capture functional information in both GM and WM and further integrate them together by using our developed feature extraction, selection, and ensemble learning algorithms. The experimental results verify that the dynamic FCT can provide valuable functional information in the WM; by combining it with the dynamic FC in the GM, the diagnosis accuracy for MCI subjects can be significantly improved even using RS-fMRI data alone. Hum Brain Mapp 38:5019-5034, 2017. © 2017 Wiley Periodicals, Inc.

Diffusion-weighted tractography in the common marmoset monkey at 9.4T.

The common marmoset (Callithrix jacchus) is a small New World primate that is becoming increasingly popular in the neurosciences as an animal model of preclinical human disease. With several major disorders characterized by alterations in neural white matter (e.g., multiple sclerosis, Alzheimer's disease, schizophrenia), proposed to be transgenically modeled using marmosets, the ability to isolate and characterize reliably major white matter fiber tracts with MRI will be of use for evaluating structural brain changes related to disease processes and symptomatology. Here, we propose protocols for isolating major white matter fiber tracts in the common marmoset using in vivo ultrahigh-field MRI (9.4T) diffusion-weighted imaging (DWI) data. With the use of a high angular-resolution DWI (256 diffusion-encoding directions) sequence, collected on four anesthetized marmosets, we provide guidelines for manually drawing fiber-tracking regions of interest, based on easily identified anatomical landmarks in DWI native space. These fiber-tract isolation protocols are expected to be experimentally useful for visualization and quantification of individual white matter fiber tracts in both control and experimental groups of marmosets (e.g., transgenic models). As disease models in the marmoset advance, the determination of how macroscopic white matter anatomy is altered as a function of disease state will be relevant in bridging the existing translational gap between preclinical rodent models and human patients.NEW & NOTEWORTHY Although significant progress has been made in mapping white matter connections in the marmoset brain using ex vivo tracing techniques, the application of in vivo virtual dissection of major white matter fiber tracts has been established by few studies in the marmoset literature. Here, we demonstrate the feasibility of whole-brain diffusion-weighted tractography in anesthetized marmosets at ultrahigh-field MRI (9.4T) and propose protocols for isolating nine major white matter fiber tracts in the marmoset brain.

Microstructural changes of brain in patients with aromatic L-amino acid decarboxylase deficiency.

Aromatic L-amino acid decarboxylase (AADC) deficiency is an uncommon inherited neurometabolic disease. The clinical presentations and MR findings in children with AADC deficiency were investigated. Total 12 children (6 boys, 6 girls), aged from 9 to 50 months (mean, 23 ±13 months), with AADC deficiency, were enrolled for analysis. Of 12 patients enrolled, clinical presentations included global developmental delay with generalized hypotonia in 12 (100%), dystonia in 12 (100%), oculogyric crisis in 12 (100%), and excessive sweating in 8 (67%). Sleep problem was also found in 4 (33%). Of 15 MR examinations, the major changes included 6 (40%) with diffusely prominent bilateral frontal sulci, 10 (67%) with prominent frontal horns, and 12 (80%) with hypomyelination. In AADC patients, the frontal horn was significantly widened (P < 0.01), and the volume of caudate nucleus was also significantly smaller than that of controls (P = 0.02). The ratios of thickness of the splenium to that of the genu of corpus callosum were also significantly increased (P < 0.01). There was also significant decrease of fiber density indices in major white matter fiber tracts. Using Tract-Based Spatial Statistics approach, we also revealed significant change in major fiber tracts related to language function and motor function. In conclusion, the present study indicated that AADC deficiency may have significant impact on brain development, especially the frontal lobe and fiber tracts related to language function and motor function. Long-term follow-up of brain MRI in patients with AADC deficiency may clarify the possible effect of AADC deficiency on brain development. Hum Brain Mapp 38:1532-1540, 2017. © 2016 Wiley Periodicals, Inc.

Axon density and axon orientation dispersion in children born preterm.

Very preterm birth (VPT, <32 weeks' gestation) is associated with altered white matter fractional anisotropy (FA), the biological basis of which is uncertain but may relate to changes in axon density and/or dispersion, which can be measured using Neurite Orientation Dispersion and Density Imaging (NODDI). This study aimed to compare whole brain white matter FA, axon dispersion, and axon density between VPT children and controls (born ≥37 weeks' gestation), and to investigate associations with perinatal factors and neurodevelopmental outcomes. FA, neurite dispersion, and neurite density were estimated from multishell diffusion magnetic resonance images for 145 VPT and 33 control 7-year-olds. Diffusion values were compared between groups and correlated with perinatal factors (gestational age, birthweight, and neonatal brain abnormalities) and neurodevelopmental outcomes (IQ, motor, academic, and behavioral outcomes) using Tract-Based Spatial Statistics. Compared with controls, VPT children had lower FA and higher axon dispersion within many major white matter fiber tracts. Neonatal brain abnormalities predicted lower FA and higher axon dispersion in many major tracts in VPT children. Lower FA, higher axon dispersion, and lower axon density in various tracts correlated with poorer neurodevelopmental outcomes in VPT children. FA and NODDI measures distinguished VPT children from controls and were associated with neonatal brain abnormalities and neurodevelopmental outcomes. This study provides a more detailed and biologically meaningful interpretation of white matter microstructure changes associated with prematurity. Hum Brain Mapp 37:3080-3102, 2016. © 2016 Wiley Periodicals, Inc.

The white matter query language: a novel approach for describing human white matter anatomy.

We have developed a novel method to describe human white matter anatomy using an approach that is both intuitive and simple to use, and which automatically extracts white matter tracts from diffusion MRI volumes. Further, our method simplifies the quantification and statistical analysis of white matter tracts on large diffusion MRI databases. This work reflects the careful syntactical definition of major white matter fiber tracts in the human brain based on a neuroanatomist's expert knowledge. The framework is based on a novel query language with a near-to-English textual syntax. This query language makes it possible to construct a dictionary of anatomical definitions that describe white matter tracts. The definitions include adjacent gray and white matter regions, and rules for spatial relations. This novel method makes it possible to automatically label white matter anatomy across subjects. After describing this method, we provide an example of its implementation where we encode anatomical knowledge in human white matter for ten association and 15 projection tracts per hemisphere, along with seven commissural tracts. Importantly, this novel method is comparable in accuracy to manual labeling. Finally, we present results applying this method to create a white matter atlas from 77 healthy subjects, and we use this atlas in a small proof-of-concept study to detect changes in association tracts that characterize schizophrenia.

White matter structural integrity differs between people with schizophrenia and healthy groups as a function of cognitive control

A behavioral hallmark of schizophrenia is poor cognitive control. Recent evidence suggests that problems with cognitive control in schizophrenia are related to disconnectivity along major white matter fibers. Although deficits of cognitive control are common in schizophrenia, a proportion of otherwise healthy subjects show poor cognitive control performance. The present study sought to address this potential confound by comparing white matter integrity between a group with schizophrenia and otherwise healthy individuals with either high or low levels of cognitive control (based on working memory span performance). Diffusion tensor imaging was used to evaluate white matter integrity in 24 participants with schizophrenia, 24 healthy participants with high cognitive control (HCC), and 25 healthy participants with low cognitive control (LCC). To test for differences in fractional anisotropy (FA) across major white matter fiber tracts, a voxelwise region of interest analysis was conducted in standardized brain space. In a separate analysis, regions of interest were manually drawn in native brain space to isolate superior longitudinal fasciculus (SLF), a tract implicated in cognitive control performance. The voxelwise analysis demonstrated widespread lower FA in the schizophrenia group compared to the HCC group. With a high degree of concordance, the manual ROI analysis revealed lower FA in the schizophrenia group compared to the HCC group. Taken together, these results provide evidence to suggest that structural differences identified between healthy groups and schizophrenia may not be entirely specific to the disease process and can vary as a function of cognitive control capacity in the comparison group.

Use of electric field orientation as an index for estimating the contribution of model complexity in transcranial direct current stimulation forward head model development

The study evaluates the role of human skull composition and brain anisotropy in the context of transcranial direct current stimulation (tDCS) based predictive modeling. Four head models were developed and each proposed attribute (cancellous bone and brain anisotropy) was compared with the isotropic model. By employing a single high-definition montage, the efficacy of each attribute in shaping induced electric field was analyzed by its magnitude and orientation information. Relative error (RE) was used to estimate the variation in field magnitude. It was observed that for a given high-definition montage, the brain anisotropy contributed to 5% change (RE) in the strength of the gray matter (GM) electric field and 10% for the white matter (WM). Inclusion of diploe in the model resulted in 45% variation in the magnitude of the brain electric field. On average, brain anisotropy contributed to field deviations of up to 20 degrees in major WM fiber tracts. Skull heterogeneity caused field deviations of up to 35 degrees in diploe, 15 degrees in subcutaneous fat and marginal variations in brain regions. These simulation results demonstrated the importance of considering refinement in forward models of tDCS, especially; the role of diploe should be considered for more accurate field assessments.

Moderate and late preterm infants exhibit widespread brain white matter microstructure alterations at term-equivalent age relative to term-born controls.

Despite the many studies documenting cerebral white matter microstructural alterations associated with very preterm birth (<32 weeks' gestation), there is a dearth of similar research in moderate and late preterm infants (born 32-36 weeks' gestation), who experience higher rates of neurodevelopmental delays than infants born at term (≥ 37 weeks' gestation). We therefore aimed to determine whether whole brain white matter microstructure differs between moderate and late preterm infants and term-born controls at term-equivalent age, as well as to identify potential perinatal risk factors for white matter microstructural alterations in moderate and late preterm infants. Whole brain white matter microstructure was studied in 193 moderate and late preterm infants and 83 controls at term-equivalent age by performing Tract-Based Spatial Statistics analysis of diffusion tensor imaging data. Moderate and late preterm infants had lower fractional anisotropy and higher mean, axial and radial diffusivities compared with controls in nearly 70% of the brain's major white matter fiber tracts. In the moderate and late preterm group, being born small for gestational age and male sex were associated with lower fractional anisotropy, largely within the optic radiation, corpus callosum and corona radiata. In conclusion, moderate and late preterm infants exhibit widespread brain white matter microstructural alterations compared with controls at term-equivalent age, in patterns consistent with delayed or disrupted white matter microstructural development. These findings may underpin some of the neurodevelopmental delays observed in moderate and late preterm children.

Characteristics of diffusion-tensor imaging for healthy adult rhesus monkey brains.

Diffusion-tensor imaging can be used to observe the microstructure of brain tissue. Fractional sotropy reflects the integrity of white matter fibers. Fractional anisotropy of a young adult brain is low in gray matter, high in white matter, and highest in the splenium of the corpus callosum. Thus, we selected the anterior and posterior limbs of the internal capsule, head of the caudate nucleus, semioval center, thalamus, and corpus callosum (splenium and genu) as regions of interest when using diffusion-tensor imaging to observe fractional anisotropy of major white matter fiber tracts and the deep gray matter of healthy rhesus monkeys aged 4–8 years. Results showed no laterality ferences in fractional anisotropy values. Fractional anisotropy values were low in the head of date nucleus and thalamus in gray matter. Fractional anisotropy values were highest in the splenium of corpus callosum in the white matter, followed by genu of the corpus callosum and the posterior limb of the internal capsule. Fractional anisotropy values were lowest in the semioval center and posterior limb of internal capsule. These results suggest that fractional anisotropy values in major white matter fibers and the deep gray matter of 4–8-year-old rhesus monkeys are similar to those of healthy young people.

Incorrect ICBM-DTI-81 atlas orientation and white matter labels

The ICBM-DTI-81 atlas (Mori et al., 2008; Oishi et al., 2008)1 provides perhaps the most widely used reference coordinate system for group analyses of diffusion tensor MR images. The atlas comprises, among other data, structural images (e.g., T2-weighted), scalar maps of diffusion (e.g., fractional anisotropy), and a map of manually labeled major white matter fiber tracts. Unfortunately, in our work using this atlas, we have encountered several substantial problems in terms of (a) correctness of spatial orientation and (b) correctness of white matter tract labels. These issues raise questions about the validity of published studies using the ICBM-DTI-81 atlas. We also detected substantial changes applied to the atlas files with no documentation or user notification. Because the changed files are distributed under the same name and the earlier versions are no longer available, it is virtually impossible to reproduce published research using the atlas. We have contacted the atlas maintainers with our concerns and have been in active communication with them since to discuss possible fixes. As correcting the atlas turned out to take considerable time, however, and because it is not feasible to notify all users of the atlas directly, we feel that communicating our findings to the neuroimaging community at large is warranted. The atlas maintainers were apprised of our intent to write and publish this article and did not raise any objections. Thus, researchers using the ICBM-DTI-81 atlas can verify the validity of their analyses. To this end, we provide a discussion of the significance and potential impact of each problem identified, as well as detailed recommendations for confirming whether or not a particular study was affected.

In vivo diffusion tensor magnetic resonance imaging and fiber tracking of the mouse brain

Until very recently, the study of neural architecture using fixed tissue has been a major scientific focus of neurologists and neuroanatomists. A non-invasive detailed insight into the brain's axonal connectivity in vivo has only become possible since the development of diffusion tensor magnetic resonance imaging (DT-MRI). This unique approach of analyzing axonal projections in the living brain was used in the present study to describe major white matter fiber tracts of the mouse brain and also to identify for the first time non-invasively the rich connectivity between the amygdala and different target regions. To overcome the difficulties associated with high spatially and temporally resolved DT-MRI measurements a 4-shot diffusion weighted spin echo (SE) echo planar imaging (EPI) protocol was adapted to mouse brain imaging at 9.4T. Diffusion tensor was calculated from data sets acquired by using 30 diffusion gradient directions while keeping the acquisition time at 91 min. Two fiber tracking algorithms were employed. A deterministic approach (fiber assignment by continuous tracking - FACT algorithm) allowed us to identify and generate the 3D representations of various neural pathways. A probabilistic approach was further used for the generation of probability maps of connectivity with which it was possible to investigate - in a statistical sense - all possible connecting pathways between selected seed points. We show here applications to determine the connection probability between regions belonging to the visual or limbic systems. This method does not require a priori knowledge about the projections' trajectories and is shown to be efficient even if the investigated pathway is long or three-dimensionally complex. Additionally, high resolution images of rotational invariant parameters of the diffusion tensor, such as fractional anisotropy, volume ratio or main eigenvalues allowed quantitative comparisons in-between regions of interest (ROIs) and showed significant differences between various white matter regions.

Potential of diffusion tensor MRI in the assessment of periventricular leukomalacia

Aim: This study aimed to investigate magnetic resonance (MR) diffusion tensor imaging (DTI) and fiber tractography in the assessment of altered major white matter (WM) fiber tracts in periventricular leukomalacia (PVL).