White Matter Structures Research Articles

Element Changes Occurring in Brain Point at the White Matter Abnormalities in Rats Exposed to the Ketogenic Diet During Prenatal Life.

A large number of clinical studies demonstrate that the ketogenic diet (KD) may be an effective approach to the reduction of epileptic seizures in children and adults. Such dietary therapy could also help pregnant women with epilepsy, especially since most antiseizure drugs have teratogenic action. However, there is a lack of medical data, considering the safety of using KD during gestation for the progeny. Therefore, we examined the influence of KD used prenatally in rats on the elemental composition of the selected brain regions in their offspring. For this purpose, synchrotron radiation-induced X-ray fluorescence (SR-XRF) microscopy was utilized, and elements such as P, S, K, Ca, Fe, and Zn were determined. Moreover, to verify whether the possible effects of KD are temporary or long-term, different stages of animal postnatal development were taken into account in our experiment. The obtained results confirmed the great applicability of SR-XRF microscopy to track the element changes occurring in the brain during postnatal development as well as those induced by prenatal exposure to the high-fat diet. The topographic analysis of the brains taken from offspring of mothers fed with KD during pregnancy and appropriate control individuals showed a potential influence of such dietary treatment on the brain levels of elements such as P and S. In the oldest progeny, a significant reduction of the surface of brain areas characterized by an increased P and S content, which histologically/morphologically correspond to white matter structures, was noticed. In turn, quantitative elemental analysis showed significantly decreased levels of Fe in the striatum and white matter of 30-day-old rats exposed prenatally to KD. This effect was temporary and was not noticed in adult animals. The observed abnormalities may be related to the changes in the accumulation of sphingomyelin and sulfatides and may testify about disturbances in the structure and integrity of the myelin, present in the white matter.

Neuroimaging Findings for the Overnight Consolidation of Learned Non-native Speech Sounds

Abstract Research over the past two decades has documented the importance of sleep to language learning. Sleep has been suggested to play a role in establishing new speech representations as well; however, the neural mechanisms corresponding to sleep-mediated effects on speech perception behavior are unknown. In this study, we trained monolingual English-speaking adults to perceive differences between the Hindi dental vs. retroflex speech contrast in the evening. We examined the blood oxygen level dependent signal using functional magnetic resonance imaging during perceptual tasks on both the trained talker and on an untrained talker shortly after training, and again the next morning. We also employed diffusion tensor imaging to determine if individual differences in white matter structure could predict variability in overnight consolidation. We found greater activity in cortical regions associated with language processing (e.g., left insula) on the second day. Fractional anisotropy values in the anterior thalamic radiation and the uncinate fasciculus was associated with the magnitude of overnight change in perceptual behavior on the generalization (untrained) talker, after controlling for differences in sleep duration and initial learning. Our findings suggest that speech-perceptual information is subject to an overnight transfer of information to the cortex. Moreover, neural structure appears to be linked to individual differences in efficiency of overnight consolidation.

Mass spectrometry imaging of two neocortical areas reveals the histological selectivity of schizophrenia-associated lipid alterations

BACKGROUND: Schizophrenia is a psychiatric disorder known to affect brain structure and functionality. Structural changes in the brain at the level of gross anatomical structures have been fairly well studied, while microstructural changes, especially those associated with changes in the molecular composition of the brain, are still being investigated. Of special interest are lipids and metabolites, for which some previous studies have shown association with schizophrenia. AIM: To utilize a spatially resolved analysis of the brain lipidome composition to investigate the degree and nature of schizophrenia-associated lipidome alterations in the gray and white matter structures of two neocortical regions — the dorsolateral prefrontal cortex (Brodmann area 9, BA9) and the posterior part of the superior temporal gyrus (Brodmann area 22, posterior part, BA22p), as well compare the distribution of the changes between the two regions and tissue types. METHODS: We employed Matrix-Assisted Laser Desorption/Ionization Mass Spectrometric Imaging (MALDI-MSI), supplemented by a statistical analysis, to examine the lipid composition of brain sections. A total of 24 neocortical sections from schizophrenia patients (n=2) and a healthy control group (n=2), representing the two aforementioned neocortical areas, were studied, yielding data for 131 lipid compounds measured across more than a million MALDI-MSI pixels. RESULTS: Our findings revealed an uneven distribution of schizophrenia-related lipid alterations across the two neocortical regions. The BA22p showed double the differences in its subcortical white matter structures compared to BA9, while less bias was detected in the gray matter layers. While the schizophrenia-associated lipid differences generally showed good agreement between brain regions at the lipid class level for both gray and white matter, there were consistently more discrepancies for white matter structures. CONCLUSION: Our study found a consistent yet differential association of schizophrenia with the brain lipidome composition of distinct neocortical areas, particularly subcortical white matter. These findings highlight the need for broader brain coverage in future schizophrenia research and underscore the potential of spatially resolved molecular analysis methods in identifying structure-specific effects.

A lifespan perspective on fetal alcohol syndrome: Developmental challenges and outcomes

Fetal alcohol syndrome (FAS) and fetal alcohol spectrum disorder (FASD) result from alcohol exposure during the intrauterine life of the fetus and are the most common nonheritable, avoidable causes of intellectual disability and a wide range of neurobehavioral outcomes. Alcohol causes neurotoxicity in the developing brain. Children with FASD experience behavioral issues from an early age and progress to adulthood. The prevalence of alcohol consumption among women is increasing. Clinically, symptoms in children with FAS manifest in a wide range of ways, probably as a result of the quantity and timing of alcohol exposure, as well as maternal and genetic effects. All of these elements contribute to the mechanisms through which alcohol harms a growing brain, the specifics of which are still largely unknown. Neuroimaging reveals alterations in the structure of the brain, white matter structure, cortical development, and functional connections. However, recent advancements and ongoing research have yielded encouraging findings that could result in the use of FAS screening tools and therapy for individuals. Intervention aims to enhance development, address behavioural challenges, and ensure suitable educational programming. Prioritizing early childhood intervention is crucial to prevent secondary disabilities that may arise from delays in obtaining a definitive diagnosis of Fetal Alcohol Syndrome. Effective nutritional therapies as well as cognitive rehabilitation are now being tested. This disorder represents the intersection of complicated societal, community, family, and biological circumstances that increase the risk for intergenerational suffering, health disparity, and social injustice. This paper aims to guide healthcare professionals and policymaker in enhancing the quality of life for individuals with FAS through effective strategies.

Examining relationships among NODDI indices of white matter structure in prefrontal cortical-thalamic-striatal circuitry and OCD symptomatology

Obsessive-compulsive disorder is a psychiatric disorder characterized by intrusive thoughts and repetitive behaviors. There are two prominent features: Harm Avoidance (HA) and Incompleteness (INC). Previous resting-state studies reported abnormally elevated connectivity between prefrontal cortical (PFC) and subcortical regions (thalamus, striatum) in OCD participants. Yet, little is known about the white matter (WM) structural abnormalities in these connections. Using brain parcellation and segmentation, whole brain tractography, and Neurite Orientation Dispersion and Density Imaging (NODDI), we aimed to characterize WM structural abnormalities in OCD vs. healthy controls and determine the extent to which NODDI indices of these connections were associated with subthreshold-threshold HA, INC and overall OCD symptom severity across all participants. Four PFC regions were segmented: ventral medial (vmPFC), ventrolateral (vlPFC), dorsomedial (dmPFC), and dorsolateral (dlPFC). NODDI Neurite Density (NDI) and Orientation Dispersion (ODI) indices of WM structure were extracted from connections between these PFC regions and the thalamus (42 OCD, 44 healthy controls, mean age[SD] = 23.65[4.25]y, 63.9% female) and striatum (38 OCD, 41 healthy controls, mean age[SD] = 23.59[4.27]y, 64.5% female). Multivariate analyses of covariance revealed no between-group differences in these indices. Multivariate regression models revealed that greater NDI in vmPFC-thalamus, greater NDI and ODI in vmPFC-striatum, and greater NDI in dmPFC-thalamus connections were associated with greater INC severity (Q ≤ 0.032). These findings highlight the utility of NODDI in the examination of WM structure in OCD, provide valuable insights into specific WM alterations underlying dimensional INC, and can facilitate the development of customized treatments for OCD individuals with treatment-resistant symptoms.

White matter microstructure damage measured by automated fiber quantification correlates with pain symptoms in lung cancer patients.

To investigative the white matter (WM) alterations in lung cancer patients with cancer pain (CP+), and explore the correlations between damaged WM fiber tracts and clinical indicators. Twenty-six CP+, 26 lung cancer patients without CP (CP-), and 31 healthy controls (HC) were recruited. All participants underwent diffusion tensor imaging (DTI) and clinical assessments. Automated fiber quantification (AFQ) technique was performed to identify the 20 WM fiber bundles, and the fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were extracted. Intergroup comparisons of these diffusion metrics were conducted based on the entire fiber bundle level and 100 node levels along each tract. The associations between altered diffusion metrics and the numeric rating scale (NRS) scores, as well as the pain duration, were analyzed. At the entire level, the CP + group showed impaired WM structure in the right cingulum hippocampus (CH_R). At the pointwise level, the CP + group exhibited extensive nodal FA reduction or MD, RD, and AD elevation. In addition, the AD of the posterior portion of the right inferior longitudinal fasciculus (ILF_R, nodes 71-75) in the CP + group was positively correlated with the pain duration, and the FA of CH_R (nodes 22-38) was negatively correlated with NRS score. Extensive WM microstructural damage may be a pattern of brain abnormalities in lung cancer patients with CP, and in particular, specific nodal disruption along pain-related fiber tracts may be a sensitive imaging biomarker to characterize the severity and duration of CP.

A reassessment of spinal cord pathology in severe infantile spinal muscular atrophy: Reassessment of spinal cord pathology.

Spinal muscular atrophy (SMA) is a life-limiting paediatric motor neuron disease characterised by lower motor neuron loss, skeletal muscle atrophy and respiratory failure, if untreated. Revolutionary treatments now extend patient survival. However, a limited understanding of the foundational neuropathology challenges the evaluation of therapeutic success. As opportunities to study treatment-naïve tissue decrease, we have characterised spinal cord pathology in severe infantile SMA using gold-standard techniques, providing a baseline to measure treatment success and therapeutic limitations. Detailed histological analysis, stereology and transmission electron microscopy were applied to post-mortem spinal cord from severe infantile SMA patients to estimate neuron number at the end of life; characterise the morphology of ventral horn, lateral horn and Clarke's column neuron populations; assess cross-sectional spinal cord area; and observe myelinated white matter tracts in the clinically relevant thoracic spinal cord. Ventral horn neuron loss was substantial in all patients, even the youngest cases. The remaining ventral horn neurons were small with abnormal, occasionally chromatolytic morphology, indicating cellular damage. In addition to ventral horn pathology, Clarke's column sensory-associated neurons displayed morphological features of cellular injury, in contrast to the preserved sympathetic lateral horn neurons. Cellular changes were associated with aberrant development of grey and white matter structures that affected the overall dimensions of the spinal cord. We provide robust quantification of the neuronal deficit found at the end of life in SMA spinal cord. We question long-accepted dogmas of SMA pathogenesis and shed new light on SMA neuropathology out with the ventral horn, which must be considered in future therapeutic design.

Modeling electrical impedance in brain tissue with diffusion tensor imaging for functional neurosurgery applications

Objective.Decades ago, neurosurgeons used electrical impedance measurements in the brain for coarse intraoperative tissue differentiation. Over time, these techniques were largely replaced by more refined imaging and electrophysiological localization. Today, advanced methods of diffusion tensor imaging (DTI) and finite element method (FEM) modeling may permit non-invasive, high-resolution intracerebral impedance prediction. However, expectations for tissue-impedance relationships and experimentally verified parameters for impedance modeling in human brains are lacking. This study seeks to address this need.Approach.We used FEM to simulate high-resolution single- and dual-electrode impedance measurements along linear electrode trajectories through (1) canonical gray and white matter tissue models, and (2) selected anatomic structures within whole-brain patient DTI-based models. We then compared intraoperative impedance measurements taken at known locations along deep brain stimulation (DBS) surgical trajectories with model predictions to evaluate model accuracy and refine model parameters.Main results.In DTI-FEM models, single- and dual-electrode configurations performed similarly. While only dual-electrode configurations were sensitive to white matter fiber orientation, other influences on impedance, such as white matter density, enabled single-electrode impedance measurements to display significant spatial variation even within purely white matter structures. We compared 308 intraoperative single-electrode impedance measurements in five DBS patients to DTI-FEM predictions at one-to-one corresponding locations. After calibration of model coefficients to these data, predicted impedances reliably estimated intraoperative measurements in all patients (R=0.784±0.116,n=5). Through this study, we derived an updated value for the slope coefficient of the DTI conductance model published by Tuchet al,k=0.0649 S⋅smm-3 (originalk=0.844), for use specifically in humans at physiological frequencies.Significance.This is the first study to compare impedance estimates from imaging-based models of human brain tissue to experimental measurements at the same locationsin vivo. Accurate, non-invasive, imaging-based impedance prediction has numerous applications in functional neurosurgery, including tissue mapping, intraoperative electrode localization, and DBS.

Unveiling the Exquisite Microstructural Details in Zebrafish Brain Non-Invasively Using Magnetic Resonance Imaging at 28.2 T

Zebrafish (Danio rerio) is an important animal model for a wide range of neurodegenerative diseases. However, obtaining the cellular resolution that is essential for studying the zebrafish brain remains challenging as it requires high spatial resolution and signal-to-noise ratios (SNR). In the current study, we present the first MRI results of the zebrafish brain at the state-of-the-art magnetic field strength of 28.2 T. The performance of MRI at 28.2 T was compared to 17.6 T. A 20% improvement in SNR was observed at 28.2 T as compared to 17.6 T. Excellent contrast, resolution, and SNR allowed the identification of several brain structures. The normative T1 and T2 relaxation values were established over different zebrafish brain structures at 28.2 T. To zoom into the white matter structures, we applied diffusion tensor imaging (DTI) and obtained axial, radial, and mean diffusivity, as well as fractional anisotropy, at a very high spatial resolution. Visualisation of white matter structures was achieved by short-track track-density imaging by applying the constrained spherical deconvolution method (stTDI CSD). For the first time, an algorithm for stTDI with multi-shell multi-tissue (msmt) CSD was tested on zebrafish brain data. A significant reduction in false-positive tracks from grey matter signals was observed compared to stTDI with single-shell single-tissue (ssst) CSD. This allowed the non-invasive identification of white matter structures at high resolution and contrast. Our results show that ultra-high field DTI and tractography provide reproducible and quantitative maps of fibre organisation from tiny zebrafish brains, which can be implemented in the future for a mechanistic understanding of disease-related microstructural changes in zebrafish models of various brain diseases.

Effect of white matter uncertainty visualization in neurosurgical decision making.

Fiber tracking is a powerful technique that provides insight into the brain's white matter structure. Despite its potential, the inherent uncertainties limit its widespread clinical use. These uncertainties potentially hamper the clinical decisions neurosurgeons have to make before, during, and after the surgery. Many techniques have been developed to visualize uncertainties, however, there is limited evidence to suggest whether these uncertainty visualization influences neurosurgical decision-making. In this paper, we evaluate the hypothesis that uncertainty visualization in fiber tracking influences neurosurgeon's decisions and the confidence in their decisions. For this purpose, we designed a user study through an online interactive questionnaire and evaluate the influence of uncertainty visualization in neurosurgical decision-making. The results of this study emphasize the importance of uncertainty visualization in clinical decision making by highlighting the influence of different interval of uncertainty visualization in critical clinical decisions.

Distributed associations among white matter hyperintensities and structural brain networks with fluid cognition in healthy aging

AbstractWhite matter hyperintensities (WMHs) are associated with age-related cognitive impairment and increased risk of Alzheimer’s disease. However, the manner by which WMHs contribute to cognitive impairment is unclear. Using a combination of predictive modeling and network neuroscience, we investigated the relationship between structural white matter connectivity and age, fluid cognition, and WMHs in 68 healthy adults (18–78 years). Consistent with previous work, WMHs were increased in older adults and exhibited a strong negative association with fluid cognition. Extending previous work, using predictive modeling, we demonstrated that age, WMHs, and fluid cognition were jointly associated with widespread alterations in structural connectivity. Subcortical-cortical connections between the thalamus/basal ganglia and frontal and parietal regions of the default mode and frontoparietal networks were most prominent. At the network level, both age and WMHs were negatively associated with network density and communicability, and positively associated with modularity. Spatially, WMHs were most prominent in arterial zones served by the middle cerebral artery and associated lenticulostriate branches that supply subcortical regions. Finally, WMHs overlapped with all major white matter tracts, most prominently in tracts that facilitate subcortical-cortical communication and are implicated in fluid cognition, including the anterior thalamic-radiations and forceps minor. Finally, results of mediation analyses suggest that whole-brain WMH load influences age-related decline in fluid cognition. Thus, across multiple levels of analysis, we showed that WMHs were increased in older adults and associated with altered structural white matter connectivity and network topology involving subcortical-cortical pathways critical for fluid cognition.

Phonological, orthographic and morphological skills are related to structural properties of ventral and motor white matter pathways in skilled and impaired readers

Using diffusion tensor imaging (DTI), we assessed the extent to which fractional anisotropy values in the dorsal (i.e., arcuate fasciculus; AF) versus ventral (i.e., inferior fronto-occipital fasciculus; IFOF) distinction of structural white matter pathways associated with selected reading processes, could be replicated in skilled adult readers (N = 17) and extended to adults with reading impairments (N = 13). In addition to the AF and IFOF, motor-based tracts (i.e., posterior limb of the internal capsule (PLIC) and the frontal aslant tract (FAT)) were isolated to explore their role in reading performance. Several interesting relationships with reading performance emerged. First, orthographic awareness was related to the left IFOF in skilled readers, whereas orthographic awareness was related to left PLIC for impaired readers. Morphological awareness was related to left FAT for skilled readers, whereas morphological awareness was related to right AF, right IFOF and left PLIC for impaired readers. Overall, these findings support the notion that adult reading performance (both skilled and impaired) is related to the structural properties of the ventral white matter pathways. More consideration should be paid to motor pathways, particularly the PLIC, and their role in compensatory reading strategies in individuals with reading impairments.

Quantitative metrics commonly derived from diffusion tractography covary with streamline length: a characterization and method of adjustment.

Tractography algorithms are used extensively to delineate white matter structures, by operating on the voxel-wise information generated through the application of diffusion tensor imaging (DTI) or other models to diffusion weighted (DW) magnetic resonance imaging (MRI) data. Through statistical modelling, we demonstrate that these methods commonly yield substantial and systematic associations between streamline length and several tractography derived quantitative metrics, such as fractional anisotropy (FA). These associations may be described as piecewise linear. For streamlines shorter than an inflection point (determined for a group of tracts delineated for each individual brain), estimates of FA exhibit a positive linear relation with streamline length. For streamlines longer than the point of inflection, the association is weaker, with the slope of the relationship between streamline length and FA differing only marginally from zero. As the association is most pronounced for a range of streamline lengths encountered typically in DW imaging of the human brain (less than ~ 100mm), our results suggest that some quantitative metrics derived from diffusion tractography have the potential to mislead, if variations in streamline length are not considered. A method is described, whereby an Akaike information weighted average of linear, Blackman and piecewise linear model predictions, may be used to compensate effectively for the association of FA (and other quantitative metrics) with streamline length, across the entire range of streamline lengths present in each specimen.

Altered associations between white matter structure and psychopathology in previously institutionalized adolescents

Previously institutionalized adolescents show increased risk for psychopathology, though placement into high-quality foster care can partially mitigate this risk. White matter (WM) structure is associated with early institutional rearing and psychopathology in youth. Here we investigate associations between WM structure and psychopathology in previously institutionalized youth. Adolescent psychopathology data were collected using the MacArthur Health and Behavior Questionnaire. Participants underwent diffusion MRI, and data were processed using fixel-based analyses. General linear models investigated interactions between institutionalization groups and psychopathology on fixel metrics. Supplementary analyses also examined the main effects of psychopathology and institutionalization group on fixel metrics. Ever–Institutionalized children included 41 randomized to foster care (Mage=16.6), and 40 to care-as-usual (Mage=16.7)). In addition, 33 participants without a history of institutionalization were included as a reference group (Mage=16.9). Ever–Institutionalized adolescents displayed altered general psychopathology–fixel associations within the cerebellar peduncles, inferior longitudinal fasciculi, corticospinal tract, and corpus callosum, and altered externalizing–fixel associations within the cingulum and fornix. Our findings indicate brain–behavior associations reported in the literature may not be generalizable to all populations. Previously institutionalized youth may develop differential brain development, which in turn leads to altered neural correlates of psychopathology that are still apparent in adolescence.

Understanding the mechanisms of fatigue in multiple sclerosis: linking interoception, metacognition and white matter dysconnectivity.

One of the most prominent symptoms in multiple sclerosis is pathological fatigue, often described by sufferers as one of the most debilitating symptoms, affecting quality of life and employment. However, the mechanisms of both, physical and cognitive fatigue in multiple sclerosis remain elusive. Here, we use behavioural tasks and quantitative MRI to investigate the neural correlates of interoception (the ability to sense internal bodily signals) and metacognition (the ability of the brain to assess its own performance), in modulating cognitive fatigue. Assuming that structural damage caused by multiple sclerosis pathology might impair the neural pathways subtending interoception and/or metacognition, we considered three alternative hypotheses to explain fatigue as a consequence of, respectively: (i) reduced interoceptive accuracy, (ii) reduced interoceptive insight or (iii) reduced global metacognition. We then explored associations between these behavioural measures and white matter microstructure, assessed by diffusion and magnetisation transfer MRI. Seventy-one relapsing-remitting multiple sclerosis patients participated in this cross-sectional study (mean age 43, 62% female). Patient outcomes relevant for fatigue were measured, including disability, disease duration, depression, anxiety, sleepiness, cognitive function, disease modifying treatment and quality of life. Interoceptive and metacognitive parameters were measured using heartbeat tracking and discrimination tasks, and metacognitive visual and memory tasks. MRI was performed in 69 participants, including diffusion tensor MRI, neurite orientation dispersion and density imaging and quantitative magnetisation transfer. Associations between interoception and metacognition and the odds of high cognitive fatigue were tested by unconditional binomial logistic regression. The odds of cognitive fatigue were higher in the people with low interoceptive insight (P = 0.03), while no significant relationships were found between fatigue and other interoceptive or metacognitive parameters, suggesting a specific impairment in interoceptive metacognition, rather than interoception generally, or metacognition generally. Diffusion MRI-derived fractional anisotropy and neurite density index showed significant (P < 0.05) negative associations with cognitive fatigue in a widespread bilateral white matter network. Moreover, there was a significant (P < 0.05) interaction between cognitive fatigue and interoceptive insight, suggesting that the poorer the white matter structure, the lower the interoceptive insight, and the worse the fatigue. The results point towards metacognitive impairment confined to the interoceptive domain, in relapsing-remitting patients with cognitive fatigue. The neural basis of this impairment is supported by a widespread white matter network in which loss of neurite density plays a role.

Detailed delineation of the fetal brain in diffusion MRI via multi-task learning.

Diffusion-weighted MRI is increasingly used to study the normal and abnormal development of fetal brain inutero. Recent studies have shown that dMRI can offer invaluable insights into the neurodevelopmental processes in the fetal stage. However, because of the low data quality and rapid brain development, reliable analysis of fetal dMRI data requires dedicated computational methods that are currently unavailable. The lack of automated methods for fast, accurate, and reproducible data analysis has seriously limited our ability to tap the potential of fetal brain dMRI for medical and scientific applications. In this work, we developed and validated a unified computational framework to (1) segment the brain tissue into white matter, cortical/subcortical gray matter, and cerebrospinal fluid, (2) segment 31 distinct white matter tracts, and (3) parcellate the brain's cortex and delineate the deep gray nuclei and white matter structures into 96 anatomically meaningful regions. We utilized a set of manual, semi-automatic, and automatic approaches to annotate 97 fetal brains. Using these labels, we developed and validated a multi-task deep learning method to perform the three computations. Our evaluations show that the new method can accurately carry out all three tasks, achieving a mean Dice similarity coefficient of 0.865 on tissue segmentation, 0.825 on white matter tract segmentation, and 0.819 on parcellation. The proposed method can greatly advance the field of fetal neuroimaging as it can lead to substantial improvements in fetal brain tractography, tract-specific analysis, and structural connectivity assessment.

Topological properties of white matter network in first untreated children and adolescents with obsessive-compulsive disorder

Objective: To investigate the topological properties of the white matter network in the drug-naive first-episode children and adolescent with obsessive-compulsive disorder (OCD). Methods: This study was a case-control study. First-episode OCD childhren and adolescents(OCD group) who were treated in the outpatient or inpatient department of the Second Affiliated Hospital of Xinxiang Medical University from July 2018 to October 2023 were collected as the research subjects. Healthy controls (control group)matched by gender, age and education level were used as controls. Deterministic tractography technique was used to construct the whole brain white matter structural network, and graph theory analysis method was used to analyze the topological attributes of the whole brain white matter structure network in OCD children and adolescents. A network-based statistical method was used to examine the inter-group differences in the functional connectivity strength of the whole brain network. Results: Finally, 31 cases were included in the obsessive-compulsive disorder group, including 22 males and 9 females, with an age of (13.5±1.6) years; There were 34 cases in the control group, including 22 males and 12 females, with an age of (12.7±1.4) years. The global efficiency and local efficiency of the OCD group (0.62±0.03, 0.70±0.07) were significantly higher than those of the control group (0.50±0.06, 0.54±0.21) [both P<0.01, false discovery rate(FDR)correction]; while the characteristic path length (1.77±0.08) was significantly smaller than that of the control group (2.10±0.23) (P<0.01, FDR correction).The centrality comparison of nodal betweenness centrality showed that in the OCD group, the connections were enhanced in the left lateral orbitofrontal gyrus, right dorsal agranular insula, left dorsal granular insula, right granular insula, right posterior central gyrus main area of parietal lobe, left ventral granular insula, granular insula, left ventral granulosa, left granular insula, and left dorsal agranular insula [all P<0.001, family wise error (FWE) correction], while the connection of right thalamic was weakened (P<0.001, FWE correction), There were sub-networks characterized by significantly enhanced connection strength of relevant nodes in subcortical, visual network, and default mode network (P<0.05, permutation test 5 000 times). Conclusions: The topological properties of the brain's functional network in children and adolescents with OCD exhibit abnormalities, indicating an immature state of brain functional connectivity.

Structural Development of Speech Networks in Young Children at Risk for Speech Disorder.

Characterizing the structural development of the neural speech network in early childhood is important for understanding speech acquisition. To investigate speech in the developing brain, 94 children aged 4-7-years-old at risk for early speech disorder were scanned using diffusion weighted imaging (DWI) magnetic resonance imaging (MRI). Additionally, each child completed the Syllable Repetition Task (SRT), a validated measure of phoneme articulation. The DWI data were modeled using multi-compartment restriction spectrum imaging (RSI) to measure restricted and hindered diffusion properties in both grey and white matter. Consequently, we analyzed the diffusion data using both whole brain analysis, and automated fiber quantification (AFQ) analysis to establish tract profiles for each of six fiber pathways thought to be important for supporting speech development. In the whole brain analysis, we found that SRT performance was associated with restricted diffusion in bilateral inferior frontal gyrus ( pars opercularis ), right pre-supplementary/ supplementary motor area (pre-SMA/SMA), and bilateral cerebellar grey matter ( p < .005). Age moderated these associations in left pars opercularis and frontal aslant tract (FAT). However, in both cases only the cerebellar findings survived a cluster correction. We also found associations between SRT performance and restricted diffusion in cortical association fiber pathways, especially left FAT, and in the cerebellar peduncles. Analyses using automatic fiber quantification (AFQ) highlighted differences in high and low performing children along specific tract profiles, most notably in left but not right FAT. These findings suggest that individual differences in speech performance are reflected in structural gray and white matter differences as measured by restricted and hindered diffusion metrics, and offer important insights into developing brain networks supporting speech in very young children.

An Algorithmic Approach to MR Imaging of Hypomyelinating Leukodystrophies.

Hypomyelinating leukodystrophies (HLDs) are a heterogeneous group of white matter diseases characterized by permanent deficiency of myelin deposition in brain. MRI is instrumental in the diagnosis and recommending genetic analysis, and is especially useful as many patients have a considerable clinical overlap, with the primary presenting complains being global developmental delay with psychomotor regression. Hypomyelination is defined as deficient myelination on two successive MR scans, taken at least 6 months apart, one of which should have been obtained after 1 year of age. Due to subtle differences in MRI features, the need for a systematic imaging approach to diagnose and classify hypomyelinating disorders is reiterated. The presented article provides an explicit review of imaging features of a myriad of primary and secondary HLDs, using state of the art genetically proven MR cases. A systematic pattern-based approach using MR features and specific clinical clues is illustrated for a quick yet optimal diagnosis of common as well as rare hypomyelinating disorders. The major MR features helping to narrow the differential diagnosis include extent of involvement like diffuse or patchy hypomyelination with selective involvement or sparing of certain white matter structures like optic radiations, median lemniscus, posterior limb of internal capsule and periventricular white matter; cerebellar atrophy; brainstem, corpus callosal or basal ganglia involvement; T2 hypointense signal of the thalami; and presence of calcifications. The authors also discuss the genetic and pathophysiologic basis of HLDs and recent methods to quantify myelin in vivo using advanced neuroradiology tools. The proposed algorithmic approach provides an improved understanding of these rare yet important disorders, enhancing diagnostic precision and improving patient outcomes. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 5.

White matter structure and derived network properties are used to predict the progression from mild cognitive impairment of older adults to Alzheimer’s disease

ObjectiveTo identify white matter fiber injury and network changes that may lead to mild cognitive impairment (MCI) progression, then a joint model was constructed based on neuropsychological scales to predict high-risk individuals for Alzheimer's disease (AD) progression among older adults with MCI.MethodsA total of 173 MCI patients were included from the Alzheimer's Disease Neuroimaging Initiative(ADNI) database and randomly divided into training and testing cohorts. Forty-five progressed to AD during a 4-year follow-up period. Diffusion tensor imaging (DTI) techniques extracted relevant DTI quantitative features for each patient. In addition, brain networks were constructed based on white matter fiber bundles to extract network property features. Ensemble dimensionality reduction was applied to reduce both DTI quantitative features and network features from the training cohort, and machine learning algorithms were added to construct white matter signature. In addition, 52 patients from the National Alzheimer's Coordinating Center (NACC) database were used for external validation of white matter signature. A joint model was subsequently generated by combining with scale scores, and its performance was evaluated using data from the testing cohort.ResultsBased on multivariate logistic regression, clinical dementia rating and Alzheimer’s disease assessment scales (CDRS and ADAS, respectively) were selected as independent predictive factors. A joint model was constructed in combination with the white matter signature. The AUC, sensitivity, and specificity in the training cohort were 0.938, 0.937, and 0.91, respectively, and the AUC, sensitivity, and specificity in the test cohort were 0.905, 0.923, and 0.872, respectively. The Delong test showed a statistically significant difference between the joint model and CDRS or ADAS scores (P < 0.05), yet no significant difference between the joint model and the white matter signature (P = 0.341).ConclusionThe present results demonstrate that a joint model combining neuropsychological scales can be constructed by using machine learning and DTI technology to identify MCI patients who are at high-risk of progressing to AD.