White Matter Fiber Tracts Research Articles

In Silico Investigation of Biomechanical Response of a Human Brain Subjected to Primary Blast.

The brain response to the explosion-induced primary blast waves is actively sought. Over the past decade, reasonable progress has been made in the fundamental understanding of blast traumatic brain injury (bTBI) using head surrogates and animal models. Yet, the current understanding of how blast waves interact with human is in nascent stages, primarily due to the lack of data in human. The biomechanical response in human is critically required to faithfully establish the connection to the aforementioned bTBI models. In this work, the biomechanical cascade of the brain under a primary blast has been elucidated using a detailed, full-body human model. The full-body model allowed us to holistically probe short- (<5 ms) and long-term (200 ms) brain responses. The full-body model has been extensively validated against impact loading in the past. We have further validated the head model against blast loading. We have also incorporated the structural anisotropy of the brain white matter. The blast wave transmission, and linear and rotational motion of the head were dominant pathways for the loading of the brain, and these loading paradigms generated distinct biomechanical fields within the brain. Blast transmission and linear motion of the head governed the volumetric response, whereas the rotational motion of the head governed the deviatoric response. Blast induced head rotation alone produced diffuse injury pattern in white matter fiber tracts. The biomechanical response under blast was comparable to the impact event. These insights will augment laboratory and clinical investigations of bTBI and help devise better blast mitigation strategies.

Four Streams Within the Prefrontal Cortex: Integrating Structural and Functional Connectivity.

Merging functional evidence derived from studies of autism spectrum disorder and attention-deficit/hyperactivity disorder converges in four neural streams of the prefrontal cortex, hence suggesting a model of information processing through four streams: motor through Brodmann area (BA) 8, emotion through BA 9, memory through BA 10, and emotional-related sensory through BA 11. A growing body of functional data has been supporting this model of information processing. Nevertheless, the underlying structural connectivity was only recently unveiled by a population-based high-definition tractography study with data from 1,065 individuals. This update provides a brief overview of recent evidence supporting the anatomofunctional integration of the four streams of the prefrontal cortex and reviews the white matter fiber tracts subserving the four streams.

Exploring subtypes of multiple sclerosis through unsupervised machine learning of automated fiber quantification.

This study aimed to subtype multiple sclerosis (MS) patients using unsupervised machine learning on white matter (WM) fiber tracts and investigate the implications for cognitive function and disability outcomes. We utilized the automated fiber quantification (AFQ) method to extract 18 WM fiber tracts from the imaging data of 103 MS patients in total. Unsupervised machine learning techniques were applied to conduct cluster analysis and identify distinct subtypes. Clinical and diffusion tensor imaging (DTI) metrics were compared among the subtypes, and survival analysis was conducted to examine disability progression and cognitive impairment. The clustering analysis revealed three distinct subtypes with variations in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD). Significant differences were observed in clinical and DTI metrics among the subtypes. Subtype 3 showed the fastest disability progression and cognitive decline, while Subtype 2 exhibited a slower rate, and Subtype 1 fell in between. Subtyping MS based on WM fiber tracts using unsupervised machine learning identified distinct subtypes with significant cognitive and disability differences. WM abnormalities may serve as biomarkers for predicting disease outcomes, enabling personalized treatment strategies and prognostic predictions for MS patients.

TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance

We propose a geometric deep-learning-based framework, TractGeoNet, for performing regression using diffusion magnetic resonance imaging (dMRI) tractography and associated pointwise tissue microstructure measurements. By employing a point cloud representation, TractGeoNet can directly utilize tissue microstructure and positional information from all points within a fiber tract without the need to average or bin data along the streamline as traditionally required by dMRI tractometry methods. To improve regression performance, we propose a novel loss function, the Paired-Siamese Regression loss, which encourages the model to focus on accurately predicting the relative differences between regression label scores rather than just their absolute values. In addition, to gain insight into the brain regions that contribute most strongly to the prediction results, we propose a Critical Region Localization algorithm. This algorithm identifies highly predictive anatomical regions within the white matter fiber tracts for the regression task. We evaluate the effectiveness of the proposed method by predicting individual performance on two neuropsychological assessments of language using a dataset of 20 association white matter fiber tracts from 806 subjects from the Human Connectome Project Young Adult dataset. The results demonstrate superior prediction performance of TractGeoNet compared to several popular regression models that have been applied to predict individual cognitive performance based on neuroimaging features. Of the twenty tracts studied, we find that the left arcuate fasciculus tract is the most highly predictive of the two studied language performance assessments. Within each tract, we localize critical regions whose microstructure and point information are highly and consistently predictive of language performance across different subjects and across multiple independently trained models. These critical regions are widespread and distributed across both hemispheres and all cerebral lobes, including areas of the brain considered important for language function such as superior and anterior temporal regions, pars opercularis, and precentral gyrus. Overall, TractGeoNet demonstrates the potential of geometric deep learning to enhance the study of the brain's white matter fiber tracts and to relate their structure to human traits such as language performance.

Integration of diffusion tensor imaging parameters with mesh morphing for in-depth analysis of brain white matter fibre tracts.

Averaging is commonly used for data reduction/aggregation to analyse high-dimensional MRI data, but this often leads to information loss. To address this issue, we developed a novel technique that integrates diffusion tensor metrics along the whole volume of the fibre bundle using a 3D mesh-morphing technique coupled with principal component analysis for delineating case and control groups. Brain diffusion tensor MRI scans of high school rugby union players (n = 30, age 16-18) were acquired on a 3 T MRI before and after the sports season. A non-contact sport athlete cohort with matching demographics (n = 12) was also scanned. The utility of the new method in detecting differences in diffusion tensor metrics of the right corticospinal tract between contact and non-contact sport athletes was explored. The first step was to run automated tractography on each subject's native space. A template model of the right corticospinal tract was generated and morphed into each subject's native shape and space, matching individual geometry and diffusion metric distributions with minimal information loss. The common dimension of the 20 480 diffusion metrics allowed further data aggregation using principal component analysis to cluster the case and control groups as well as visualization of diffusion metric statistics (mean, ±2 SD). Our approach of analysing the whole volume of white matter tracts led to a clear delineation between the rugby and control cohort, which was not possible with the traditional averaging method. Moreover, our approach accounts for the individual subject's variations in diffusion tensor metrics to visualize group differences in quantitative MR data. This approach may benefit future prediction models based on other quantitative MRI methods.

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.

Brain white matter microstructural alterations in patients with systemic lupus erythematosus: an automated fiber quantification study.

This study aimed to identify damaged segments of brain white matter fiber tracts in patients with systemic lupus erythematosus (SLE) using diffusion tensor imaging (DTI)-based automated fiber quantification (AFQ), and analyze their relationship with cognitive impairment. Clinical and imaging data for 39 female patients with SLE and for 44 female healthy controls (HCs) were collected. AFQ was used to track whole-brain white matter tracts in each participant, and each tract was segmented into 100 equally spaced nodes. DTI metrics including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated at each node. Correlations were also explored between DTI metrics in the damaged segments of white matter fiber tracts and neuropsychological test scores of patients with SLE. Compared with HCs, SLE patients exhibited significantly lower FA values, and significantly higher MD, AD, RD values in many white matter tracts (all P < 0.05, false discovery rate-corrected). FA values in nodes 97-100 of the left inferior fronto-occipital fasciculus (IFOF) positively correlated with the mini-mental state examination score. AFQ enables precise and accurate identification of damage to white matter fiber tracts in brains of patients with SLE. FA values in the left IFOF correlate with cognitive impairment in SLE.

Alterations in white matter fiber tracts and their correlation with flying cadet behavior.

An increasing number of studies have shown that flight training alters the human brain structure; however, most studies have focused on gray matter, and the exploration of white matter structure has been largely neglected. This study aimed to investigate the changes in white matter structure induced by flight training and estimate the correlation between such changes and psychomotor and flight performance. Diffusion tensor imaging data were obtained from 25 flying cadets and 24 general college students. Data were collected in 2019 and 2022 and analyzed using automated fiber quantification. This study found no significant changes in the flight group in 2019. However, in 2022, the flight group exhibited significant alterations in the diffusion tensor imaging of the right anterior thalamic radiation, left cingulum cingulate, bilateral superior longitudinal fasciculus, and left arcuate fasciculus. These changes occurred within local nodes of the fiber tracts. In addition, we found that changes in fiber tracts in the 2022 flight group were correlated with the reaction time of the psychomotor test task and flight duration. These findings may help improve flight training programs and provide new ideas for the selection of excellent pilots.

White matter microstructure alterations in type 2 diabetes mellitus and its correlation with cerebral small vessel disease and cognitive performance

Microstructural abnormalities of white matter fiber tracts are considered as one of the etiology of diabetes-induced neurological disorders. We explored the cerebral white matter microstructure alteration accurately, and to analyze its correlation between cerebral small vessel disease (CSVD) burden and cognitive performance in type 2 diabetes mellitus (T2DM). The clinical-laboratory data, cognitive scores [including mini-mental state examination (MMSE), Montreal cognitive assessment (MoCA), California verbal learning test (CVLT), and symbol digit modalities test (SDMT)], CSVD burden scores of the T2DM group (n = 34) and healthy control (HC) group (n = 21) were collected prospectively. Automatic fiber quantification (AFQ) was applied to generate bundle profiles along primary white matter fiber tracts. Diffusion tensor images (DTI) metrics and 100 nodes of white matter fiber tracts between groups were compared. Multiple regression analysis was used to analyze the relationship between DTI metrics and cognitive scores and CSVD burden scores. For fiber-wise and node-wise, DTI metrics in some commissural and association fibers were increased in T2DM. Some white matter fiber tracts DTI metrics were independent predictors of cognitive scores and CSVD burden scores. White matter fiber tracts damage in patients with T2DM may be characterized in specific location, especially commissural and association fibers. Aberrational specific white matter fiber tracts are associated with visuospatial function and CSVD burden.

Cortical microstructural involvement in cerebral small vessel disease

BackgroundIn cerebral small vessel disease (CSVD), cortical atrophy occurs at a later stage compared to microstructural abnormalities and therefore cannot be used for monitoring short-term disease progression. We aimed to investigate whether cortical diffusion tensor imaging (DTI) and quantitative (q) magnetic resonance imaging (MRI) are able to detect early microstructural involvement of the cerebral cortex in CSVD. Materials and Methods33 CSVD patients without significant cortical or whole-brain atrophy and 16 healthy control subjects were included and underwent structural MRI, DTI and high-resolution qMRI with T2, T2* and T2′ mapping at 3 T as well as comprehensive cognitive assessment. After tissue segmentation and reconstruction of the cortical boundaries with the Freesurfer software, DTI and qMRI parameters were saved as surface datasets and averaged across all vertices. ResultsCortical diffusivity and quantitative T2 values were significantly increased in patients compared to controls (p < 0.05). T2 values correlated significantly positively with white matter hyperintensity (WMH) volume (p < 0.01). Both cortical diffusivity and T2 showed significant negative associations with axonal damage to the white matter fiber tracts (p < 0.05). ConclusionsCortical diffusivity and quantitative T2 mapping are suitable to detect microstructural involvement of the cerebral cortex in CSVD and represent promising imaging biomarkers for monitoring disease progression and effects of therapeutical interventions in clinical studies.

Mixed reality compared to the traditional ex cathedra format for neuroanatomy learning: the value of a three-dimensional virtual environment to better understand the real world.

Neuroanatomy comprehension is a keystone of understanding intracranial surgeries. Traditionally taught to students during ex cathedra courses, neuroanatomy is described as complex. Mixed reality (MxR) opens new perspectives in the learning process. This study aims to compare MxR-based courses with traditional ex cathedra lectures for neuroanatomy education. Two lectures describing the neuroanatomy of the anterior circulation arteries ("Vascular Lecture" [VS]) and important white matter fiber tracts ("White Fibers Lecture" [WF]) were designed and delivered in ex cathedra and MxR-based formats with the same audio content. Ninety-one medical students were randomly assigned to group A (ex cathedra WF/MxR VS) or group B (MxR WF/ex cathedra VS). The MxR content was delivered via MxR goggles. Prior to each lecture, students took a 10-item multiple choice question (MCQ) pretest. After the lectures, students took a 20-item MCQ posttest (75% neuroanatomy, 25% clinical correlation). The pretest scores showed no statistical difference between groups. Median posttest scores increased by 14.3% after using the MxR-based format compared to the ex cathedra format (16.00 [13.0, 18.0] vs 14.0 [11.0, 17.0], respectively, p < 0.01). Regarding the VS, students scored 21.7% better using the MxR format compared to the ex cathedra format (14.0 [12.0, 16.0] vs 11.5 [10.0, 14.0], p < 0.001). Concerning the WF, the median score using MxR was 18.0 (17.0, 19.0), and the median score using the ex cathedra format was 17.0 (16.0, 18.0; p < 0.01). Students showed high motivation to learn neuroanatomy in the future using MxR (74%) rather than ex cathedra format (25%; p < 0.001). Mild discomfort using the MxR goggles was reported by 48.3% of participants. Most participants (95.5%) preferred the MxR-based teaching. Students acquired a better knowledge of the anatomy of the anterior circulation arteries and white fiber tracts using MxR-based teaching as compared to the standard ex cathedra format. The perception of lecture quality and learning motivation was better using MxR-based teaching despite some mild discomfort. The development of MxR-based solutions is promising to improve neuroanatomy education.

Bridging Reduced Grip Strength and Altered Executive Function: Specific Brain White Matter Structural Changes in Patients with Alzheimer's Disease.

To investigate the correlation between specific fiber tracts and grip strength and cognitive function in patients with Alzheimer's disease (AD) by fixel-based analysis (FBA). AD patients were divided into AD with low grip strength (AD-LGS, n=29) and AD without low grip strength (AD-nLGS, n=25), along with 31 normal controls (NC). General data, neuropsychological tests, grip strength and cranial magnetic resonance imaging (MRI) scans were collected. FBA evaluated white matter (WM) fiber metrics, including fiber density (FD), fiber cross-sectional (FC), and fiber density and cross-sectional area (FDC). The mean fiber indicators of the fiber tracts of interest (TOI) were extracted in cerebral region of significant statistical differences in FBA to further compare the differences between groups and analyze the correlation between fiber properties and neuropsychological test scores. Compared to AD-nLGS group, AD-LGS group showed significant reductions in FDC in several cerebral regions. In AD patients, FDC values of bilateral uncinate fasciculus and left superior longitudinal fasciculus were positively correlated with Clock Drawing Test scores, while FDC of splenium of corpus callosum, bilateral anterior cingulate tracts, forceps major, and bilateral inferior longitudinal fasciculus were positively correlated with the Executive Factor Score of Memory and Executive Screening scale scores. Reduced grip strength in AD patients is associated with extensive impairment of WM structural integrity. Changes in FDC of specific WM fiber tracts related to executive function play a significant mediating role in the reduction of grip strength in AD patients.

White Matter Tract Integrity Is Reduced in Depression and in Individuals With Genetic Liability to Depression

BackgroundWhile major depression has been linked to changes in white matter architecture, it remains unclear whether risk factors for depression are directly associated with these alterations. We reexamined white matter fiber tracts in individuals with depressive symptoms and investigated the connection between genetic and environmental risk for depression and structural changes in the brain. MethodsWe included 19,183 participants from the UK Biobank imaging cohort, with depression status and adverse life experience based on questionnaire data and genetic liability for depression quantified by polygenic scores. The integrity of 27 white matter tracts was assessed using mean fractional anisotropy derived from diffusion magnetic resonance imaging. ResultsWhite matter integrity was reduced, particularly in thalamic and intracortical fiber tracts, in individuals with depressive symptoms, independent of current symptom status. In a group of healthy individuals without depression, increasing genetic risk and increasing environmental risk were associated with reduced integrity in relevant fiber tracts, particularly in thalamic radiations. This association was stronger than expected based on statistical dependencies between samples, as confirmed by subsequent in silico simulations and permutation tests. ConclusionsWhite matter alterations in thalamic and association tracts are associated with depressive symptoms and genetic risk for depression in unaffected individuals, suggesting an intermediate phenotype at the brain level.

Florbetapir PET-assessed demyelination is associated with faster tau accumulation in an APOE ε4-dependent manner.

The main objectives were to test whether (1) a decrease in myelin is associated with enhanced rate of fibrillar tau accumulation and cognitive decline in Alzheimer's disease, and (2) whether apolipoprotein E (APOE) ε4 genotype is associated with worse myelin decrease and thus tau accumulation. To address our objectives, we repurposed florbetapir-PET as a marker of myelin in the white matter (WM) based on previous validation studies showing that beta-amyloid (Aβ) PET tracers bind to WM myelin. We assessed 43 Aβ-biomarker negative (Aβ-) cognitively normal participants and 108 Aβ+ participants within the AD spectrum with florbetapir-PET at baseline and longitudinal flortaucipir-PET as a measure of fibrillar tau (tau-PET) over ~ 2 years. In linear regression analyses, we tested florbetapir-PET in the whole WM and major fiber tracts as predictors of tau-PET accumulation in a priori defined regions of interest (ROIs) and fiber-tract projection areas. In mediation analyses we tested whether tau-PET accumulation mediates the effect of florbetapir-PET in the whole WM on cognition. Finally, we assessed the role of myelin alteration on the association between APOE and tau-PET accumulation. Lower florbetapir-PET in the whole WM or at a given fiber tract was predictive of faster tau-PET accumulation in Braak stages or the connected grey matter areas in Aβ+ participants. Faster tau-PET accumulation in higher cortical brain areas mediated the association between a decrease in florbetapir-PET in the WM and a faster rate of decline in global cognition and episodic memory. APOE ε4 genotype was associated with a worse decrease in the whole WM florbetapir-PET and thus enhanced tau-PET accumulation. Myelin alterations are associated in an APOE ε4 dependent manner with faster tau progression and cognitive decline, and may therefore play a role in the etiology of AD.

The association between microbiome and white matter integrity in the elderly with youthful memory in superaging

AbstractBackgroundAging‐related cognitive decline has been regarded as a normal consequence of aging. However, previous research on ‘SuperAgers’ suggests that such decline is not inevitable. The purpose of this study was to investigate the relationship between the microbiome and white matter integrity in SuperAgers, which may help us understand the mechanism that preserves memory function in these individuals.MethodWe enrolled a total of 115 elderly individuals aged 60 years or older with normal cognitive function, which was defined as scoring one standard deviation or more above the population norm on tests of memory, attention, language, visuospatial ability, and frontal function in the Seoul Neuropsychological Screening Battery. SuperAgers were defined based on their delayed recall scores compared to middle‐aged adults (45 years old) in both the Seoul Verbal Learning Test and the Rey‐Osterrieth Complex Figure Test. Diffusion tensor images were analyzed using tract‐based spatial statistics (TBSS). Parameters related to diffusion tensors were calculated at the voxel level to generate fractional anisotropy (FA) images using the DTIFit. FA values, a diffusion tensor parameter reflecting white matter fiber tract integrity, were measured and compared between SuperAgers and typical agers using the FSL randomise program. For microbiome analysis, EZ biocloud was used to perform alpha diversity, taxonomic hierarchy, and taxonomic composition analysis for each subject analysis. Group analysis was also conducted using the taxonomic composition values, and further group analysis was carried out using ACE, Chao1, and Jacknife, which are indicators of species richness. Statistical analyses were conducted using Statistical Package for Social Science 19.ResultCompared to typical agers, superagers showed a higher number of Firmicutes and Actinobacteria, which have been associated with higher cognitive function. However, there was no significant difference between superagers and typical agers. Additionally, an analysis of the association between p_firmicutes, c_mollicutes, o_lactobacilales and f_erysipelotrichaceae with white matter integrity showed no significant association.ConclusionBased on these results, it can be inferred that superagers may have a correlation with microbiome strains that are closely related to cognitive function, as well as white matter in the brain

White matter fiber integrity and structural brain network topology: implications for balance function in postischemic stroke patients.

Previous studies have suggested that ischemic stroke can result in white matter fiber injury and modifications in the structural brain network. However, the relationship with balance function scores remains insufficiently explored. Therefore, this study aims to explore the alterations in the microstructural properties of brain white matter and the topological characteristics of the structural brain network in postischemic stroke patients and their potential correlations with balance function. We enrolled 21 postischemic stroke patients and 21 age, sex, and education-matched healthy controls (HC). All participants underwent balance function assessment and brain diffusion tensor imaging. Tract-based spatial statistics (TBSS) were used to compare the fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity of white matter fibers between the two groups. The white matter structural brain network was constructed based on the automated anatomical labeling atlas, and we conducted a graph theory-based analysis of its topological properties, including global network properties and local node properties. Additionally, the correlation between the significant structural differences and balance function score was analyzed. The TBSS results showed that in comparison to the HC, postischemic stroke patients exhibited extensive damage to their whole-brain white matter fiber tracts (P < 0.05). Graph theory analysis showed that in comparison to the HC, postischemic stroke patients exhibited statistically significant reductions in the values of global efficiency, local efficiency, and clustering coefficient, as well as an increase in characteristic path length (P < 0.05). In addition, the degree centrality and nodal efficiency of some nodes in postischemic stroke patients were significantly reduced (P < 0.05). The white matter fibers of the entire brain in postischemic stroke patients are extensively damaged, and the topological properties of the structural brain network are altered, which are closely related to balance function. This study is helpful in further understanding the neural mechanism of balance function after ischemic stroke from the white matter fiber and structural brain network topological properties.

Autism spectrum disorder-specific changes in white matter connectome edge density based on functionally defined nodes.

Autism spectrum disorder (ASD) is associated with both functional and microstructural connectome disruptions. We deployed a novel methodology using functionally defined nodes to guide white matter (WM) tractography and identify ASD-related microstructural connectome changes across the lifespan. We used diffusion tensor imaging and clinical data from four studies in the national database for autism research (NDAR) including 155 infants, 102 toddlers, 230 adolescents, and 96 young adults - of whom 264 (45%) were diagnosed with ASD. We applied cortical nodes from a prior fMRI study identifying regions related to symptom severity scores and used these seeds to construct WM fiber tracts as connectome Edge Density (ED) maps. Resulting ED maps were assessed for between-group differences using voxel-wise and tract-based analysis. We then examined the association of ASD diagnosis with ED driven from functional nodes generated from different sensitivity thresholds. In ED derived from functionally guided tractography, we identified ASD-related changes in infants (pFDR ≤ 0.001-0.483). Overall, more wide-spread ASD-related differences were detectable in ED based on functional nodes with positive symptom correlation than negative correlation to ASD, and stricter thresholds for functional nodes resulted in stronger correlation with ASD among infants (z = -6.413 to 6.666, pFDR ≤ 0.001-0.968). Voxel-wise analysis revealed wide-spread ED reductions in central WM tracts of toddlers, adolescents, and adults. We detected early changes of aberrant WM development in infants developing ASD when generating microstructural connectome ED map with cortical nodes defined by functional imaging. These were not evident when applying structurally defined nodes, suggesting that functionally guided DTI-based tractography can help identify early ASD-related WM disruptions between cortical regions exhibiting abnormal connectivity patterns later in life. Furthermore, our results suggest a benefit of involving functionally informed nodes in diffusion imaging-based probabilistic tractography, and underline that different age cohorts can benefit from age- and brain development-adapted image processing protocols.

Symposium 07: Early Development in Infants and Toddlers with Agenesis of the Corpus Callosum

Summary AbstractThe corpus callosum is the largest transverse white matter fiber tract connecting the two hemispheres of the brain. It plays an essential role in the interhemispheric transfer and bihemispheric coordination involved in perception and sensory-motor integration, as well as higher level cognition (e.g., memory, executive functioning). The corpus callosum forms prenatally (10-15 weeks gestation) and continues to develop through early adulthood. Damage to the corpus callosum throughout the lifespan, but especially later in life, can result in sensory and motor coordination deficits and produces disconnection syndromes.However, individuals born with a partial or fully absent corpus callosum, known as Agenesis of the Corpus Callosum (ACC), do not exhibit a full disconnection syndrome and may, in fact, demonstrate many intact skills including broadly average intellectual abilities. Agenesis of the corpus callosum (ACC) is a relatively common congenital brain malformation that occurs in 1 out of ∼4000 live births (Glass, Shaw, Ma, &amp; Sherr, 2008; Paul et al., 2007). There is significant heterogeneity in outcomes for these individuals, often related to the presence of additional neuropathology, and the presence of co-occurring medical and genetic conditions. In adults for whom ACC is the primary neurological finding, there is a core constellation of cognitive symptoms that include reduced interhemispheric transfer, slowed processing, and difficulty with complex novel problem solving (Brown &amp; Paul, 2019). There is also an elevated likelihood of autism spectrum disorders in this population as well (Paul et al, 2014).Modern ultrasound technology facilitates diagnosis of ACC in utero, offering a unique opportunity to study these individuals’ development from infancy. However, no studies to date have examined early development in this population using validated measures, leaving neuropsychologists, neurologists and primary care providers to make educated guesses about what families should expect and appropriate therapies/treatment.This symposium presents a series of studies examining the early development of individuals with primary ACC across domains including language, adaptive skills, autism symptomology, and temperament/anxiety. We will provide the first in-depth prospective characterization of development in ACC relative to typically developing children at 6, 12, 18 and 24 months and discuss what these findings reveal about brain development and plasticity more broadly (e.g. how does early disruption in interhemispheric connection impact cognitive development across key domains?).Finally, while ACC is defined specifically by corpus callosum anatomy, the corpus callosum has been implicated in other neurodevelopmental conditions (Paul, 2011). Utilizing extant comparison, we also assess how early development in ACC both overlaps and differs from two monogenic conditions (e.g. Fragile X and Down Syndrome) and a developmental behavioral diagnoses (e.g. autism) and discuss how early disruptions in interhemispheric transfer may contribute to shared behavioral phenotypes in these conditions.

GLIOMA-06 THE GLIOMA CONNECTOME PROJECT – UPDATE

Abstract The ability to define specific fiber tracts in individual patients before, during and following surgery represents a major advance in neurosurgical oncology. The Human Connectome Project (Glasser, 2016) generated insights into the organization of critically important “hubs” and pathways (edges) defining 7 major networks. There is a paradigm shift in our view of neurological functions from an outdated “modular” concept to a network perspective (Fox, 2018). Connectomics affects all branches of clinical neuroscience, including neuropsychiatry, stroke, trauma, neurodegenerative diseases, and especially glioblastoma. We first developed individualized maps of white matter (WM) pathways for a connectivity-based paradigm for neurosurgical planning (Tunç, 2016). We reviewed the principles and the potential applications of connectomics in the neurosurgical treatment of gliomas (Henderson, 2020). Working with key thought leaders, experienced neurosurgeons, and scientific partners (Omniscient Neurotechnology; Michael Sughrue), we developed the Glioma Connectome Project protocol, accruing the first patients at the University of Pennsylvania (UPCC#22321), funded by the StacheStrong, and now accruing patients nationwide: U. Miami (PI, Michael Ivan), Northwestern (PI, Matthew Tate), U. of Nebraska (Michele Aizenberg), Henry Ford Hospital (Ian Lee), University of Texas (Yoshua Esquenazi), and U. Pittsburgh (Costas Hadjipanayis). The specific aims of the GCP include 1) validation of connectomic metrics (global efficiency, FWHM, betweenness centrality, etc.); 2) cognitive correlates using the NIH Toolbox; 3) mapping the trajectory of recovery; 4) the effects of treatment at the level of WM fiber tracts; 5) re-imagining surgical approaches and 6) developing biomarkers for “virtual trials” to assess response to therapy. In the past 2.5 years since the inception of the GCP, we have overcome numerous technical and organizational challenges to develop a federated system to harmonize “big data”. The scientific and practical lessons learned will be highlighted.

Enhanced white matter fiber tract of the cortical visual system in visual artists: implications for creativity.

This study aimed to examine the white matter characteristics of visual artists (VAs) in terms of visual creativity and the structural connectivity within the cortical visual system. Diffusion spectrum imaging was utilized to examine the changes in white matter within the cortical visual system of a group of VAs (n = 25) in comparison to a group of healthy controls matched for age and education (n = 24). To assess the integrity of white matter and its relationship with visual creativity, we conducted a comprehensive analysis using region-based and track-specific tractographic examinations. Our study uncovered that VAs demonstrated increased normalized quantitative anisotropy in specific brain regions, including the right inferior temporal gyrus and right lateral occipital gyrus, along with the corresponding white matter fiber tracts connecting these regions. These enhancements within the cortical visual system were also found to be correlated with measures of visual creativity obtained through psychological assessments. The noted enhancement in the white matter within the cortical visual system of VAs, along with its association with visual creativity, is consistent with earlier research demonstrating heightened functional connectivity in the same system among VAs. Our study's findings suggest a link between the visual creativity of VAs and structural alterations within the brain's visual system.