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Charcot–Marie–Tooth disease (CMT) is a genetically heterogeneous hereditary peripheral neuropathy. Brain volumetry and diffusion tensor imaging (DTI) were performed in 47 controls and 47 CMT patients with PMP22 duplication (n = 10), MFN2 (n = 15), GJB1 (n = 11), or NEFL mutations (n = 11) to investigate for structural changes in the cerebellum. Volume of cerebellar white matter (WM) was significantly reduced in CMT patients with NEFL mutations. Abnormal DTI findings were observed in the superior, middle, and inferior cerebellar peduncles, predominantly in NEFL mutations and partly in GJB1 mutations. Cerebellar ataxia was more prevalent in the NEFL mutation group (72.7%) than the GJB1 mutation group (9.1%) but was not observed in other genotypic subtypes, which indicates that structural cerebellar abnormalities were associated with the presence of cerebellar ataxia. However, NEFL and GJB1 mutations did not affect cerebellar gray matter (GM), and neither cerebellar GM nor WM abnormalities were observed in the PMP22 duplication or MFN2 mutation groups. We found structural evidence of cerebellar WM abnormalities in CMT patients with NEFL and GJB1 mutations and an association between cerebellar WM involvement and cerebellar ataxia in these genetic subtypes, especially in the NEFL subgroup. Therefore, we suggest that neuroimaging, such as MRI volumetry or DTI, for CMT patients could play an important role in detecting abnormalities of cerebellar WM.

Whether reading in different writing systems recruits language-unique or language-universal neural processes is a long-standing debate. Many studies have shown the left arcuate fasciculus (AF) to be involved in phonological and reading processes. In contrast, little is known about the role of the right AF in reading, but some have suggested that it may play a role in visual spatial aspects of reading or the prosodic components of language. The right AF may be more important for reading in Chinese due to its logographic and tonal properties, but this hypothesis has yet to be tested. We recruited a group of Chinese-English bilingual children (8.2 to 12.0 years old) to explore the common and unique relation of reading skill in English and Chinese to fractional anisotropy (FA) in the bilateral AF. We found that both English and Chinese reading skills were positively correlated with FA in the rostral part of the left AF-direct segment. Additionally, English reading skill was positively correlated with FA in the caudal part of the left AF-direct segment, which was also positively correlated with phonological awareness. In contrast, Chinese reading skill was positively correlated with FA in certain segments of the right AF, which was positively correlated with visual spatial ability, but not tone discrimination ability. Our results suggest that there are language universal substrates of reading across languages, but that certain left AF nodes support phonological mechanisms important for reading in English, whereas certain right AF nodes support visual spatial mechanisms important for reading in Chinese.

An emerging field of human brain imaging deals with the characterization of the connectome, a comprehensive global description of structural and functional connectivity within the human brain. However, the question of how functional and structural connectivity are related has not been fully answered yet. Here, we used different methods to estimate the connectivity between each voxel of the cerebral cortex based on functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data in order to obtain observer-independent functional–structural connectomes of the human brain. Probabilistic fiber-tracking and a novel global fiber-tracking technique were used to measure structural connectivity whereas for functional connectivity, full and partial correlations between each voxel pair's fMRI-timecourses were calculated. For every voxel, two vectors consisting of functional and structural connectivity estimates to all other voxels in the cortex were correlated with each other. In this way, voxels structurally and functionally connected to similar regions within the rest of the brain could be identified. Areas forming parts of the ‘default mode network’ (DMN) showed the highest agreement of structure–function connectivity. Bilateral precuneal and inferior parietal regions were found using all applied techniques, whereas the global tracking algorithm additionally revealed bilateral medial prefrontal cortices and early visual areas. There were no significant differences between the results obtained from full and partial correlations. Our data suggests that the DMN is the functional brain network, which uses the most direct structural connections. Thus, the anatomical profile of the brain seems to shape its functional repertoire and the computation of the whole-brain functional–structural connectome appears to be a valuable method to characterize global brain connectivity within and between populations. •Structure–function connectivity relationship•Multi-modal data fusion•Voxel-wise connectivity analysis•Default mode network•Global fiber-tracking

The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA–DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18–85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at (http://enigma.loni.ucla.edu/ongoing/dti-working-group/). •We harmonize a DTI protocol for genetic studies of FA; protocols are made public.•Template created from 400 adults (18–85) from 4 sites with different DTI parameters•Meta-analysis of heritability from 2 sites performed voxelwise and in ROIs.•Reliable pooled heritability estimates found for most regions of the brain.•Results will help guide future studies to harmonize and combine DTI data.

Diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) can quantify indices related to brain structure and their change in pathology. However, only few studies have applied these techniques to spinal cord injury (SCI), and subtle microstructural changes in the brain of SCI individuals are not well understood. Our goal was to investigate structural changes in the brain using DTI (fractional anisotropy, FA; mean diffusivity, MD) and DKI parameters (kurtosis anisotropy, KA; mean kurtosis, MK) in subacute SCI and to study whether these changes were associated with clinical outcomes. Twenty-eight individuals with SCI underwent brain MRI 3 months post-injury, alongside 20 healthy controls. Imaging included a multi-shell diffusion protocol, from which DTI and DKI metrics (FA, MD, KA and MK) were derived. Group comparisons were conducted for each metric across 17 brain regions selected based on their relevance to SCI from previous studies. Multiple comparison corrections were applied per metric to account for the number of examined regions. Effect sizes were calculated using Cohen's . For regions showing significant group differences, Spearman correlations were performed to assess associations between imaging metrics and clinical outcomes, including neurological status (ISNCSCI) and functional independence (SCIM III), with correction for multiple comparisons. MD was significantly higher in the right genu of the corpus callosum in the SCI group (adjusted  = 0.021). In this region, MD negatively correlated with SCIM scores (  = -0.51,  = 0.022), whereas MK showed a positive correlation (  = 0.482,  = 0.038). Structural changes in the corpus callosum may reflect impaired interhemispheric communication, linked to reduced functional independence after SCI. DTI and DKI could serve as complementary tools for identifying brain-based biomarkers, potentially informing recovery trajectories.

The objective of this study is to evaluate machine learning algorithms aimed at predicting surgical treatment outcomes in groups of patients with temporal lobe epilepsy (TLE) using only the structural brain connectome. Specifically, the brain connectome is reconstructed using white matter fiber tracts from presurgical diffusion tensor imaging. To achieve our objective, a two-stage connectome-based prediction framework is developed that gradually selects a small number of abnormal network connections that contribute to the surgical treatment outcome, and in each stage a linear kernel operation is used to further improve the accuracy of the learned classifier. Using a 10-fold cross validation strategy, the first stage in the connectome-based framework is able to separate patients with TLE from normal controls with 80% accuracy, and second stage in the connectome-based framework is able to correctly predict the surgical treatment outcome of patients with TLE with 70% accuracy. Compared to existing state-of-the-art methods that use VBM data, the proposed two-stage connectome-based prediction framework is a suitable alternative with comparable prediction performance. Our results additionally show that machine learning algorithms that exclusively use structural connectome data can predict treatment outcomes in epilepsy with similar accuracy compared with “expert-based” clinical decision. In summary, using the unprecedented information provided in the brain connectome, machine learning algorithms may uncover pathological changes in brain network organization and improve outcome forecasting in the context of epilepsy. •Presurgery brain connectome and temporal lobe epilepsy treatment outcome prediction•Brain connectomes reconstructed using white matter fiber tracts from DTI•Two-stage connectome-based framework used to predict treatment outcome•Machine learning algorithms that exclusively use structural connectome data•Prediction framework accuracy is similar to expert-based clinical decision.

The prenatal period represents a critical time for brain growth and development. These rapid neurological advances render the fetus susceptible to various influences with life-long implications for mental health. Maternal distress signals are a dominant early life influence, contributing to birth outcomes and risk for offspring psychopathology. This prospective longitudinal study evaluated the association between prenatal maternal distress and infant white matter microstructure. Participants included a racially and socioeconomically diverse sample of 85 mother–infant dyads. Prenatal distress was assessed at 17 and 29 weeks’ gestational age (GA). Infant structural data were collected via diffusion tensor imaging (DTI) at 42–45 weeks’ postconceptional age. Findings demonstrated that higher prenatal maternal distress at 29 weeks’ GA was associated with increased fractional anisotropy, b = .283, t (64) = 2.319, p = .024, and with increased axial diffusivity, b = .254, t (64) = 2.067, p = .043, within the right anterior cingulate white matter tract. No other significant associations were found with prenatal distress exposure and tract fractional anisotropy or axial diffusivity at 29 weeks’ GA, or earlier in gestation.

Aims This study aimed to assess the glymphatic dysfunction in idiopathic normal pressure hydrocephalus (iNPH) patients and its recovery post‐shunt surgery using diffusion tensor image analysis along perivascular spaces (DTI‐ALPS). Methods Thirty‐five iNPH patients and forty healthy controls (HC) underwent MRI scans and neuropsychological assessments at baseline. A follow‐up study, conducted three months post‐shunt surgery, included fifteen iNPH patients. The DTI‐ALPS index was calculated to assess the glymphatic system status. Group differences were evaluated using the Mann–Whitney U test, while the paired Wilcoxon signed‐rank test was employed to compare pre‐operative and post‐operative ALPS indices. Multiple linear regression was utilized to analyze the association between changes in the ALPS index (ΔALPS) and alterations in clinical scores. Results Baseline examinations disclosed iNPH patients had a lower ALPS index than HC (p < 0.0001). We found a significantly increased ALPS index at 3 months after surgery compared to baseline (p < 0.0001). Positive correlations between theΔALPS and the increments of MMSE score (ΔMMSE) were found in all iNPH patients. Baseline age and ΔALPS emerged as significant predictors of ΔMMSE, with the model explaining 68.13% of the variance (R2 = 0.6813). Conclusion Glymphatic function in iNPH was enhanced following shunt surgery, which positively impacted cognitive recovery. The DTI‐ALPS index may serve as a useful predictor of shunting efficacy in iNPH patients.

Diverse structural and functional brain alterations have been identified in both schizophrenia and bipolar disorder, but with variable replicability, significant overlap and often in limited number of subjects. In this paper, we aimed to clarify differences between bipolar disorder and schizophrenia by combining fMRI (collected during an auditory oddball task) and diffusion tensor imaging (DTI) data. We proposed a fusion method, “multimodal CCA+ joint ICA”, which increases flexibility in statistical assumptions beyond existing approaches and can achieve higher estimation accuracy. The data collected from 164 participants (62 healthy controls, 54 schizophrenia and 48 bipolar) were extracted into “features” (contrast maps for fMRI and fractional anisotropy (FA) for DTI) and analyzed in multiple facets to investigate the group differences for each pair-wised groups and each modality. Specifically, both patient groups shared significant dysfunction in dorsolateral prefrontal cortex and thalamus, as well as reduced white matter (WM) integrity in anterior thalamic radiation and uncinate fasciculus. Schizophrenia and bipolar subjects were separated by functional differences in medial frontal and visual cortex, as well as WM tracts associated with occipital and frontal lobes. Both patients and controls showed similar spatial distributions in motor and parietal regions, but exhibited significant variations in temporal lobe. Furthermore, there were different group trends for age effects on loading parameters in motor cortex and multiple WM regions, suggesting that brain dysfunction and WM disruptions occurred in identified regions for both disorders. Most importantly, we can visualize an underlying function–structure network by evaluating the joint components with strong links between DTI and fMRI. Our findings suggest that although the two patient groups showed several distinct brain patterns from each other and healthy controls, they also shared common abnormalities in prefrontal thalamic WM integrity and in frontal brain mechanisms. ► fMRI–DTI data fusion across 3 groups (HC, SZ, BP) for 164 subjects. ► Propose a novel general, flexible fusion model based on multimodal CCA+ joint ICA. ► Identify group-discriminating aspects in multiple facets. ► Visualize the potential function–structure network by evaluating the joint ICs.

Different imaging modalities provide essential complementary information that can be used to enhance our understanding of brain disorders. This study focuses on integrating multiple imaging modalities to identify individuals at risk for mild cognitive impairment (MCI). MCI, often an early stage of Alzheimer's disease (AD), is difficult to diagnose due to its very mild or insignificant symptoms of cognitive impairment. Recent emergence of brain network analysis has made characterization of neurological disorders at a whole-brain connectivity level possible, thus providing new avenues for brain diseases classification. Employing multiple-kernel Support Vector Machines (SVMs), we attempt to integrate information from diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) for improving classification performance. Our results indicate that the multimodality classification approach yields statistically significant improvement in accuracy over using each modality independently. The classification accuracy obtained by the proposed method is 96.3%, which is an increase of at least 7.4% from the single modality-based methods and the direct data fusion method. A cross-validation estimation of the generalization performance gives an area of 0.953 under the receiver operating characteristic (ROC) curve, indicating excellent diagnostic power. The multimodality classification approach hence allows more accurate early detection of brain abnormalities with greater sensitivity.