Severity-Dependent Interhemispheric White Matter Connectivity Predicts Poststroke Neglect Recovery.

Left-sided spatial neglect is a very common and challenging issue after right-hemispheric stroke, which strongly and negatively affects daily living behavior and recovery of stroke survivors. The mechanisms underlying recovery of spatial neglect remain controversial, particularly regarding the involvement of the intact, contralesional hemisphere, with potential contributions ranging from maladaptive to compensatory. In the present prospective, observational study, we assessed neglect severity in 54 right-hemispheric stroke patients (32 male; 22 female) at admission to and discharge from inpatient neurorehabilitation. We demonstrate that the interaction of initial neglect severity and spared white matter (dis)connectivity resulting from individual lesions (as assessed by diffusion tensor imaging, DTI) explains a significant portion of the variability of poststroke neglect recovery. In mildly impaired patients, spared structural connectivity within the lesioned hemisphere is sufficient to attain good recovery. Conversely, in patients with severe impairment, successful recovery critically depends on structural connectivity within the intact hemisphere and between hemispheres. These distinct patterns, mediated by their respective white matter connections, may help to reconcile the dichotomous perspectives regarding the role of the contralesional hemisphere as exclusively compensatory or not. Instead, they suggest a unified viewpoint wherein the contralesional hemisphere can - but must not necessarily - assume a compensatory role. This would depend on initial impairment severity and on the available, spared structural connectivity. In the future, our findings could serve as a prognostic biomarker for neglect recovery and guide patient-tailored therapeutic approaches.

Altered white matter connectivity of ventral language networks in autism spectrum disorder: An automated fiber quantification analysis with multi-site datasets.

Comprehension and pragmatic deficits are prevalent in autism spectrum disorder (ASD) and are potentially linked to altered connectivity in the ventral language networks. However, previous magnetic resonance imaging studies have not sufficiently explored the microstructural abnormalities in the ventral fiber tracts underlying comprehension dysfunction in ASD. Additionally, the precise locations of white matter (WM) changes in the long tracts of patients with ASD remain poorly understood. In the current study, we applied the automated fiber-tract quantification (AFQ) method to investigate the fine-grained WM properties of the ventral language pathway and their relationships with comprehension and symptom manifestation in ASD. The analysis included diffusion/T1 weighted imaging data of 83 individuals with ASD and 83 age-matched typically developing (TD) controls. Case-control comparisons were performed on the diffusion metrics of the ventral tracts at both the global and point-wise levels. We also explored correlations between diffusion metrics, comprehension performance, and ASD traits, and conducted subgroup analyses based on age range to examine developmental moderating effects. Individuals with ASD exhibited remarkable hypoconnectivity in the ventral tracts, particularly in the temporal portions of the left inferior longitudinal fasciculus (ILF) and the inferior fronto-occipital fasciculus (IFOF). These WM abnormalities were associated with poor comprehension and more severe ASD symptoms. Furthermore, WM alterations in the ventral tract and their correlation with comprehension dysfunction were more prominent in younger children with ASD than in adolescents. These findings indicate that WM disruptions in the temporal portions of the left ILF/IFOF are most notable in ASD, potentially constituting the core neurological underpinnings of comprehension and communication deficits in autism. Moreover, impaired WM connectivity and comprehension ability in patients with ASD appear to improve with age.

Cortico-striatal white-matter connectivity underlies the ability to exert goal-directed control.

The balance between goal-directed and habitual control has been proposed to determine the flexibility of instrumental behaviour, in both humans and animals. This view is supported by neuroscientific studies that have implicated dissociable neural pathways in the ability to flexibly adjust behaviour when outcome values change. A previous Diffusion Tensor Imaging study provided preliminary evidence that flexible instrumental performance depends on the strength of parallel cortico-striatal white-matter pathways previously implicated in goal-directed and habitual control. Specifically, estimated white-matter strength between caudate and ventromedial prefrontal cortex correlated positively with behavioural flexibility, and posterior putamen-premotor cortex connectivity correlated negatively, in line with the notion that these pathways compete for control. However, the sample size of the original study was limited, and so far, there have been no attempts to replicate these findings. In the present study, we aimed to conceptually replicate these findings by testing a large sample of 205 young adults to relate cortico-striatal connectivity to performance on the slips-of-action task. In short, we found only positive neural correlates of goal-directed performance, including striatal connectivity (caudate and anterior putamen) with the dorsolateral prefrontal cortex. However, we failed to provide converging evidence for the existence of a neural habit system that puts limits on the capacity for flexible, goal-directed action. We discuss the implications of our findings for dual-process theories of instrumental action.

An accelerated degeneration of white matter microstructure and networks in the nondemented old-old.

The nondemented old-old over the age of 80 comprise a rapidly increasing population group; they can be regarded as exemplars of successful aging. However, our current understanding of successful aging in advanced age and its neural underpinnings is limited. In this study, we measured the microstructural and network-based topological properties of brain white matter using diffusion-weighted imaging scans of 419 community-dwelling nondemented older participants. The participants were further divided into 230 young-old (between 72 and 79, mean = 76.25 ± 2.00) and 219 old-old (between 80 and 92, mean = 83.98 ± 2.97). Results showed that white matter connectivity in microstructure and brain networks significantly declined with increased age and that the declined rates were faster in the old-old compared with young-old. Mediation models indicated that cognitive decline was in part through the age effect on the white matter connectivity in the old-old but not in the young-old. Machine learning predictive models further supported the crucial role of declines in white matter connectivity as a neural substrate of cognitive aging in the nondemented older population. Our findings shed new light on white matter connectivity in the nondemented aging brains and may contribute to uncovering the neural substrates of successful brain aging.

White matter microstructural variability linked to differential attentional skills and impulsive behavior in a pediatric population.

Structural and functional magnetic resonance imaging (MRI) studies have suggested a neuroanatomical basis that may underly attention-deficit-hyperactivity disorder (ADHD), but the anatomical ground truth remains unknown. In addition, the role of the white matter (WM) microstructure related to attention and impulsivity in a general pediatric population is still not well understood. Using a state-of-the-art structural connectivity pipeline based on the Brainnetome atlas extracting WM connections and its subsections, we applied dimensionality reduction techniques to obtain biologically interpretable WM measures. We selected the top 10 connections-of-interests (located in frontal, parietal, occipital, and basal ganglia regions) with robust anatomical and statistical criteria. We correlated WM measures with psychometric test metrics (Conner's Continuous Performance Test 3) in 171 children (27 Dx ADHD, 3Dx ASD, 9-13 years old) from the population-based GESTation and Environment cohort. We found that children with lower microstructural complexity and lower axonal density show a higher impulsive behavior on these connections. When segmenting each connection in subsections, we report WM alterations localized in one or both endpoints reflecting a specific localization of WM alterations along each connection. These results provide new insight in understanding the neurophysiology of attention and impulsivity in a general population.

High-resolution multi-shot diffusion imaging of structural networks in healthy neurocognitive aging.

Healthy neurocognitive aging has been associated with the microstructural degradation of white matter pathways that connect distributed gray matter regions, assessed by diffusion-weighted imaging (DWI). However, the relatively low spatial resolution of standard DWI has limited the examination of age-related differences in the properties of smaller, tightly curved white matter fibers, as well as the relatively more complex microstructure of gray matter. Here, we capitalize on high-resolution multi-shot DWI, which allows spatial resolutions < 1 mm3 to be achieved on clinical 3T MRI scanners. We assessed whether traditional diffusion tensor-based measures of gray matter microstructure and graph theoretical measures of white matter structural connectivity assessed by standard (1.5 mm3 voxels, 3.375 µl volume) and high-resolution (1 mm3 voxels, 1µl volume) DWI were differentially related to age and cognitive performance in 61 healthy adults 18-78 years of age. Cognitive performance was assessed using an extensive battery comprising 12 separate tests of fluid (speed-dependent) cognition. Results indicated that the high-resolution data had larger correlations between age and gray matter mean diffusivity, but smaller correlations between age and structural connectivity. Moreover, parallel mediation models including both standard and high-resolution measures revealed that only the high-resolution measures mediated age-related differences in fluid cognition. These results lay the groundwork for future studies planning to apply high-resolution DWI methodology to further assess the mechanisms of both healthy aging and cognitive impairment.

Associations of maternal emotion regulation with child white matter connectivity in Black American mother-child dyads.

Parental emotion regulation plays a major role in parent-child interactions, and in turn, neural plasticity in children, particularly during sensitive developmental periods. However, little is known about how parental emotion dysregulation is associated with variation in children's brain structure, which was the goal of this study. Forty-five Black American mother-child dyads were recruited from an intergenerational trauma study; emotion regulation in mothers and their children (age 8-13 years) was assessed. Diffusion-weighted images were collected in children; deterministic tractography was used to reconstruct pathways of relevance to emotion regulation. Metrics of white matter connectivity [fractional anisotropy (FA), mean diffusivity (MD)] were extracted for pathways. Socio-economic variables were also included in statistical models. Maternal emotion dysregulation was the strongest predictor of child fornix MD (r = .35, p = .001), indicating that more severe emotion dysregulation in mothers corresponded with lower fornix connectivity in children. Maternal impulsivity was a strong predictor of child fornix MD (r = .51, p < .001). Maternal emotion dysregulation may adversely influence connectivity of the child.s fornix, a hippocampal-striatal pathway implicated in reward processes; these associations remained even after accounting for other socio-environmental factors. Dysregulated maternal emotions may uniquely impact children's adaptation to trauma/stress by affecting networks that support appetitive processing.

Joint contributions of cortical morphometry and white matter microstructure in healthy brain aging: A partial least squares correlation analysis.

Cortical atrophy and degraded axonal health have been shown to coincide during normal aging; however, few studies have examined these measures together. To lend insight into both the regional specificity and the relative timecourse of structural degradation of these tissue compartments across the adult lifespan, we analyzed gray matter (GM) morphometry (cortical thickness, surface area, volume) and estimates of white matter (WM) microstructure (fractional anisotropy, mean diffusivity) using traditional univariate and more robust multivariate techniques to examine age associations in 186 healthy adults aged 20-94 years old. Univariate analysis of each tissue type revealed that negative age associations were largest in frontal GM and WM tissue and weaker in temporal, cingulate, and occipital regions, representative of not only an anterior-to-posterior gradient, but also a medial-to-lateral gradient. Multivariate partial least squares correlation (PLSC) found the greatest covariance between GM and WM was driven by the relationship between WM metrics in the anterior corpus callosum and projections of the genu, anterior cingulum, and fornix; and with GM thickness in parietal and frontal regions. Surface area was far less susceptible to age effects and displayed less covariance with WM metrics, while regional volume covariance patterns largely mirrored those of cortical thickness. Results support a retrogenesis-like model of aging, revealing a coupled relationship between frontal and parietal GM and the underlying WM, which evidence the most protracted development and the most vulnerability during healthy aging.

Effects of hypogonadism on brain development during adolescence in girls with Turner syndrome.

Gonadal steroids play an important role in brain development, particularly during puberty. Girls with Turner syndrome (TS), a genetic disorder characterized by the absence of all or part of the second X chromosome, mostly present a loss of ovarian function and estrogen deficiency, as well as neuroanatomical abnormalities. However, few studies have attempted to isolate the indirect effects of hormones from the direct genetic effects of X chromosome insufficiency. Brain structural (i.e., gray matter [GM] morphology and white matter [WM] connectivity) and functional phenotypes (i.e., resting-state functional measures) were investigated in 23 adolescent girls with TS using multimodal MRI to assess the role of hypogonadism in brain development in TS. Specifically, all girls with TS were divided into a hormonally subnormal group and an abnormal subgroup according to their serum follicle-stimulating hormone (FSH) levels, with the karyotypes approximately matched between the two groups. Statistical analyses revealed significant effects of the "group-by-age" interaction on GM volume around the left medial orbitofrontal cortex and WM diffusion parameters around the bilateral corticospinal tract, anterior thalamic radiation, left superior longitudinal fasciculus, and cingulum bundle, but no significant "group-by-age" or group differences were observed in resting-state functional measures. Based on these findings, estrogen deficiency has a nontrivial impact on the development of the brain structure during adolescence in girls with TS. Our present study provides novel insights into the mechanism by which hypogonadism influences brain development during adolescence in girls with TS, and highlights the important role of estrogen replacement therapy in treating TS.

Analytic tractography: A closed-form solution for estimating local white matter connectivity with diffusion MRI.

White matter structures composed of myelinated axons in the living human brain are primarily studied by diffusion-weighted MRI (dMRI). These long-range projections are typically characterized in a two-step process: dMRI signal is used to estimate the orientation of axon segments within each voxel, then these local orientations are linked together to estimate the spatial extent of putative white matter bundles. Tractography, the process of tracing bundles across voxels, either requires computationally expensive (probabilistic) simulations to model uncertainty in fiber orientation or ignores it completely (deterministic). Furthermore, simulation necessarily generates a finite number of trajectories, introducing "simulation error" to trajectory estimates. Here we introduce a method to analytically (via a closed-form solution) take an orientation distribution function (ODF) from each voxel and calculate the probabilities that a trajectory projects from a voxel into each directly adjacent voxels. We validate our method by demonstrating experimentally that probabilistic simulations converge to our analytically computed transition probabilities at the voxel level as the number of simulated seeds increases. We then show that our method accurately calculates the ground-truth transition probabilities from a publicly available phantom dataset. As a demonstration, we incorporate our analytic method for voxel transition probabilities into the Voxel Graph framework, creating a quantitative framework for assessing white matter structure, which we call "analytic tractography". The long-range connectivity problem is reduced to finding paths in a graph whose adjacency structure reflects voxel-to-voxel analytic transition probabilities. We demonstrate that this approach performs comparably to the current most widely-used probabilistic and deterministic approaches at a fraction of the computational cost. We also demonstrate that analytic tractography works on multiple diffusion sampling schemes, reconstruction method or parameters used to define paths. Open source software compatible with popular dMRI reconstruction software is provided.

Whole brain white matter connectivity analysis using machine learning: An application to autism.

In this paper, we propose an automated white matter connectivity analysis method for machine learning classification and characterization of white matter abnormality via identification of discriminative fiber tracts. The proposed method uses diffusion MRI tractography and a data-driven approach to find fiber clusters corresponding to subdivisions of the white matter anatomy. Features extracted from each fiber cluster describe its diffusion properties and are used for machine learning. The method is demonstrated by application to a pediatric neuroimaging dataset from 149 individuals, including 70 children with autism spectrum disorder (ASD) and 79 typically developing controls (TDC). A classification accuracy of 78.33% is achieved in this cross-validation study. We investigate the discriminative diffusion features based on a two-tensor fiber tracking model. We observe that the mean fractional anisotropy from the second tensor (associated with crossing fibers) is most affected in ASD. We also find that local along-tract (central cores and endpoint regions) differences between ASD and TDC are helpful in differentiating the two groups. These altered diffusion properties in ASD are associated with multiple robustly discriminative fiber clusters, which belong to several major white matter tracts including the corpus callosum, arcuate fasciculus, uncinate fasciculus and aslant tract; and the white matter structures related to the cerebellum, brain stem, and ventral diencephalon. These discriminative fiber clusters, a small part of the whole brain tractography, represent the white matter connections that could be most affected in ASD. Our results indicate the potential of a machine learning pipeline based on white matter fiber clustering.

Tests of cortical parcellation based on white matter connectivity using diffusion tensor imaging.

The cerebral cortex is conventionally divided into a number of domains based on cytoarchitectural features. Diffusion tensor imaging (DTI) enables noninvasive parcellation of the cortex based on white matter connectivity patterns. However, the correspondence between DTI-connectivity-based and cytoarchitectural parcellation has not been systematically established. In this study, we compared histological parcellation of New World monkey neocortex to DTI- connectivity-based classification and clustering in the same brains. First, we used supervised classification to parcellate parieto-frontal cortex based on DTI tractograms and the cytoarchitectural prior (obtained using Nissl staining). We performed both within and across sample classification, showing reasonable classification performance in both conditions. Second, we used unsupervised clustering to parcellate the cortex and compared the clusters to the cytoarchitectonic standard. We then explored the similarities and differences with several post-hoc analyses, highlighting underlying principles that drive the DTI-connectivity-based parcellation. The differences in parcellation between DTI-connectivity and Nissl histology probably represent both DTI's bias toward easily-tracked bundles and true differences between cytoarchitectural and connectivity defined domains. DTI tractograms appear to cluster more according to functional networks, rather than mapping directly onto cytoarchitectonic domains. Our results show that caution should be used when DTI-tractography classification, based on data from another brain, is used as a surrogate for cytoarchitectural parcellation.

Connectivity of the ventral visual cortex is necessary for object recognition in patients.

The functional profiles of regions in the ventral occipital-temporal cortex (VTC), a critical region for object visual recognition, are associated with the VTC connectivity patterns to nonvisual regions relevant to the corresponding object domain. However, whether and how whole-brain connections affect recognition behavior remains untested. We directly examined the necessity of VTC connectivity in object recognition behavior by testing 82 patients whose lesion spared relevant VTC regions but affected various white matter (WM) tracts and other regions. In these patients, we extracted the whole-brain anatomical connections of two VTC domain-selective (large manmade objects and animals) clusters with probabilistic tractography, and examined whether such connectivity pattern can predict recognition performance of the corresponding domains with support vector regression (SVR) analysis. We found that the whole-brain anatomical connectivity of large manmade object-specific cluster successfully predicted patients' large object recognition performance but not animal recognition or control tasks, even after we excluded connections with early visual regions. The contributing connections to large object recognition included tracts between VTC-large object cluster and distributed regions both within and beyond the visual cortex (e.g., putamen, superior, and middle temporal gyrus). These results provide causal evidence that the VTC whole-brain anatomical connectivity is necessary for at least certain domains of object recognition behavior.

Human Cerebellar Sub-millimeter Diffusion Imaging Reveals the Motor and Non-motor Topography of the Dentate Nucleus.

The reciprocal cortico-cerebellar loops that underlie cerebellar contributions to motor and cognitive behavior form one of the largest systems in the primate brain. Work with non-human primates has shown that the dentate nucleus, the major output nucleus of the cerebellum, contains topographically distinct connections to both motor and non-motor regions, yet there is no evidence for how the cerebellar cortex connects to the dentate nuclei in humans. Here we used in-vivo sub-millimeter diffusion imaging to characterize this fundamental component of the cortico-cerebellar loop, and identified a pattern of superior motor and infero-lateral non-motor connectivity strikingly similar to that proposed by animal work. Crucially, we also present first evidence that the dominance for motor connectivity observed in non-human primates may be significantly reduced in man - a finding that is in accordance with the proposed increase in cerebellar contributions to higher cognitive behavior over the course of primate evolution.

Mapping neuroplastic potential in brain-damaged patients.

It is increasingly acknowledged that the brain is highly plastic. However, the anatomic factors governing the potential for neuroplasticity have hardly been investigated. To bridge this knowledge gap, we generated a probabilistic atlas of functional plasticity derived from both anatomic magnetic resonance imaging results and intraoperative mapping data on 231 patients having undergone surgery for diffuse, low-grade glioma. The atlas includes detailed level of confidence information and is supplemented with a series of comprehensive, connectivity-based cluster analyses. Our results show that cortical plasticity is generally high in the cortex (except in primary unimodal areas and in a small set of neural hubs) and rather low in connective tracts (especially associative and projection tracts). The atlas sheds new light on the topological organization of critical neural systems and may also be useful in predicting the likelihood of recovery (as a function of lesion topology) in various neuropathological conditions-a crucial factor in improving the care of brain-damaged patients.

White Matter Connectivity Pattern Associate with Characteristics of Scalp EEG Signals.

The rhythm of electroencephalogram (EEG) depends on the neuroanatomical-based parameters such as white matter (WM) connectivity. However, the impacts of these parameters on the specific characteristics of EEG have not been clearly understood. Previous studies demonstrated that, these parameters contribute the inter-subject differences of EEG during performance of specific task such as motor imagery (MI). Though researchers have worked on this phenomenon, the idea is yet to be understood in terms of the mechanism that underlies such differences. Here, to tackle this issue, we began our investigations by first examining the structural features related to scalp EEG characteristics, which are event-related desynchronizations (ERDs), during MI using diffusion MRI. Twenty-four right-handed subjects were recruited to accomplish MI tasks and MRI scans. Based on the high spatial resolution of the structural and diffusion images, the motor-related WM links, such as basal ganglia (BG)-primary somatosensory cortex (SM1) pathway and supplementary motor area (SMA)-SM1 connection, were reconstructed by using probabilistic white matter tractography. Subsequently, the relationships of WM characteristics with EEG signals were investigated. These analyses demonstrated that WM pathway characteristics, including the connectivity strength and the positional characteristics of WM connectivity on SM1 (defined by the gyrus-sulcus ratio of connectivity, GSR), have a significant impact on ERDs when doing MI. Interestingly, the high GSR of WM connections between SM1 and BG were linked to the better ERDs. These results therefore, indicated that the connectivity in the gyrus of SM1 interacted with MI network which played the critical role for the scalp EEG signal extraction of MI to a great extent. The study provided the coupling mechanism between structural and dynamic physiological features of human brain, which would also contribute to understanding individual differences of EEG in MI-brain computer interface.

Disrupted white matter structural connectivity in heroin abusers.

Neurocognitive impairment is one of the factors that put heroin abusers at greater risk for relapse, and deficits in related functional brain connections have been found. However, the alterations in structural brain connections that may underlie these functional and neurocognitive impairments remain largely unknown. In the present study, we investigated topological organization alterations in the structural network of white matter in heroin abusers and examined the relationships between the network changes and clinical measures. We acquired diffusion tensor imaging datasets from 76 heroin abusers and 78 healthy controls. Network-based statistic was applied to identify alterations in interregional white matter connectivity, and graph theory methods were used to analyze the properties of global networks. The participants also completed a battery of neurocognitive measures. One increased subnetwork characterizing widespread abnormalities in structural connectivity was present in heroin users, which mainly composed of default-mode, attentional and visual systems. The connection strength was positively correlated with increases in fractional anisotropy in heroin abusers. Intriguingly, the changes in within-frontal and within-temporal connections in heroin abusers were significantly correlated with daily heroin dosage and impulsivity scores, respectively. These findings suggest that heroin abusers have extensive abnormal white matter connectivity, which may mediate the relationship between heroin dependence and clinical measures. The increase in white matter connectivity may be attributable to the inefficient microstructure integrity of white matter. The present findings extend our understanding of cerebral structural disruptions that underlie neurocognitive and functional deficits in heroin addiction and provide circuit-level markers for this chronic disorder.

Human Frontal-Subcortical Circuit and Asymmetric Belief Updating.

How humans integrate information to form beliefs about reality is a question that has engaged scientists for centuries, yet the biological system supporting this process is not well understood. One of the most salient attributes of information is valence. Whether a piece of news is good or bad is critical in determining whether it will alter our beliefs. Here, we reveal a frontal-subcortical circuit in the left hemisphere that is simultaneously associated with enhanced integration of favorable information into beliefs and impaired integration of unfavorable information. Specifically, for favorable information, stronger white matter connectivity within this system, particularly between the left inferior frontal gyrus (IFG) and left subcortical regions (including the amygdala, hippocampus, thalamus, putamen, and pallidum), as well as insular cortex, is associated with greater change in belief. However, for unfavorable information, stronger connectivity within this system, particularly between the left IFG and left pallidum, putamen, and insular cortex, is associated with reduced change in beliefs. These novel results are consistent with models suggesting that partially separable processes govern learning from favorable and unfavorable information. SIGNIFICANCE STATEMENT: Beliefs of what may happen in the future are important, because they guide decisions and actions. Here, we illuminate how structural brain connectivity is related to the generation of subjective beliefs. We focus on how the valence of information is related to people's tendency to alter their beliefs. By quantifying the extent to which participants update their beliefs in response to desirable and undesirable information and relating those measures to the strength of white matter connectivity using diffusion tensor imaging, we characterize a left frontal-subcortical system that is associated simultaneously with greater belief updating in response to favorable information and reduced belief updating in response to unfavorable information. This neural architecture may allow valence to be incorporated into belief updating.

Simulating disease propagation across white matter connectome reveals anatomical substrate for neuropathology staging in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease, characterized by progressive loss of motor function. While the pathogenesis of ALS remains largely unknown, recent histological examinations of Brettschneider and colleagues have proposed four time-sequential stages of neuropathology in ALS based on levels of phosphorylated 43kDa TAR DNA-binding protein (pTDP-43) aggregation. What governs dissemination of these aggregates between segregated regions of the brain is unknown. Here, we cross-reference stages of pTDP-43 pathology with in vivo diffusion weighted imaging data of 215 adult healthy control subjects, and reveal that regions involved in pTDP-43 pathology form a strongly interconnected component of the brain network (p=0.04) likely serving as an anatomical infrastructure facilitating pTDP-43 spread. Furthermore, brain regions of subsequent stages of neuropathology are shown to be more closely interconnected than regions of more distant stages (p=0.002). Computational simulation of disease spread from first-stage motor regions across the connections of the brain network reveals a pattern of pTDP-43 aggregation that reflects the stages of sequential involvement in neuropathology (p=0.02), a pattern in favor of the hypothesis of pTDP-43 pathology to spread across the brain along axonal pathways. Our findings thus provide computational evidence of disease spread in ALS to be directed and constrained by the topology of the anatomical brain network.

Disrupted white matter connectivity underlying developmental dyslexia: A machine learning approach.

Developmental dyslexia has been hypothesized to result from multiple causes and exhibit multiple manifestations, implying a distributed multidimensional effect on human brain. The disruption of specific white-matter (WM) tracts/regions has been observed in dyslexic children. However, it remains unknown if developmental dyslexia affects the human brain WM in a multidimensional manner. Being a natural tool for evaluating this hypothesis, the multivariate machine learning approach was applied in this study to compare 28 school-aged dyslexic children with 33 age-matched controls. Structural magnetic resonance imaging (MRI) and diffusion tensor imaging were acquired to extract five multitype WM features at a regional level: white matter volume, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. A linear support vector machine (LSVM) classifier achieved an accuracy of 83.61% using these MRI features to distinguish dyslexic children from controls. Notably, the most discriminative features that contributed to the classification were primarily associated with WM regions within the putative reading network/system (e.g., the superior longitudinal fasciculus, inferior fronto-occipital fasciculus, thalamocortical projections, and corpus callosum), the limbic system (e.g., the cingulum and fornix), and the motor system (e.g., the cerebellar peduncle, corona radiata, and corticospinal tract). These results were well replicated using a logistic regression classifier. These findings provided direct evidence supporting a multidimensional effect of developmental dyslexia on WM connectivity of human brain, and highlighted the involvement of WM tracts/regions beyond the well-recognized reading system in dyslexia. Finally, the discriminating results demonstrated a potential of WM neuroimaging features as imaging markers for identifying dyslexic individuals.