Multimodal neural correlates of dispositional resilience among healthy individuals

Resilient individuals are less likely to develop psychiatric disorders despite extreme psychological distress. This study investigated the multimodal structural neural correlates of dispositional resilience among healthy individuals. Participants included 92 healthy individuals. The Korean version of the Connor-Davidson Resilience Scale and other psychological measures were used. Gray matter volumes (GMVs), cortical thickness, local gyrification index (LGI), and white matter (WM) microstructures were analyzed using voxel-based morphometry, FreeSurfer, and tract-based spatial statistics, respectively. Higher resilient individuals showed significantly higher GMVs in the inferior frontal gyrus (IFG), increased LGI in the insula, and lower fractional anisotropy values in the superior longitudinal fasciculus II (SLF II). These resilience's neural correlates were associated with good quality of life in physical functioning or general health and low levels of depression. Therefore, the GMVs in the IFG, LGI in the insula, and WM microstructures in the SLF II can be associated with resilience that contributes to emotional regulation, empathy, and social cognition.

Functional dysregulation of the auditory cortex in bilateral perisylvian polymicrogyria: Multiparametric case analysis of the absent speech phenotype

The absence of speech is a clinical phenotype seen across neurodevelopmental syndromes, offering insights for neural language models. We present a case of bilateral perisylvian polymicrogyria (BPP) and complete absence of speech with considerable language comprehension and production difficulties. We extensively characterized the auditory speech perception and production circuitry by employing a multimodal neuroimaging approach. Results showed extensive cortical thickening in motor and auditory-language regions. The auditory cortex lacked sensitivity to speech stimuli despite relatively preserved thalamic projections yet had no intrinsic functional organization. Subcortical structures implicated in early stages of processing exhibited heightened sensitivity to speech. The arcuate fasciculus, a suggested marker of language in BPP, showed similar volume and integrity to a healthy control. The frontal aslant tract, linked to oromotor function, was partially reconstructed. These findings highlight the importance of assessing the auditory cortex beyond speech production structures to understand absent speech in BPP. Despite profound cortical alterations, the intrinsic motor network and motor-speech pathways remained largely intact. This case underscores the need for comprehensive phenotyping using multiple MRI modalities to uncover causes of severe disruption in language development.

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.

Discriminative patterns of white matter changes in Alzheimer's

Changes in structural connectivity of the Alzheimer's brain have not been widely studied utilizing cutting-edge methodologies. This study develops an efficient structural connectome-based convolutional neural network (CNN) to classify the AD and uses explanations of CNNs' choices in classification to pinpoint the discriminative changes in white matter connectivity in AD. A CNN architecture has been developed to classify normal control (NC) and AD subjects from the weighted structural connectome. Then, the CNN classification decision is visually analyzed using gradient-based localization techniques to identify the discriminative changes in white matter connectivity in Alzheimer's. The cortical regions involved in the identified discriminative structural connectivity changes in AD are highly covered in the temporal/subcortical regions. A specific pattern is identified in the discriminative changes in structural connectivity of AD, where the white matter changes are revealed within the temporal/subcortical regions and from the temporal/subcortical regions to the frontal and parietal regions in both left and right hemispheres.  The proposed approach has the potential to comprehensively analyze the discriminative structural connectivity differences in AD, change the way of detecting biomarkers, and help clinicians better understand the structural changes in AD and provide them with more confidence in automated diagnostic systems.

Cortico-amygdalar connectivity and externalizing/internalizing behavior in children with neurodevelopmental disorders

Background

Externalizing and internalizing behaviors contribute to clinical impairment in children with neurodevelopmental disorders (NDDs). Although associations between externalizing or internalizing behaviors and cortico-amygdalar connectivity have been found in clinical and non-clinical pediatric samples, no previous study has examined whether similar shared associations are present across children with different NDDs.

Methods

Multi-modal neuroimaging and behavioral data from the Province of Ontario Neurodevelopmental Disorders (POND) Network were used. POND participants aged 6–18 years with a primary diagnosis of autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD) or obsessive–compulsive disorder (OCD), as well as typically developing children (TDC) with T1-weighted, resting-state fMRI or diffusion weighted imaging (DWI) and parent-report Child Behavioral Checklist (CBCL) data available, were analyzed (total n = 346). Associations between externalizing or internalizing behavior and cortico-amygdalar structural and functional connectivity indices were examined using linear regressions, controlling for age, gender, and image-modality specific covariates. Behavior-by-diagnosis interaction effects were also examined.

Results

No significant linear associations (or diagnosis-by-behavior interaction effects) were found between CBCL-measured externalizing or internalizing behaviors and any of the connectivity indices examined. Post-hoc bootstrapping analyses indicated stability and reliability of these null results.

Conclusions

The current study provides evidence towards an absence of a shared linear relationship between internalizing or externalizing behaviors and cortico-amygdalar connectivity properties across a transdiagnostic sample of children with different primary NDD diagnoses and TDC. Different methodological approaches, including incorporation of multi-dimensional behavioral data (e.g., task-based fMRI) or clustering approaches may be needed to clarify complex brain-behavior relationships relevant to externalizing/internalizing behaviors in heterogeneous clinical NDD populations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00429-022-02483-0.

White matter connectivity networks predict levodopa-induced dyskinesia in Parkinson's disease

BACKGROUND

Although levodopa-induced dyskinesia-relevant white matter change has been evaluated, it is uncertain whether these changes may reflect the underlying predisposing conditions leading to the development of levodopa-induced dyskinesia.

OBJECTIVE

To elucidate the role of white matter connectivity networks in the development of levodopa-induced dyskinesia in drug-naïve Parkinson's disease.

METHODS

We recruited 30 patients who developed levodopa-induced dyskinesia within 5 years from MRI acquisition (vulnerable-group), 47 patients who had not developed levodopa-induced dyskinesia within 5 years (resistant-group), and 28 controls. We performed comparative analyses of whole-brain white matter integrity and connectivity using tract-based spatial and network- and degree-based statistics. We evaluated the predictability of levodopa-induced dyskinesia development and relationship with its latency, using the average connectivity strength as a predictor in Cox- and linear-regression, respectively.

RESULTS

Mean-diffusivity was lower mainly at the left frontal region in the vulnerable-group compared to the resistant-group. Network-based statistics identified a subnetwork consisting of the bilateral fronto-striato-pallido-thalamic and lateral parietal regions (subnetwork A) and degree-based statistics identified four subnetworks (hub-subnetwork) consisting of edges centered on the left superior frontal gyrus, left putamen, left insular, or left precentral gyrus, where the vulnerable-group had stronger connectivity compared to the resistant-group. Stronger connectivity within the subnetwork A and hub-subnetwork centered on the left superior frontal gyrus was a predictor of levodopa-induced dyskinesia development independent of known risk factors and had an inverse relationship with its latency.

CONCLUSIONS

Our data suggest that white matter connectivity subnetworks within corticostriatal regions play a pivotal role in the development of levodopa-induced dyskinesia.

Initial white matter connectivity differences between remitters and non-remitters of patients with panic disorder after 6 months of pharmacotherapy

Panic disorder (PD) is a harmful mental condition that causes relapsed and persistent impairment. In the treatment of PD, the prognosis for PD should be considered. However, the relationship between pharmacotherapy and biomarkers, for predicting a better response through neuroimaging, is a little known. The purpose of the present study was to examine whether there would be the initial white matter (WM) regions associated with the remission in 6 months. A total of 104 patients with PD were investigated in the study. After six months, there were 17 remission patients with PD and 81 non-remission patients. The Panic Disorder Severity Scale, Albany Panic and Phobia Questionnaire, Anxiety Sensitivity Inventory-Revised, Beck Anxiety Inventory, and Beck Depression Inventory were assessed for all patients at baseline. We compared the diffusion indices between remission and non-remission group at 6 months using Tract-Based Spatial Statistics. The results showed that the fractional anisotropy (FA) values were significantly higher in the non-remitter group compared with those in the remitter group in the WM regions, such as the posterior corona radiata and superior longitudinal fasciculus, at the 6 month evaluation. The logistic regression analysis with clinical symptom severity and FA values of the WM regions as covariates showed that FA values in those regions and the Beck Depression Inventory-II predicted poor remission. This study suggests that posterior corona radiata and superior longitudinal fasciculus are related to potential predictive factors of 6-month remission in patients with PD. WM regions associated with the long-term remission should be verified with further investigations.

INTEGRATIVE NETWORK LEARNING FOR MULTI-MODALITY BIOMARKER DATA

The biomarker networks measured by different modalities of data (e.g., structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI)) may share the same true underlying biological model. In this work, we propose a node-wise biomarker graphical model to leverage the shared mechanism between multi-modality data to provide a more reliable estimation of the target modality network and account for the heterogeneity in networks due to differences between subjects and networks of external modality. Latent variables are introduced to represent the shared unobserved biological network and the information from the external modality is incorporated to model the distribution of the underlying biological network. We propose an efficient approximation to the posterior expectation of the latent variables that reduces computational cost by at least 50%. The performance of the proposed method is demonstrated by extensive simulation studies and an application to construct gray matter brain atrophy network of Huntington's disease by using sMRI data and DTI data. The identified network connections are more consistent with clinical literature and better improve prediction in follow-up clinical outcomes and separate subjects into clinically meaningful subgroups with different prognosis than alternative methods.

Cerebral white matter connectivity, cognition, and age-related macular degeneration

Age-related macular degeneration (AMD) is a common retina disease associated with cognitive impairment in older adults. The mechanism(s) that account for the link between AMD and cognitive decline remain unclear. Here we aim to shed light on this issue by investigating whether relationships between cognition and white matter in the brain differ by AMD status. In a direct group comparison of brain connectometry maps from diffusion weighted images, AMD patients showed significantly weaker quantitative anisotropy (QA) than healthy controls, predominantly in the splenium and left optic radiation. The QA of these tracts, however, did not correlate with the visual acuity measure, indicating that this group effect is not directly driven by visual loss. The AMD and control groups did not differ significantly in cognitive performance.Across all participants, better cognitive performance (e.g. verbal fluency) is associated with stronger connectivity strength in white matter tracts including the splenium and the left inferior fronto-occipital fasciculus/inferior longitudinal fasciculus. However, there were significant interactions between group and cognitive performance (verbal fluency, memory), suggesting that the relation between QA and cognitive performance was weaker in AMD patients than in controls.This may be explained by unmeasured determinants of performance that are more common or impactful in AMD or by a recruitment bias whereby the AMD group had higher cognitive reserve. In general, our findings suggest that neural degeneration in the brain might occur in parallel to AMD in the eyes, although the participants studied here do not (yet) exhibit overt cognitive declines per standard assessments.

Frontostriatal white matter connectivity: age differences and associations with cognition and BOLD modulation

Despite the importance of cortico-striatal circuits to cognition, investigation of age effects on the structural circuitry connecting these regions is limited. The current study examined age effects on frontostriatal white matter connectivity, and identified associations with both executive function performance and dynamic modulation of blood-oxygen-level-dependent (BOLD) activation to task difficulty in a lifespan sample of 169 healthy humans aged 20-94 years. Greater frontostriatal diffusivity was associated with poorer executive function and this negative association strengthened with increasing age. Whole-brain functional magnetic resonance imaging (fMRI) analyses additionally indicated an association between frontostriatal mean diffusivity and BOLD modulation to difficulty selectively in the striatum across 2 independent fMRI tasks. This association was moderated by age, such that younger- and middle-aged individuals showed reduced dynamic range of difficulty modulation as a function of increasing frontostriatal diffusivity. Together these results demonstrate the importance of age-related degradation of frontostriatal circuitry on executive functioning across the lifespan, and highlight the need to capture brain changes occurring in early-to middle-adulthood.

White matter connectivity differences between treatment responders and non-responders in patients with panic disorder

BACKGROUND

Panic disorder (PD) is a prevalent and highly disabling mental condition. However, less is known about relationships between biomarkers that may together predict a better response to pharmacological treatment. The objective of the present study was to compare the brain white matter (WM) connectivity between treatment-responsive patients with panic disorder (RPD) and non-responsive patients with panic disorder (NRPD) after 12 weeks of pharmacotherapy.

METHODS

Sixty-four patients with PD were enrolled in this study (RPD, n = 37; NRPD, n = 27). All patients were examined by using magnetic resonance imaging at 3 Tesla. The Panic Disorder Severity Scale (PDSS), Albany Panic and Phobia Questionnaire (APPQ), Anxiety Sensitivity Inventory-Revised (ASI-R), Beck Anxiety Inventory (BAI), and Beck Depression Inventory (BDI) were administered at baseline of the study. Fractional anisotropy (FA) data were compared using tract-based spatial statistics (TBSS).

RESULTS

TBSS results showed that the FA values of the patients with NRPD were significantly higher than of those with RPD in the WM regions such as the precentral gyrus, parahippocampal gyrus, posterior corona radiata, posterior thalamic radiation, posterior parts of the corpus callosum, and precuneus. Symptom severity scales, such as ASI-R scores, showed significant positive correlations of the FA values with the fronto-temporal WM regions in NRPD.

CONCLUSIONS

These results suggest that structural changes to areas such as the fronto-limbic regions and the posterior part of default mode network, could influence medication response in PD. Further studies with a larger number of patients should be performed to confirm our findings.

White Matter Tract Changes Associated with Clinical Improvement in an Open-Label Trial Assessing Autologous Umbilical Cord Blood for Treatment of Young Children with Autism

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder characterized by social communication deficits and the presence of restricted interests and repetitive behaviors. We have previously reported significant improvements in behavior, including increased social functioning, improved communication abilities, and decreased clinical symptoms in children with ASD, following treatment with a single infusion of autologous cord blood in a phase I open‐label trial. In the current study, we aimed to understand whether these improvements were associated with concurrent changes in brain structural connectivity. Twenty‐five 2‐ to 6‐year‐old children with ASD participated in this trial. Clinical outcome measures included the Vineland Adaptive Behavior Scales‐II Socialization Subscale, Expressive One‐Word Picture Vocabulary Test‐4, and the Clinical Global Impression‐Improvement Scale. Structural connectivity was measured at baseline and at 6 months in a subset of 19 children with 25‐direction diffusion tensor imaging and deterministic tractography. Behavioral improvements were associated with increased white matter connectivity in frontal, temporal, and subcortical regions (hippocampus and basal ganglia) that have been previously shown to show anatomical, connectivity, and functional abnormalities in ASD. The current results suggest that improvements in social communication skills and a reduction in symptoms in children with ASD following treatment with autologous cord blood infusion were associated with increased structural connectivity in brain networks supporting social, communication, and language abilities. stem cells translational medicine2019;8:138&10

Mapping whole brain connectivity changes: The potential impact of different surgical resection approaches for temporal lobe epilepsy

In neurosurgery there are several situations that require transgression of the temporal cortex. For example, a subset of patients with temporal lobe epilepsy require surgical resection (most typically, en-bloc anterior temporal lobectomy). This procedure is the gold standard to alleviate seizures but is associated with chronic cognitive deficits. In recent years there have been multiple attempts to find the optimum balance between minimising the size of resection in order to preserve cognitive function, while still ensuring seizure freedom. Some attempts involve reducing the distance that the resection stretches back from the temporal pole, whilst others try to preserve one or more of the temporal gyri. More recent advanced surgical techniques (selective amygdalo-hippocamptectomies) try to remove the least amount of tissue by going under (sub-temporal), over (trans-Sylvian) or through the temporal lobe (middle-temporal), which have been related to better cognitive outcomes. Previous comparisons of these surgical techniques focus on comparing seizure freedom or behaviour post-surgery, however there have been no systematic studies showing the effect of surgery on white matter connectivity. The main aim of this study, therefore, was to perform systematic 'pseudo-neurosurgery' based on existing resection methods on healthy neuroimaging data and measuring the effect on long-range connectivity. We use anatomical connectivity maps (ACM) to determine long-range disconnection, which is complementary to existing measures of local integrity such as fractional anisotropy or mean diffusivity. ACMs were generated for each diffusion scan in order to compare whole-brain connectivity with an 'ideal resection', nine anterior temporal lobectomy and three selective approaches. For en-bloc resections, as distance from the temporal pole increased, reduction in connectivity was evident within the arcuate fasciculus, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, and the uncinate fasciculus. Increasing the height of resections dorsally reduced connectivity within the uncinate fasciculus. Sub-temporal amygdalohippocampectomy resections were associated with connectivity patterns most similar to the 'ideal' baseline resection, compared to trans-Sylvian and middle-temporal approaches. In conclusion, we showed the utility of ACM in assessing long-range disconnections/disruptions during temporal lobe resections, where we identified the sub-temporal resection as the least disruptive to long-range connectivity which may explain its better cognitive outcome. These results have a direct impact on understanding the amount and/or type of cognitive deficit post-surgery, which may not be obtainable using local measures of white matter integrity.

Impaired white matter connectivity between regions containing mirror neurons, and relationship to negative symptoms and social cognition, in patients with first-episode schizophrenia

In schizophrenia, abnormalities in structural connectivity between brain regions known to contain mirror neurons and their relationship to negative symptoms related to a domain of social cognition are not well understood. Diffusion tensor imaging (DTI) scans were acquired in 16 patients with first episode schizophrenia and 16 matched healthy controls. FA and Trace of the tracts interconnecting regions known to be rich in mirror neurons, i.e., anterior cingulate cortex (ACC), inferior parietal lobe (IPL) and premotor cortex (PMC) were evaluated. A significant group effect for Trace was observed in IPL-PMC white matter fiber tract (F (1, 28) = 7.13, p = .012), as well as in the PMC-ACC white matter fiber tract (F (1, 28) = 4.64, p = .040). There were no group differences in FA. In addition, patients with schizophrenia showed a significant positive correlation between the Trace of the left IPL-PMC white matter fiber tract, and the Ability to Feel Intimacy and Closeness score (rho = .57, p = 0.034), and a negative correlation between the Trace of the left PMC-ACC and the Relationships with Friends and Peers score (rho = remove -.54, p = 0.049). We have demonstrated disrupted white mater microstructure within the white matter tracts subserving brain regions containing mirror neurons. We further showed that such structural disruptions might impact negative symptoms and, more specifically, contribute to the inability to feel intimacy (a measure conceptually related to theory of mind) in first episode schizophrenia. Further studies are needed to understand the potential of our results for diagnosis, prognosis and therapeutic interventions.

Damage to the left uncinate fasciculus is associated with heightened schizotypal traits: A multimodal lesion-mapping study

A growing body of evidence suggests that individuals with pronounced schizotypal traits also display particular neurophysiological and morphological features - notably with regard to left frontotemporal connectivity. However, the studies published to date have focused on subclinical subjects and psychiatric patients, rather than brain-damaged patients. Here, we used the French version of the Schizotypal Personality Questionnaire to assess schizotypal traits in a sample of 97 patients having undergone surgical resection of a diffuse low-grade glioma. Patients having received other neurooncological treatments (including chemotherapy and radiotherapy) were not included. A combination of ROI-based based voxel-wise and tract-wise lesion-symptom mapping and a disconnectome analysis were performed, in order to identify the putative neural network associated with schizotypy. The ROI-based lesion-symptom mapping revealed a significant relationship between the cognitive-perceptual (positive) dimension of schizotypy and the left inferior gyrus (including the pars opercularis and the pars orbitalis). Importantly, we found that disconnection of the left uncinate fasciculus (UF) was a powerful predictor of the positive dimension of schizotypy. Lastly, the disconnection analysis indicated that the positive dimension of schizotypy was significantly associated with the white matter fibres deep in the left orbital and inferior frontal gyri and the left superior temporal pole, which mainly correspond to the spatial topography of the left UF. Taken as a whole, our results suggest that dysconnectivity of the neural network supplied by the left UF is associated with heightened positive schizotypal traits. Our new findings may be of value in interpreting current research in the field of biological psychiatry.

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.

Occipital cortical gyrification reductions associate with decreased functional connectivity in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by progressive muscular weakness and atrophy. Several morphometric studies have been conducted to investigate the gray matter volume or thickness changes in ALS, whereas the cortical folding pattern remains poorly understood. In the present study, we applied a surface-based local gyrification index (LGI) from high resolution MRI data to quantify the cortical folding in matched samples of 25 ALS patients versus 25 healthy controls. Using resting-state fMRI data, we further conducted seed-based functional connectivity analysis to explore the functional correlate of the cortical folding changes. We found that ALS patients had significantly reduced LGI in right occipital cortex and that abnormality in this region associated with decreased functional connectivity in the bilateral precuneus. This set of findings was speculated to result from disturbed white matter connectivity in ALS. In the patient group, we revealed significant negative correlations between disease duration and the LGIs of a cluster in the left superior frontal gyrus, which may reflect the cognitive deterioration in ALS. In summary, our results suggest that LGI may provide a useful means to assess ALS-related neurodegeneration and to study the pathophysiology of ALS.

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.

Sensitivity analysis of human brain structural network construction

Network neuroscience leverages diffusion-weighted magnetic resonance imaging and tractography to quantify structural connectivity of the human brain. However, scientists and practitioners lack a clear understanding of the effects of varying tractography parameters on the constructed structural networks. With diffusion images from the Human Connectome Project (HCP), we characterize how structural networks are impacted by the spatial resolution of brain atlases, total number of tractography streamlines, and grey matter dilation with various graph metrics. We demonstrate how injudicious combinations of highly refined brain parcellations and low numbers of streamlines may inadvertently lead to disconnected network models with isolated nodes. Furthermore, we provide solutions to significantly reduce the likelihood of generating disconnected networks. In addition, for different tractography parameters, we investigate the distributions of values taken by various graph metrics across the population of HCP subjects. Analyzing the ranks of individual subjects within the graph metric distributions, we find that the ranks of individuals are affected differently by atlas scale changes. Our work serves as a guideline for researchers to optimize the selection of tractography parameters and illustrates how biological characteristics of the brain derived in network neuroscience studies can be affected by the choice of atlas parcellation schemes.

Diffusion tractography has been proven to be a promising noninvasive technique to study the network properties of the human brain. However, how various tractography and network construction parameters affect network properties has not been studied using a large cohort of high-quality data. We utilize data provided by the Human Connectome Project to characterize the changes to network properties induced by varying the brain parcellation atlas scales, the number of reconstructed tractography tracks, and the degree of grey matter dilation with graph metrics. We illustrate the importance of increasing the reconstructed track sampling rate when higher atlas scales are used. In addition to changing the raw values of graph metrics, we find that the ranks of individuals relative to the population metric distributions are altered. We further discuss how the dependency of graph metric ranks can affect the brain characteristics derived in group comparison studies using network neuroscience techniques.

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.

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.

Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease, with large variation in survival between patients. Currently, it remains rather difficult to predict survival based on clinical parameters alone. Here, we set out to use clinical characteristics in combination with MRI data to predict survival of ALS patients using deep learning, a machine learning technique highly effective in a broad range of big-data analyses. A group of 135 ALS patients was included from whom high-resolution diffusion-weighted and T1-weighted images were acquired at the first visit to the outpatient clinic. Next, each of the patients was monitored carefully and survival time to death was recorded. Patients were labeled as short, medium or long survivors, based on their recorded time to death as measured from the time of disease onset. In the deep learning procedure, the total group of 135 patients was split into a training set for deep learning (n = 83 patients), a validation set (n = 20) and an independent evaluation set (n = 32) to evaluate the performance of the obtained deep learning networks. Deep learning based on clinical characteristics predicted survival category correctly in 68.8% of the cases. Deep learning based on MRI predicted 62.5% correctly using structural connectivity and 62.5% using brain morphology data. Notably, when we combined the three sources of information, deep learning prediction accuracy increased to 84.4%. Taken together, our findings show the added value of MRI with respect to predicting survival in ALS, demonstrating the advantage of deep learning in disease prognostication.

The pace of vocabulary growth during preschool predicts cortical structure at school age

Children vary greatly in their vocabulary development during preschool years. Importantly, the pace of this early vocabulary growth predicts vocabulary size at school entrance. Despite its importance for later academic success, not much is known about the relation between individual differences in early vocabulary development and later brain structure and function. Here we examined the association between vocabulary growth in children, as estimated from longitudinal measurements from 14 to 58 months, and individual differences in brain structure measured in 3rd and 4th grade (8-10 years old). Our results show that the pace of vocabulary growth uniquely predicts cortical thickness in the left supramarginal gyrus. Probabilistic tractography revealed that this region is directly connected to the inferior frontal gyrus (pars opercularis) and the ventral premotor cortex, via what is most probably the superior longitudinal fasciculus III. Our findings demonstrate, for the first time, the relation between the pace of vocabulary learning in children and a specific change in the structure of the cerebral cortex, specifically, cortical thickness in the left supramarginal gyrus. They also highlight the fact that differences in the pace of vocabulary growth are associated with the dorsal language stream, which is thought to support speech perception and articulation.

Primary and secondary alterations of white matter connectivity in schizophrenia: A study on first-episode and chronic patients using whole-brain tractography-based analysis

Schizophrenia is a debilitating mental disorder that is associated with an impaired connection of cerebral white matter. Studies on patients with chronic and first-episode schizophrenia have found widespread white matter abnormalities. However, it is unclear whether the altered connections are inherent in or secondary to the disease. Here, we sought to identify white matter tracts with altered connections and to distinguish primary or secondary alterations among 74 fiber tracts across the whole brain using an automatic tractography-based analysis method. Thirty-one chronic, 25 first-episode patients with schizophrenia and 31 healthy controls were recruited to receive diffusion spectrum magnetic resonance imaging at 3T. Seven tracts were found to exhibit significant differences between the groups; they included the right arcuate fasciculus, bilateral fornices, left superior longitudinal fasciculus I, and fibers of the corpus callosum to the bilateral dorsolateral prefrontal cortices (DLPFC), bilateral temporal poles, and bilateral hippocampi. Post-hoc between-group analyses revealed that the connection of the callosal fibers to the bilateral DLPFC was significantly decreased in chronic patients but not in first-episode patients. In a stepwise regression analysis, the decline of the tract connection was significantly predicted by the duration of illness. In contrast, the remaining six tracts showed significant alterations in both first-episode and chronic patients and did not associate with clinical variables. In conclusion, reduced white matter connectivity of the callosal fibers to the bilateral DLPFC may be a secondary change that degrades progressively in the chronic stage, whereas alterations in the other six tracts may be inherent in the disease.

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.