Local structural connectivity is associated with social cognition in autism spectrum disorder.

The current theory implying local, short-range overconnectivity in autism spectrum disorder, contrasting with long-range underconnectivity, is based on heterogeneous results, on limited data involving functional connectivity studies, on heterogeneous paediatric populations and non-specific methodologies. In this work, we studied short-distance structural connectivity in a homogeneous population of males with high-functioning autism spectrum disorder and used a novel methodology specifically suited for assessing U-shaped short-distance tracts, including a recently developed tractography-based atlas of the superficial white matter fibres. We acquired diffusion-weighted MRI for 58 males (27 subjects with high-functioning autism spectrum disorder and 31 control subjects) and extracted the mean generalized fractional anisotropy of 63 short-distance tracts. Neuropsychological evaluation included Wechsler Adult Intelligence Scale IV (WAIS-IV), Communication Checklist-Adult, Empathy Quotient, Social Responsiveness Scale and Behaviour Rating Inventory of Executive Function-Adult (BRIEF-A). In contradiction with the models of short-range over-connectivity in autism spectrum disorder, we found that patients with autism spectrum disorder had a significantly decreased anatomical connectivity in a component comprising 13 short tracts compared to controls. Specific short-tract atypicalities in temporal lobe and insula were significantly associated with clinical manifestations of autism spectrum disorder such as social awareness, language structure, pragmatic skills and empathy, emphasizing their importance in social dysfunction. Short-range decreased anatomical connectivity may thus be an important substrate of social deficits in autism spectrum disorder, in contrast with current models.

Shape analysis of the cingulum, uncinate and arcuate fasciculi in patients with bipolar disorder.

BACKGROUND: Abnormal maturation of brain connectivity is supposed to underlie the dysfunctional emotion regulation in patients with bipolar disorder (BD). To test this hypothesis, white matter integrity is usually investigated using measures of water diffusivity provided by MRI. Here we consider a more intuitive aspect of the morphometry of the white matter tracts: the shape of the fibre bundles, which is associated with neurodevelopment. We analyzed the shape of 3 tracts involved in BD: the cingulum (CG), uncinate fasciculus (UF) and arcuate fasciculus (AF). METHODS: We analyzed diffusion MRI data in patients with BD and healthy controls. The fibre bundles were reconstructed using Q-ball-based tractography and automated segmentation. Using Isomap, a manifold learning method, the differences in the shape of the reconstructed bundles were visualized and quantified. RESULTS: We included 112 patients and 82 controls in our analysis. We found the left AF of patients to be further extended toward the temporal pole, forming a tighter hook than in controls. We found no significant difference in terms of shape for the left UF, the left CG or the 3 right fasciculi. However, in patients compared with controls, the ventrolateral branch of the left UF in the orbitofrontal region had a tendency to be larger, and the left CG of patients had a tendency to be smaller in the frontal lobe and larger in the parietal lobe. LIMITATIONS: This was a cross-sectional study. CONCLUSION: Our results suggest neurodevelopmental abnormalities in the left AF in patients with BD. The statistical tendencies observed for the left UF and left CG deserve further study.

Clustering of Whole-Brain White Matter Short Association Bundles Using HARDI Data.

Human brain connectivity is extremely complex and variable across subjects. While long association and projection bundles are stable and have been deeply studied, short association bundles present higher intersubject variability, and few studies have been carried out to adequately describe the structure, shape, and reproducibility of these bundles. However, their analysis is crucial to understand brain function and better characterize the human connectome. In this study, we propose an automatic method to identify reproducible short association bundles of the superficial white matter, based on intersubject hierarchical clustering. The method is applied to the whole brain and finds representative clusters of similar fibers belonging to a group of subjects, according to a distance metric between fibers. We experimented with both affine and non-linear registrations and, due to better reproducibility, chose the results obtained from non-linear registration. Once the clusters are calculated, our method performs automatic labeling of the most stable connections based on individual cortical parcellations. We compare results between two independent groups of subjects from a HARDI database to generate reproducible connections for the creation of an atlas. To perform a better validation of the results, we used a bagging strategy that uses pairs of groups of 27 subjects from a database of 74 subjects. The result is an atlas with 44 bundles in the left hemisphere and 49 in the right hemisphere, of which 33 bundles are found in both hemispheres. Finally, we use the atlas to automatically segment 78 new subjects from a different HARDI database and to analyze stability and lateralization results.

Fast Automatic Segmentation of White Matter Streamlines Based on a Multi-Subject Bundle Atlas.

This paper presents an algorithm for fast segmentation of white matter bundles from massive dMRI tractography datasets using a multisubject atlas. We use a distance metric to compare streamlines in a subject dataset to labeled centroids in the atlas, and label them using a per-bundle configurable threshold. In order to reduce segmentation time, the algorithm first preprocesses the data using a simplified distance metric to rapidly discard candidate streamlines in multiple stages, while guaranteeing that no false negatives are produced. The smaller set of remaining streamlines is then segmented using the original metric, thus eliminating any false positives from the preprocessing stage. As a result, a single-thread implementation of the algorithm can segment a dataset of almost 9 million streamlines in less than 6 minutes. Moreover, parallel versions of our algorithm for multicore processors and graphics processing units further reduce the segmentation time to less than 22 seconds and to 5 seconds, respectively. This performance enables the use of the algorithm in truly interactive applications for visualization, analysis, and segmentation of large white matter tractography datasets.

Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography.

Human brain connection map is far from being complete. In particular the study of the superficial white matter (SWM) is an unachieved task. Its description is essential for the understanding of human brain function and the study of pathogenesis triggered by abnormal connectivity. In this work we automatically created a multi-subject atlas of SWM diffusion-based bundles of the whole brain. For each subject, the complete cortico-cortical tractogram is first split into sub-tractograms connecting pairs of gyri. Then intra-subject shape-based fiber clustering performs compression of each sub-tractogram into a set of bundles. Proceeding further with shape-based clustering provides a match of the bundles across subjects. Bundles found in most of the subjects are instantiated in the atlas. To increase robustness, this procedure was performed with two independent groups of subjects, in order to discard bundles without match across the two independent atlases. Finally, the resulting intersection atlas was projected on a third independent group of subjects in order to filter out bundles without reproducible and reliable projection. The final multi-subject diffusion-based U-fiber atlas is composed of 100 bundles in total, 50 per hemisphere, from which 35 are common to both hemispheres.

Childhood trauma and the limbic network: a multimodal MRI study in patients with bipolar disorder and controls.

BACKGROUND: Childhood trauma (CT) is a major risk factor for psychiatric conditions. It is hypothesized that CT effects are mediated by the limbic system. Few multimodal neuroimaging studies allow an integrated perspective of this impact. Our goal was thus to study the effects of CT on the limbic network. METHODS: We acquired multimodal MRI (T1, diffusion weighted, and resting state fMRI) data from 79 subjects (47 healthy controls and 32 patients with bipolar disorder, BD). We performed correlational analyses between Childhood Trauma Questionnaire (sub)scores (physical and emotional abuse/neglect and sexual abuse) and anatomo-functional measurements of the limbic network (hippocampal and amygdala volumes, prefronto-limbic functional connectivity, uncinate fractional anisotropy). RESULTS: We found CTQ total scores to be negatively correlated with amygdala volume, prefronto-limbic functional connectivity (FC) and uncinate fractional anisotropy in our sample. Considering subscores, neglects (physical and emotional) were the only to affect neural parameters. The patients with BD drove most of the results. LIMITATIONS: Small sample size and low level of trauma in controls. CONCLUSIONS: Our multimodal approach enabled an integrated view of the long-term effects of CT on the limbic system.

Dysfunctional decision-making related to white matter alterations in bipolar I disorder.

OBJECTIVE: This study investigated how frontal white matter (WM) alterations in patients with bipolar I disorder (BD-I) are linked to motivational dysregulation, often reported in the form of risk-taking and impulsivity, and whether structure-function relations in patients might differ from healthy subjects (HC). METHOD: We acquired diffusion data from 24 euthymic BD-I patients and 24 controls, to evaluate WM integrity of selected frontal tracts. Risk-taking was assessed by the Cambridge Gambling Task and impulsivity by self-report with the Barratt-Impulsiveness Scale. RESULTS: BD-I patients displayed significantly lower integrity in the right cingulum compared to HC. They also showed more risk-taking behavior and reported increased trait-impulsivity. Risk-taking was negatively associated with WM integrity in the right cingulum. Impulsivity was not related to WM integrity in investigated tracts. Together with age and sex, FA in the cingulum explained 25% of variance in risk-taking scores in all study participants. The left inferior fronto-occipital fasciculus (IFOF) was specifically predictive of risk-taking behavior in BD-I patients, but not in HC. LIMITATIONS: The employed parameters did not allow us to specify the exact origin of WM changes, nor did the method allow the analysis of specific brain subregions. Also, sample size was moderate and the sample included patients with lifetime alcohol dependence/abuse, hence effects found need replication and have to be interpreted with caution. CONCLUSION: Our results further strengthen recent models linking structural changes in frontal networks to behavioral markers of BD-I. They extend recent findings by showing that risk-taking is also linked to the cingulum in BD-I and HC, while other prefrontal tracts (IFOF) are specifically implicated in risk-taking behavior in BD-I patients. Meanwhile, self-reported impulsivity was not associated with WM integrity of the tracts investigated in our study.

Organising white matter in a brain without corpus callosum fibres.

Isolated corpus callosum dysgenesis (CCD) is a congenital malformation which occurs during early development of the brain. In this study, we aimed to identify and describe its consequences beyond the lack of callosal fibres, on the morphology, microstructure and asymmetries of the main white matter bundles with diffusion imaging and fibre tractography. Seven children aged between 9 and 13 years old and seven age- and gender-matched control children were studied. First, we focused on bundles within the mesial region of the cerebral hemispheres: the corpus callosum, Probst bundles and cingulum which were selected using a conventional region-based approach. We demonstrated that the Probst bundles have a wider connectivity than the previously described rostrocaudal direction, and a microstructure rather distinct from the cingulum but relatively close to callosal remnant fibres. A sigmoid bundle was found in two partial ageneses. Second, the corticospinal tract, thalamic radiations and association bundles were extracted automatically via an atlas of adult white matter bundles to overcome bias resulting from a priori knowledge of the bundles' anatomical morphology and trajectory. Despite the lack of callosal fibres and the colpocephaly observed in CCD, all major white matter bundles were identified with a relatively normal morphology, and preserved microstructure (i.e. fractional anisotropy, mean diffusivity) and asymmetries. Consequently the bundles' organisation seems well conserved in brains with CCD. These results await further investigations with functional imaging before apprehending the cognition variability in children with isolated dysgenesis.

A multicenter tractography study of deep white matter tracts in bipolar I disorder: psychotic features and interhemispheric disconnectivity.

IMPORTANCE: Tractography studies investigating white matter (WM) abnormalities in patients with bipolar disorder have yielded heterogeneous results owing to small sample sizes. The small size limits their generalizability, a critical issue for neuroimaging studies of biomarkers of bipolar I disorder (BPI). OBJECTIVES: To study WM abnormalities using whole-brain tractography in a large international multicenter sample of BPI patients and to compare these alterations between patients with or without a history of psychotic features during mood episodes. DESIGN, SETTING, AND PARTICIPANTS: A cross-sectional, multicenter, international, Q-ball imaging tractography study comparing 118 BPI patients and 86 healthy control individuals. In addition, among the patient group, we compared those with and without a history of psychotic features. University hospitals in France, Germany, and the United States contributed participants. INTERVENTIONS: Participants underwent assessment using the Diagnostic Interview for Genetic Studies at the French sites or the Structured Clinical Interview for DSM-IV at the German and US sites. Diffusion-weighted magnetic resonance images were acquired using the same acquisition parameters and scanning hardware at each site. We reconstructed 22 known deep WM tracts using Q-ball imaging tractography and an automatized segmentation technique. MAIN OUTCOMES AND MEASURES: Generalized fractional anisotropy values along each reconstructed WM tract. RESULTS: Compared with controls, BPI patients had significant reductions in mean generalized fractional anisotropy values along the body and the splenium of the corpus callosum, the left cingulum, and the anterior part of the left arcuate fasciculus when controlling for age, sex, and acquisition site (corrected for multiple testing). Patients with a history of psychotic features had a lower mean generalized fractional anisotropy value than those without along the body of the corpus callosum (corrected for multiple testing). CONCLUSIONS AND RELEVANCE: In this multicenter sample, BPI patients had reduced WM integrity in interhemispheric, limbic, and arcuate WM tracts. Interhemispheric pathways are more disrupted in patients with than in those without psychotic symptoms. Together these results highlight the existence of an anatomic disconnectivity in BPI and further underscore a role for interhemispheric disconnectivity in the pathophysiological features of psychosis in BPI.

Interactive segmentation of white-matter fibers using a multi-subject atlas.

We present a fast algorithm for automatic segmentation of white matter fibers from tractography datasets based on a multi-subject bundle atlas. We describe a sequential version of the algorithm that runs on a desktop computer CPU, as well as a highly parallel version that uses a Graphics Processing Unit (GPU) as an accelerator. Our sequential implementation runs 270 times faster than a C++/Python implementation of a previous algorithm based on the same segmentation method, and 21 times faster than a highly optimized C version of the same previous algorithm. Our parallelized implementation exploits the multiple computation units and memory hierarchy of the GPU to further speed up the algorithm by a factor of 30 with respect to our sequential code. As a result, the time to segment a subject dataset of 800,000 fibers is reduced from more than 2.5 hours in the Python/C++ code, to less than one second in the GPU version.

Study of the variability of short association bundles on a HARDI database.

The construction of an atlas of the human brain connectome, in particular, the cartography of fiber bundles of superficial white matter (SWM) is a complex and unachieved task. Its description is essential for the understanding of human brain function and the study of several pathologies. In this work we applied an automatic white matter bundle segmentation method proposed in the literature for the analysis of the variability of a big amount of superficial white matter bundles. The method was applied to 30 subjects of a high quality HARDI database, adding several processing steps in order to improve the results. Then we calculated some indices for studying the variability of 40 SWM fiber bundles from each hemisphere, and we constructed a model of these bundles in the MNI standard space.

GPU-based acceleration of an automatic white matter segmentation algorithm using CUDA.

This paper presents a parallel implementation of an algorithm for automatic segmentation of white matter fibers from tractography data. We execute the algorithm in parallel using a high-end video card with a Graphics Processing Unit (GPU) as a computation accelerator, using the CUDA language. By exploiting the parallelism and the properties of the memory hierarchy available on the GPU, we obtain a speedup in execution time of 33.6 with respect to an optimized sequential version of the algorithm written in C, and of 240 with respect to the original Python/C++ implementation. The execution time is reduced from more than two hours to only 35 seconds for a subject dataset of 800,000 fibers, thus enabling applications that use interactive segmentation and visualization of small to medium-sized tractography datasets.

The CONNECT project: Combining macro- and micro-structure.

In recent years, diffusion MRI has become an extremely important tool for studying the morphology of living brain tissue, as it provides unique insights into both its macrostructure and microstructure. Recent applications of diffusion MRI aimed to characterize the structural connectome using tractography to infer connectivity between brain regions. In parallel to the development of tractography, additional diffusion MRI based frameworks (CHARMED, AxCaliber, ActiveAx) were developed enabling the extraction of a multitude of micro-structural parameters (axon diameter distribution, mean axonal diameter and axonal density). This unique insight into both tissue microstructure and connectivity has enormous potential value in understanding the structure and organization of the brain as well as providing unique insights to abnormalities that underpin disease states. The CONNECT (Consortium Of Neuroimagers for the Non-invasive Exploration of brain Connectivity and Tracts) project aimed to combine tractography and micro-structural measures of the living human brain in order to obtain a better estimate of the connectome, while also striving to extend validation of these measurements. This paper summarizes the project and describes the perspective of using micro-structural measures to study the connectome.