Structural Variability Across the Primate Brain: A Cross-Species Comparison.

A large amount of variability exists across human brains; revealed initially on a small scale by postmortem studies and, more recently, on a larger scale with the advent of neuroimaging. Here we compared structural variability between human and macaque monkey brains using grey and white matter magnetic resonance imaging measures. The monkey brain was overall structurally as variable as the human brain, but variability had a distinct distribution pattern, with some key areas showing high variability. We also report the first evidence of a relationship between anatomical variability and evolutionary expansion in the primate brain. This suggests a relationship between variability and stability, where areas of low variability may have evolved less recently and have more stability, while areas of high variability may have evolved more recently and be less similar across individuals. We showed specific differences between the species in key areas, including the amount of hemispheric asymmetry in variability, which was left-lateralized in the human brain across several phylogenetically recent regions. This suggests that cerebral variability may be another useful measure for comparison between species and may add another dimension to our understanding of evolutionary mechanisms.

Rostro-caudal Architecture of the Frontal Lobes in Humans.

The nature of the inputs and outputs of a brain region defines its functional specialization. The frontal portion of the brain is essential for goal-directed behaviors, however, the biological basis for its functional organization is unknown. Here, exploring structural connectomic properties, we delineated 12 frontal areas, defined by the pattern of their white matter connections. This result was highly reproducible across neuroimaging centers, acquisition parameters, and participants. These areas corresponded to regions functionally engaged in specific tasks, organized along a rostro-caudal axis from the most complex high-order association areas to the simplest idiotopic areas. The rostro-caudal axis along which the 12 regions were organized also reflected a gradient of cortical thickness, myelination, and cell body density. Importantly, across the identified regions, this gradient of microstructural features was strongly associated with the varying degree of information processing complexity. These new anatomical signatures shed light onto the structural organization of the frontal lobes and could help strengthen the prediction or diagnosis of neurodevelopmental and neurodegenerative disorders.

Inter-individual differences in audio-motor learning of piano melodies and white matter fiber tract architecture.

Humans vary substantially in their ability to learn new motor skills. Here, we examined inter-individual differences in learning to play the piano, with the goal of identifying relations to structural properties of white matter fiber tracts relevant to audio-motor learning. Non-musicians (n = 18) learned to perform three short melodies on a piano keyboard in a pure audio-motor training condition (vision of their own fingers was occluded). Initial learning times ranged from 17 to 120 min (mean ± SD: 62 ± 29 min). Diffusion-weighted magnetic resonance imaging was used to derive the fractional anisotropy (FA), an index of white matter microstructural arrangement. A correlation analysis revealed that higher FA values were associated with faster learning of piano melodies. These effects were observed in the bilateral corticospinal tracts, bundles of axons relevant for the execution of voluntary movements, and the right superior longitudinal fasciculus, a tract important for audio-motor transformations. These results suggest that the speed with which novel complex audio-motor skills can be acquired may be determined by variability in structural properties of white matter fiber tracts connecting brain areas functionally relevant for audio-motor learning.

Probabilistic tractography recovers a rostrocaudal trajectory of connectivity variability in the human insular cortex.

The insular cortex of macaques has a wide spectrum of anatomical connections whose distribution is related to its heterogeneous cytoarchitecture. Although there is evidence of a similar cytoarchitectural arrangement in humans, the anatomical connectivity of the insula in the human brain has not yet been investigated in vivo. In the present work, we used in vivo probabilistic white-matter tractography and Laplacian eigenmaps (LE) to study the variation of connectivity patterns across insular territories in humans. In each subject and hemisphere, we recovered a rostrocaudal trajectory of connectivity variation ranging from the anterior dorsal and ventral insula to the dorsal caudal part of the long insular gyri. LE suggested that regional transitions among tractography patterns in the insula occur more gradually than in other brain regions. In particular, the change in tractography patterns was more gradual in the insula than in the medial premotor region, where a sharp transition between different tractography patterns was found. The recovered trajectory of connectivity variation in the insula suggests a relation between connectivity and cytoarchitecture in humans resembling that previously found in macaques: tractography seeds from the anterior insula were mainly found in limbic and paralimbic regions and in anterior parts of the inferior frontal gyrus, while seeds from caudal insular territories mostly reached parietal and posterior temporal cortices. Regions in the putative dysgranular insula displayed more heterogeneous connectivity patterns, with regional differences related to the proximity with either putative granular or agranular regions.

Predictive coding during action observation - A depth-resolved intersubject functional correlation study at 7T.

While the brain regions involved in action observation are relatively well documented in humans and primates, how these regions communicate to help understand and predict actions remains poorly understood. Traditional views emphasized a feed-forward architecture in which visual features are organized into increasingly complex representations that feed onto motor programs in parietal and then premotor cortices where the matching of observed actions upon the observer's own motor programs contributes to action understanding. Predictive coding models place less emphasis on feed-forward connections and propose that feed-back connections from premotor regions back to parietal and visual neurons represent predictions about upcoming actions that can supersede visual inputs when actions become predictable, with visual input then merely representing prediction errors. Here we leverage the notion that feed-back connections target specific cortical layers to help adjudicate across these views. Specifically, we test whether observing sequences of hand actions in their natural order, which permits participants to predict upcoming actions, triggers more feed-back input to parietal regions than seeing the same actions in a scrambled sequence that hinders making predictions. Using submillimeter fMRI acquisition at 7T, we find that watching predictable sequences triggers more action-related activity (as measured using intersubject functional correlation) in the parietal cortical area PFt at depths receiving feed-back connections (layers III and V/VI) than watching the exact same actions in scrambled and hence unpredictable sequence. In addition, functional connectivity analysis performed using intersubject functional connectivity confirms that these increased action-related signals in PFt could originate from ventral premotor region BA44. This data showcases the utility of intersubject functional correlation in combination with 7T MRI to explore the architecture of social cognition under more naturalistic conditions, and provides evidence for models that emphasize the importance of feed-back connections in action prediction.

Heuristics for connectivity-based brain parcellation of SMA/pre-SMA through force-directed graph layout.

We propose the use of force-directed graph layout as an explorative tool for connectivity-based brain parcellation studies. The method can be used as a heuristic to find the number of clusters intrinsically present in the data (if any) and to investigate their organisation. It provides an intuitive representation of the structure of the data and facilitates interactive exploration of properties of single seed voxels as well as relations among (groups of) voxels. We validate the method on synthetic data sets and we investigate the changes in connectivity in the supplementary motor cortex, a brain region whose parcellation has been previously investigated via connectivity studies. This region is supposed to present two easily distinguishable connectivity patterns, putatively denoted by SMA (supplementary motor area) and pre-SMA. Our method provides insights with respect to the connectivity patterns of the premotor cortex. These present a substantial variation among subjects, and their subdivision into two well-separated clusters is not always straightforward.

Atypically high influence of subcortical activity on primary sensory regions in autism.

BACKGROUND: Hypersensitivity, stereotyped behaviors and attentional problems in autism spectrum disorder (ASD) are compatible with inefficient filtering of undesired or irrelevant sensory information at early stages of neural processing. This could stem from the persistent overconnectivity between primary sensory regions and deep brain nuclei in both children and adults with ASD - as reported by several previous studies - which could reflect a decreased or arrested maturation of brain connectivity. However, it has not yet been investigated whether this overconnectivity can be modelled as an excessive directional influence of subcortical brain activity on primary sensory cortical regions in ASD, with respect to age-matched typically developing (TD) individuals. METHODS: To this aim, we used dynamic causal modelling to estimate (1) the directional influence of subcortical activity on cortical processing and (2) the functional segregation of primary sensory cortical regions from subcortical activity in 166 participants with ASD and 193 TD participants from the Autism Brain Imaging Data Exchange (ABIDE). We then specifically tested the hypothesis that the age-related changes of these indicators of brain connectivity would differ between the two groups. RESULTS: We found that in TD participants age was significantly associated with decreased influence of subcortical activity on cortical processing, paralleled by an increased functional segregation of cortical sensory processing from subcortical activity. Instead these effects were highly reduced and mostly absent in ASD participants, suggesting a delayed or arrested development of the segregation between subcortical and cortical sensory processing in ASD. CONCLUSION: This atypical configuration of subcortico-cortical connectivity in ASD can result in an excessive amount of unprocessed sensory input relayed to the cortex, which is likely to impact cognitive functioning in everyday situations where it is beneficial to limit the influence of basic sensory information on cognitive processing, such as activities requiring focused attention or social interactions.

White matter correlates of hemi-face dominance in happy and sad expression.

The neural underpinnings of human emotional expression are thought to be unevenly distributed among the two brain hemispheres. However, little is known on the anatomy supporting this claim, particularly in the cerebral white matter. Here, we explored the relationship between hemi-face dominance in emotional expression and cerebral white matter asymmetries in 33 healthy participants. Measures of emotional expression were derived from pictures of the participant's faces in a 'happy smiling' and a 'sad frowning' conditions. Chimeric faces were constructed by mirroring right and left hemi-faces, as done in previous studies, resulting in a left mirrored and right mirrored chimeric face per picture. To gain measures of hemi-face dominance per participant, a jury of 20 additional participants rated which chimeric face shows the higher intensity of emotional expressivity, by marking a 155 mm line between the two versions. Measures of the asymmetry of the uncinate, the cingulum and the three branches of superior longitudinal fasciculi were derived from diffusion-weighted imaging tractography dissections. Group effect analyses indicated that the degree of asymmetry in emotional expression was not as prominent as reported in the literature and showed a large inter-individual variability. The degree of asymmetry in emotional expression was, however, significantly associated with the asymmetries in connective properties of the fronto-temporal and fronto-parietal tracts, specifically the uncinate fasciculus and the first branch of the superior longitudinal fasciculus. Therefore, this result raises novel hypotheses on the relationship of specific white matter tracts and emotional expression, especially their role in mood disorders.

Classifying Autism Spectrum Disorder Using the Temporal Statistics of Resting-State Functional MRI Data With 3D Convolutional Neural Networks.

Resting-state functional magnetic resonance imaging (rs-fMRI) data are 4-dimensional volumes (3-space + 1-time) that have been posited to reflect the underlying mechanisms of information exchange between brain regions, thus making it an attractive modality to develop diagnostic biomarkers of brain dysfunction. The enormous success of deep learning in computer vision has sparked recent interest in applying deep learning in neuroimaging. But the dimensionality of rs-fMRI data is too high (~20 M), making it difficult to meaningfully process the data in its raw form for deep learning experiments. It is currently not clear how the data should be engineered to optimally extract the time information, and whether combining different representations of time could provide better results. In this paper, we explored various transformations that retain the full spatial resolution by summarizing the temporal dimension of the rs-fMRI data, therefore making it possible to train a full three-dimensional convolutional neural network (3D-CNN) even on a moderately sized [~2,000 from Autism Brain Imaging Data Exchange (ABIDE)-I and II] data set. These transformations summarize the activity in each voxel of the rs-fMRI or that of the voxel and its neighbors to a single number. For each brain volume, we calculated regional homogeneity, the amplitude of low-frequency fluctuations, the fractional amplitude of low-frequency fluctuations, degree centrality, eigenvector centrality, local functional connectivity density, entropy, voxel-mirrored homotopic connectivity, and auto-correlation lag. We trained the 3D-CNN on a publically available autism dataset to classify the rs-fMRI images as being from individuals with autism spectrum disorder (ASD) or from healthy controls (CON) at an individual level. We attained results competitive on this task for a combined ABIDE-I and II datasets of ~66%. When all summary measures were combined the result was still only as good as that of the best single measure which was regional homogeneity (ReHo). In addition, we also applied the support vector machine (SVM) algorithm on the same dataset and achieved comparable results, suggesting that 3D-CNNs could not learn additional information from these temporal transformations that were more useful to differentiate ASD from CON.

Group analyses of connectivity-based cortical parcellation using repeated k-means clustering.

K-means clustering has become a popular tool for connectivity-based cortical segmentation using Diffusion Weighted Imaging (DWI) data. A sometimes ignored issue is, however, that the output of the algorithm depends on the initial placement of starting points, and that different sets of starting points therefore could lead to different solutions. In this study we explore this issue. We apply k-means clustering a thousand times to the same DWI dataset collected in 10 individuals to segment two brain regions: the SMA-preSMA on the medial wall, and the insula. At the level of single subjects, we found that in both brain regions, repeatedly applying k-means indeed often leads to a variety of rather different cortical based parcellations. By assessing the similarity and frequency of these different solutions, we show that approximately 256 k-means repetitions are needed to accurately estimate the distribution of possible solutions. Using nonparametric group statistics, we then propose a method to employ the variability of clustering solutions to assess the reliability with which certain voxels can be attributed to a particular cluster. In addition, we show that the proportion of voxels that can be attributed significantly to either cluster in the SMA and preSMA is relatively higher than in the insula and discuss how this difference may relate to differences in the anatomy of these regions.

Advanced lesion symptom mapping analyses and implementation as BCBtoolkit.

Background: Patients with brain lesions provide a unique opportunity to understand the functioning of the human mind. However, even when focal, brain lesions have local and remote effects that impact functionally and structurally connected circuits. Similarly, function emerges from the interaction between brain areas rather than their sole activity. For instance, category fluency requires the associations between executive, semantic, and language production functions. Findings: Here, we provide, for the first time, a set of complementary solutions for measuring the impact of a given lesion on the neuronal circuits. Our methods, which were applied to 37 patients with a focal frontal brain lesions, revealed a large set of directly and indirectly disconnected brain regions that had significantly impacted category fluency performance. The directly disconnected regions corresponded to areas that are classically considered as functionally engaged in verbal fluency and categorization tasks. These regions were also organized into larger directly and indirectly disconnected functional networks, including the left ventral fronto-parietal network, whose cortical thickness correlated with performance on category fluency. Conclusions: The combination of structural and functional connectivity together with cortical thickness estimates reveal the remote effects of brain lesions, provide for the identification of the affected networks, and strengthen our understanding of their relationship with cognitive and behavioral measures. The methods presented are available and freely accessible in the BCBtoolkit as supplementary software [1].

Visual brain plasticity induced by central and peripheral visual field loss.

Disorders that specifically affect central and peripheral vision constitute invaluable models to study how the human brain adapts to visual deafferentation. We explored cortical changes after the loss of central or peripheral vision. Cortical thickness (CoTks) and resting-state cortical entropy (rs-CoEn), as a surrogate for neural and synaptic complexity, were extracted in 12 Stargardt macular dystrophy, 12 retinitis pigmentosa (tunnel vision stage), and 14 normally sighted subjects. When compared to controls, both groups with visual loss exhibited decreased CoTks in dorsal area V3d. Peripheral visual field loss also showed a specific CoTks decrease in early visual cortex and ventral area V4, while central visual field loss in dorsal area V3A. Only central visual field loss exhibited increased CoEn in LO-2 area and FG1. Current results revealed biomarkers of brain plasticity within the dorsal and the ventral visual streams following central and peripheral visual field defects.

Connectivity-based parcellation of the macaque frontal cortex, and its relation with the cytoarchitectonic distribution described in current atlases.

Through its connectivity with the rest of the brain, a cortical region constrains its function. The advent of MRI methods such as diffusion-weighted imaging tractography allows us to estimate whole-brain anatomical connectivity at multiple seed regions in the same subject. This makes it possible to use data-driven techniques to define the spatial boundaries between adjacent brain regions characterized by sharply different connectivity. This approach has recently been employed to identify connectivity-based subdivisions of the human frontal lobe bearing an apparent similarity with cytoarchitectural subdivisions. However, the spatial relationships between the boundaries of cytoarchitectonic areas and tractography-based subdivisions remain largely hypothetical. In this work we present the first tractography-based parcellation of the frontal lobes in macaques. Diffusion-weighted data for tractography were acquired on ex vivo macaque brain specimens, ruling out the presence of various sources of noise present in acquisitions on living subjects. An unsupervised multivariate technique consistently showed the presence of 11 tractography-driven subdivisions in the frontal lobe across specimens. Comparison with several microstructural atlases suggested a heterogeneous relationship of these subdivisions with cytoarchitectonic areas: caudal frontal, medial and orbitofronal subdivisions featured the most consistent relationship between modalities, while lateral prefrontal subdivisions mostly differed from atlas-based cytoarchitectonic subdivisions. Other subdivisions were reminiscent of the organization of anatomical projections of the caudal motor cortex, as well as of the intrinsic orbitofrontal networks. Hence, although some cytoarchitectural and connectivity-based subdivisions share a similar spatial distribution, they should not necessarily be considered as equivalent. Instead, connectivity-based subdivisions appear to provide complementary information on the spatial organization of anatomical connectivity.

Disentangling subgroups of participants recruiting shared as well as different brain regions for the execution of the verb generation task: A data-driven fMRI study.

The spatial pattern of task-related brain activity in fMRI studies might be expected to change according to several variables such as handedness and age. However this spatial heterogeneity might also be due to other unmodeled sources of inter-subject variability. Since group-level results reflect patterns of task-evoked brain activity common to most of the subjects in the sample, they could conceal the presence of subgroups recruiting other brain regions beyond the common pattern. To deal with these issues, data-driven methods can be used to detect the presence of sources of inter-subject variability that might be hard to identify and therefore model a priori. Here we assess the potential of Independent Component Analysis (ICA) to detect the presence of unexpected subgroups of participants. To this end, we acquired task-evoked fMRI data on 45 healthy adults using the verb generation (VGEN) task, in which participants are visually presented with the noun of an object of everyday use, and asked to covertly generate a verb describing the corresponding action. As expected, the task elicited activity in a temporo-parieto-frontal network typically found in previous VGEN experiments. We then quantified the contribution of every subject to nine task-related spatio-temporal processes identified by ICA. A cluster analysis of this quantity yielded three subgroups of participants. Differences between the three identified subgroups were distributed in left and right prefrontal, posterior parietal and extrastriate occipital regions. These results could not be explained by differences in sex, age or handedness across the participants. Furthermore, some regions where a significant difference was found between subgroups were not present in the group-level pattern of task-related activity. We discuss the potential application of this approach for characterizing brain activity in different subgroups of patients with neuropsychiatric or neurological conditions.

Double-dissociation between the mechanism leading to impulsivity and inattention in Attention Deficit Hyperactivity Disorder: A resting-state functional connectivity study.

Two core symptoms characterize Attention Deficit Hyperactivity Disorder (ADHD) subtypes: inattentiveness and hyperactivity-impulsivity. While previous brain imaging research investigated ADHD as if it was a homogenous condition, its two core symptoms may originate from different brain mechanisms. We, therefore, hypothesized that the functional connectivity of cortico-striatal and attentional networks would be different between ADHD subtypes. We studied 165 children (mean age 10.93 years; age range, 7-17 year old) diagnosed as having ADHD based on their revised Conner's rating scale score and 170 typical developing individuals (mean age 11.46 years; age range, 7-17 year old) using resting state functional fMRI. Groups were matched for age, IQ and head motion during the MRI acquisition. We fractionated the ADHD group into predominantly inattentive, hyperactive-impulsive and combined subtypes based on their revised Conner's rating scale score. We then analyzed differences in resting state functional connectivity of the cortico-striatal and attentional networks between these subtypes. We found a double dissociation of functional connectivity in the cortico-striatal and ventral attentional networks, reflecting the subtypes of the ADHD participants. Particularly, the hyperactive-impulsive subtype was associated with increased connectivity in cortico-striatal network, whereas the inattentive subtype was associated with increased connectivity in the right ventral attention network. Our study demonstrated for the first time a right lateralized, double dissociation between specific networks associated with hyperactivity-impulsivity and inattentiveness in ADHD children, providing a biological basis for exploring symptom dimensions and revealing potential targets for more personalized treatments.

Diffusion tensor imaging in euthymic bipolar disorder - A tract-based spatial statistics study.

BACKGROUND: In the current DTI study we compared euthymic bipolar I disorder (BD-I) patients and healthy controls (HC). We subsequently divided the total patient group into lithium-users and non-lithium-users and estimated differences across the three groups. METHODS: Twenty-one euthymic BD-I patients and twenty-two HC participants were included in psychiatric interviews and MRI image acquisition (diffusion-weighted (DW) and T1-weighted scans). Fractional anisotropy (FA), radial, mean and axial diffusivity (RD, MD, AD) were estimated from the DW data, using DTI. These measures were then compared between groups using FSL Tract Based Spatial Statistics (TBSS). Correlations with age at onset, number of episodes and depression score were analyzed. RESULTS: A difference in FA, MD, RD and AD between the whole sample of euthymic BD-I patients and healthy controls could not be detected. Amongst others, lithium-using patients demonstrated a higher FA and lower RD when compared to non-lithium-using BD-I patients in the corpus callosum and left anterior corona radiata. Widespread clusters demonstrated negative FA associations and positive RD and MD associations with minor depressive symptoms. LIMITATIONS: Patients were naturalistically treated. Although the sample size is comparable to several other DTI studies, a larger sample size would have been benificial. TBSS and DTI have their own limitations. CONCLUSION: Our findings support the theory that previously described DTI-based microstructural differences between HC and BD patients could be less pronounced in euthymic BD patients. Differences in FA between patients using and not using lithium suggest a counteracting effect of lithium on white matter microstructural disturbances.

A new myeloarchitectonic map of the human neocortex based on data from the Vogt-Vogt school.

The human cerebral cortex contains numerous myelinated fibres, the arrangement and density of which is by no means homogeneous throughout the cortex. Local differences in the spatial organization of these fibres render it possible to recognize areas with a different myeloarchitecture. The neuroanatomical subdiscipline aimed at the identification and delineation of such areas is known as myeloarchitectonics. During the period extending from 1910 to 1970, Oscar and Cécile Vogt and their numerous collaborators (The Vogt-Vogt school) published a large number of myeloarchitectonic studies on the cortex of the various lobes of the human cerebrum. Recently, one of us (Nieuwenhuys in Brain Struct Funct 218: 303-352, 2013) extensively reviewed these studies. It was concluded that the data available are adequate and sufficient for the composition of a myeloarchitectonic map of the entire human neocortex. The present paper is devoted to the creation of this map. Because the data provided by the Vogt-Vogt school are derived from many different brains, a standard brain had to be introduced to which all data available could be transferred. As such, the colin27 structural scan, aligned to the MNI305 template was selected. The procedure employed in this transfer involved computer-aided transformations of the lobar maps available in the literature, to the corresponding regions of the standard brain, as well as local adjustments in the border zones of the various lobes. The resultant map includes 180 myeloarchitectonic areas, 64 frontal, 30 parietal, 6 insular, 17 occipital and 63 temporal. The designation of the various areas with simple Arabic numbers, introduced by Oscar Vogt for the frontal and parietal cortices, has been extended over the entire neocortex. It may be expected that combination of the myeloarchitectonic data of the Vogt-Vogt school, as expressed in our map, with the results of the detailed cytoarchitectonic and receptor architectonic studies of Karl Zilles and Katrin Amunts and their numerous associates, will yield a comprehensive 'supermap' of the structural organization of the human neocortex. For the time being, i. e., as long as this 'supermap' is not yet available, our map may provide a tentative frame of reference for (a) the morphological interpretation of the results of functional neuroimaging studies; (b) the selection of starting points (seed voxels, regions-of-interest) in diffusion tractography studies and

Increased Functional Connectivity Between Subcortical and Cortical Resting-State Networks in Autism Spectrum Disorder.

IMPORTANCE: Individuals with autism spectrum disorder (ASD) exhibit severe difficulties in social interaction, motor coordination, behavioral flexibility, and atypical sensory processing, with considerable interindividual variability. This heterogeneous set of symptoms recently led to investigating the presence of abnormalities in the interaction across large-scale brain networks. To date, studies have focused either on constrained sets of brain regions or whole-brain analysis, rather than focusing on the interaction between brain networks. OBJECTIVES: To compare the intrinsic functional connectivity between brain networks in a large sample of individuals with ASD and typically developing control subjects and to estimate to what extent group differences would predict autistic traits and reflect different developmental trajectories. DESIGN, SETTING, AND PARTICIPANTS: We studied 166 male individuals (mean age, 17.6 years; age range, 7-50 years) diagnosed as having DSM-IV-TR autism or Asperger syndrome and 193 typical developing male individuals (mean age, 16.9 years; age range, 6.5-39.4 years) using resting-state functional magnetic resonance imaging (MRI). Participants were matched for age, IQ, head motion, and eye status (open or closed) in the MRI scanner. We analyzed data from the Autism Brain Imaging Data Exchange (ABIDE), an aggregated MRI data set from 17 centers, made public in August 2012. MAIN OUTCOMES AND MEASURES: We estimated correlations between time courses of brain networks extracted using a data-driven method (independent component analysis). Subsequently, we associated estimates of interaction strength between networks with age and autistic traits indexed by the Social Responsiveness Scale. RESULTS: Relative to typically developing control participants, individuals with ASD showed increased functional connectivity between primary sensory networks and subcortical networks (thalamus and basal ganglia) (all t ≥ 3.13, P < .001 corrected). The strength of such connections was associated with the severity of autistic traits in the ASD group (all r ≥ 0.21, P < .0067 corrected). In addition, subcortico-cortical interaction decreased with age in the entire sample (all r ≤ -0.09, P < .012 corrected), although this association was significant only in typically developing participants (all r ≤ -0.13, P < .009 corrected). CONCLUSIONS AND RELEVANCE: Our results showing ASD-related impairment in the interaction between primary sensory cortices and subcortical regions suggest that the sensory processes they subserve abnormally influence brain information processing in individuals with ASD. This might contribute to the occurrence of hyposensitivity or hypersensitivity and of difficulties in top-down regulation of behavior.

Not on speaking terms: hallucinations and structural network disconnectivity in schizophrenia.

Auditory verbal hallucinations (AVH) in schizophrenia have previously been associated with functional deficiencies in language networks, specifically with functional disconnectivity in fronto-temporal connections in the left hemisphere and in interhemispheric connections between frontal regions. Here, we investigate whether AVH are accompanied by white matter abnormalities in tracts connecting the frontal, parietal and temporal lobes, also engaged during language tasks. We combined diffusion tensor imaging with tract-based spatial statistics and found white matter abnormalities in patients with schizophrenia as compared with healthy controls. The patients showed reduced fractional anisotropy bilaterally: in the anterior thalamic radiation (ATR), body of the corpus callosum (forceps minor), cingulum, temporal part of the superior longitudinal fasciculus (SLF) and a small area in the inferior fronto-occipital fasciculus (IFOF); and in the right hemisphere: in the visual cortex, forceps major, body of the corpus callosum (posterior parts) and inferior parietal cortex. Compared to patients without current hallucinations, patients with hallucinations revealed decreased fractional anisotropy in the left IFOF, uncinate fasciculus, arcuate fasciculus with SLF, corpus callosum (posterior parts-forceps major), cingulate, corticospinal tract and ATR. The severity of hallucinations correlated negatively with white matter integrity in tracts connecting the left frontal lobe with temporal regions (uncinate fasciculus, IFOF, cingulum, arcuate fasciculus anterior and long part and superior long fasciculus frontal part) and in interhemispheric connections (anterior corona radiata). These findings support the hypothesis that hallucinations in schizophrenia are accompanied by a complex pattern of white matter alterations that negatively affect the language, emotion and attention/perception networks.

Pathophysiological concepts in mild traumatic brain injury: diffusion tensor imaging related to acute perfusion CT imaging.

BACKGROUND: A subgroup of patients with mild traumatic brain injury (TBI) experiences residual symptoms interfering with their return to work. The pathophysiological substrate of the suboptimal outcome in these patients is a source of debate. OBJECTIVE: To provide greater insight into the pathophysiological mechanisms of mild TBI. METHODS: Diffusion tensor imaging (DTI) was performed during follow-up of 18 patients with mild TBI and compared with healthy control subjects. DTI data of the patient group were also compared with perfusion CT imaging in the acute phase of injury. RESULTS: In patients with mild TBI, a trend was observed for a decreased fractional anisotropy (FA) in widespread bilateral frontal white matter areas with increased mean diffusivity (MD) in the parieto-temporal regions, compared to healthy control subjects. Cerebral blood volume (CBV) correlated significantly with FA in several white matter tracts including the corpus callosum, the internal capsule, the inferior fronto-occipital fascicle, the corticospinal tract, the superior and the inferior longitudinal fascicle. CONCLUSION: In mild TBI with normal conventional imaging significant associations between cerebral perfusion in the acute phase of injury and DTI analyses in the chronic phase of injury were discerned. The pathophysiological concept of these findings is being outlined.