Neuroanatomical differences between first-episode psychosis patients with and without neurocognitive deficit: a 3-year longitudinal study

This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY).[Background]: The course of cognitive function in first-episode psychosis (FEP) patients suggests that some individuals are normal or near normal whereas some cases present a marked decline. The goal of the present longitudinal study was to identify neuroanatomical differences between deficit and non-deficit patients. [Methods]: Fifty nine FEP patients with neuroimage and neurocognitive information were studied at baseline and 3 year after illness onset. A global cognitive function score was used to classify deficit and non-deficit patients at baseline. Analysis of covariances and repeated-measures analysis were performed to evaluate differences in brain volumes. Age, premorbid IQ, and intracranial volume were used as covariates. We examined only volumes of whole brain, whole brain gray and white matter, cortical CSF and lateral ventricles, lobular volumes of gray and white matter, and subcortical (caudate nucleus and thalamus) regions. [Results]: At illness onset 50.8% of patients presented global cognitive deficit. There were no significant differences between neuropsychological subgroups in any of the brain regions studied at baseline [all F(1, 54) ≤ 3.42; all p ≥ 0.07] and follow-up [all F(1, 54) ≤ 3.43; all p ≥ 0.07] time points. There was a significant time by group interaction for the parietal tissue volume [F(1, 54) = 4.97, p = 0.030] and the total gray matter volume [F(1, 54) = 4.31, p = 0.042], with the deficit group showing a greater volume decrease. [Conclusion]: Our results did not confirm the presence of significant morphometric differences in the brain regions evaluated between cognitively impaired and cognitively preserved schizophrenia patients at the early stages of the illness. However, there were significant time by group interactions for the parietal tissue volume and the total gray matter volume during the 3-year follow-up period, which might indicate that cognitive deficit in schizophrenia would be associated with progressive brain volume loss.The present study was carried out at the Hospital Marqués de Valdecilla, University of Cantabria, Santander, Spain, under the following grant support: Instituto de Salud Carlos III PI020499, PI050427, PI060507, PI1000183, SENY Fundació Research Grant CI 2005-0308007, and Fundación Marqués de Valdecilla API07/011. We wish to thank the PAFIP researchers. Adele Ferro was sustained by the funds of the 2007/2013 European Social Fund Operational Programme of the Autonomous Region Friuli Venezia Giulia.Peer Reviewe

Diverging volumetric trajectories following pediatric traumatic brain injury.

Traumatic brain injury (TBI) is a significant public health concern, and can be especially disruptive in children, derailing on-going neuronal maturation in periods critical for cognitive development. There is considerable heterogeneity in post-injury outcomes, only partially explained by injury severity. Understanding the time course of recovery, and what factors may delay or promote recovery, will aid clinicians in decision-making and provide avenues for future mechanism-based therapeutics. We examined regional changes in brain volume in a pediatric/adolescent moderate-severe TBI (msTBI) cohort, assessed at two time points. Children were first assessed 2-5 months post-injury, and again 12 months later. We used tensor-based morphometry (TBM) to localize longitudinal volume expansion and reduction. We studied 21 msTBI patients (5 F, 8-18 years old) and 26 well-matched healthy control children, also assessed twice over the same interval. In a prior paper, we identified a subgroup of msTBI patients, based on interhemispheric transfer time (IHTT), with significant structural disruption of the white matter (WM) at 2-5 months post injury. We investigated how this subgroup (TBI-slow, N = 11) differed in longitudinal regional volume changes from msTBI patients (TBI-normal, N = 10) with normal WM structure and function. The TBI-slow group had longitudinal decreases in brain volume in several WM clusters, including the corpus callosum and hypothalamus, while the TBI-normal group showed increased volume in WM areas. Our results show prolonged atrophy of the WM over the first 18 months post-injury in the TBI-slow group. The TBI-normal group shows a different pattern that could indicate a return to a healthy trajectory

Functional specialization within rostral prefrontal cortex (Area 10): a meta-analysis

One of the least well understood regions of the human brain is rostral prefrontal cortex, approximating Brodmann's area 10. Here, we investigate the possibility that there are functional subdivisions within this region by conducting a meta-analysis of 104 functional neuroimaging studies (using positron emission tomography/functional magnetic resonance imaging). Studies involving working memory and episodic memory retrieval were disproportionately associated with lateral activations, whereas studies involving mentalizing (i.e., attending to one's own emotions and mental states or those of other agents) were disproportionately associated with medial activations. Functional variation was also observed along a rostral-caudal axis, with studies involving mentalizing yielding relatively caudal activations and studies involving multiple-task coordination yielding relatively rostral activations. A classification algorithm was trained to predict the task, given the coordinates of each activation peak. Performance was well above chance levels (74% for the three most common tasks; 45% across all eight tasks investigated) and generalized to data not included in the training set. These results point to considerable functional segregation within rostral prefrontal cortex

Regional gray matter volumetric changes in autism associated with social and repetitive behavior symptoms.

BackgroundAlthough differences in brain anatomy in autism have been difficult to replicate using manual tracing methods, automated whole brain analyses have begun to find consistent differences in regions of the brain associated with the social cognitive processes that are often impaired in autism. We attempted to replicate these whole brain studies and to correlate regional volume changes with several autism symptom measures.MethodsWe performed MRI scans on 24 individuals diagnosed with DSM-IV autistic disorder and compared those to scans from 23 healthy comparison subjects matched on age. All participants were male. Whole brain, voxel-wise analyses of regional gray matter volume were conducted using voxel-based morphometry (VBM).ResultsControlling for age and total gray matter volume, the volumes of the medial frontal gyri, left pre-central gyrus, right post-central gyrus, right fusiform gyrus, caudate nuclei and the left hippocampus were larger in the autism group relative to controls. Regions exhibiting smaller volumes in the autism group were observed exclusively in the cerebellum. Significant partial correlations were found between the volumes of the caudate nuclei, multiple frontal and temporal regions, the cerebellum and a measure of repetitive behaviors, controlling for total gray matter volume. Social and communication deficits in autism were also associated with caudate, cerebellar, and precuneus volumes, as well as with frontal and temporal lobe regional volumes.ConclusionGray matter enlargement was observed in areas that have been functionally identified as important in social-cognitive processes, such as the medial frontal gyri, sensorimotor cortex and middle temporal gyrus. Additionally, we have shown that VBM is sensitive to associations between social and repetitive behaviors and regional brain volumes in autism

Improving language mapping in clinical fMRI through assessment of grammar.

IntroductionBrain surgery in the language dominant hemisphere remains challenging due to unintended post-surgical language deficits, despite using pre-surgical functional magnetic resonance (fMRI) and intraoperative cortical stimulation. Moreover, patients are often recommended not to undergo surgery if the accompanying risk to language appears to be too high. While standard fMRI language mapping protocols may have relatively good predictive value at the group level, they remain sub-optimal on an individual level. The standard tests used typically assess lexico-semantic aspects of language, and they do not accurately reflect the complexity of language either in comprehension or production at the sentence level. Among patients who had left hemisphere language dominance we assessed which tests are best at activating language areas in the brain.MethodWe compared grammar tests (items testing word order in actives and passives, wh-subject and object questions, relativized subject and object clauses and past tense marking) with standard tests (object naming, auditory and visual responsive naming), using pre-operative fMRI. Twenty-five surgical candidates (13 females) participated in this study. Sixteen patients presented with a brain tumor, and nine with epilepsy. All participants underwent two pre-operative fMRI protocols: one including CYCLE-N grammar tests (items testing word order in actives and passives, wh-subject and object questions, relativized subject and object clauses and past tense marking); and a second one with standard fMRI tests (object naming, auditory and visual responsive naming). fMRI activations during performance in both protocols were compared at the group level, as well as in individual candidates.ResultsThe grammar tests generated more volume of activation in the left hemisphere (left/right angular gyrus, right anterior/posterior superior temporal gyrus) and identified additional language regions not shown by the standard tests (e.g., left anterior/posterior supramarginal gyrus). The standard tests produced more activation in left BA 47. Ten participants had more robust activations in the left hemisphere in the grammar tests and two in the standard tests. The grammar tests also elicited substantial activations in the right hemisphere and thus turned out to be superior at identifying both right and left hemisphere contribution to language processing.ConclusionThe grammar tests may be an important addition to the standard pre-operative fMRI testing

Fuzzy Fibers: Uncertainty in dMRI Tractography

Fiber tracking based on diffusion weighted Magnetic Resonance Imaging (dMRI) allows for noninvasive reconstruction of fiber bundles in the human brain. In this chapter, we discuss sources of error and uncertainty in this technique, and review strategies that afford a more reliable interpretation of the results. This includes methods for computing and rendering probabilistic tractograms, which estimate precision in the face of measurement noise and artifacts. However, we also address aspects that have received less attention so far, such as model selection, partial voluming, and the impact of parameters, both in preprocessing and in fiber tracking itself. We conclude by giving impulses for future research

Geometric Convolutional Neural Network for Analyzing Surface-Based Neuroimaging Data

The conventional CNN, widely used for two-dimensional images, however, is not directly applicable to non-regular geometric surface, such as a cortical thickness. We propose Geometric CNN (gCNN) that deals with data representation over a spherical surface and renders pattern recognition in a multi-shell mesh structure. The classification accuracy for sex was significantly higher than that of SVM and image based CNN. It only uses MRI thickness data to classify gender but this method can expand to classify disease from other MRI or fMRI dataComment: 29 page

Spherical deconvolution of multichannel diffusion MRI data with non-Gaussian noise models and spatial regularization

Spherical deconvolution (SD) methods are widely used to estimate the intra-voxel white-matter fiber orientations from diffusion MRI data. However, while some of these methods assume a zero-mean Gaussian distribution for the underlying noise, its real distribution is known to be non-Gaussian and to depend on the methodology used to combine multichannel signals. Indeed, the two prevailing methods for multichannel signal combination lead to Rician and noncentral Chi noise distributions. Here we develop a Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to Rician and noncentral Chi likelihood models. To quantify the benefits of using proper noise models, RUMBA-SD was compared with dRL-SD, a well-established method based on the RL algorithm for Gaussian noise. Another aim of the study was to quantify the impact of including a total variation (TV) spatial regularization term in the estimation framework. To do this, we developed TV spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The evaluation was performed by comparing various quality metrics on 132 three-dimensional synthetic phantoms involving different inter-fiber angles and volume fractions, which were contaminated with noise mimicking patterns generated by data processing in multichannel scanners. The results demonstrate that the inclusion of proper likelihood models leads to an increased ability to resolve fiber crossings with smaller inter-fiber angles and to better detect non-dominant fibers. The inclusion of TV regularization dramatically improved the resolution power of both techniques. The above findings were also verified in brain data

Changes in MEG resting-state networks are related to cognitive decline in type 1 diabetes mellitus patients

OBJECTIVE: Integrity of resting-state functional brain networks (RSNs) is important for proper cognitive functioning. In type 1 diabetes mellitus (T1DM) cognitive decrements are commonly observed, possibly due to alterations in RSNs, which may vary according to microvascular complication status. Thus, we tested the hypothesis that functional connectivity in RSNs differs according to clinical status and correlates with cognition in T1DM patients, using an unbiased approach with high spatio-temporal resolution functional network.; METHODS: Resting-state magnetoencephalographic (MEG) data for T1DM patients with (n=42) and without (n=41) microvascular complications and 33 healthy participants were recorded. MEG time-series at source level were reconstructed using a recently developed atlas-based beamformer. Functional connectivity within classical frequency bands, estimated by the phase lag index (PLI), was calculated within eight commonly found RSNs. Neuropsychological tests were used to assess cognitive performance, and the relation with RSNs was evaluated.; RESULTS: Significant differences in terms of RSN functional connectivity between the three groups were observed in the lower alpha band, in the default-mode (DMN), executive control (ECN) and sensorimotor (SMN) RSNs. T1DM patients with microvascular complications showed the weakest functional connectivity in these networks relative to the other groups. For DMN, functional connectivity was higher in patients without microangiopathy relative to controls (all p<0.05). General cognitive performance for both patient groups was worse compared with healthy controls. Lower DMN alpha band functional connectivity correlated with poorer general cognitive ability in patients with microvascular complications.; DISCUSSION: Altered RSN functional connectivity was found in T1DM patients depending on clinical status. Lower DMN functional connectivity was related to poorer cognitive functioning. These results indicate that functional connectivity may play a key role in T1DM-related cognitive dysfunction