A novel brain partition highlights the modular skeleton shared by structure and function

Elucidating the intricate relationship between brain structure and function, both in healthy and pathological conditions, is a key challenge for modern neuroscience. Recent progress in neuroimaging has helped advance our understanding of this important issue, with diffusion images providing information about structural connectivity (SC) and functional magnetic resonance imaging shedding light on resting state functional connectivity (rsFC). Here, we adopt a systems approach, relying on modular hierarchical clustering, to study together SC and rsFC datasets gathered independently from healthy human subjects. Our novel approach allows us to find a common skeleton shared by structure and function from which a new, optimal, brain partition can be extracted. We describe the emerging common structure-function modules (SFMs) in detail and compare them with commonly employed anatomical or functional parcellations. Our results underline the strong correspondence between brain structure and resting-state dynamics as well as the emerging coherent organization of the human brain.

The causal attributions of nursing students towards adolescent survivors of brain injury

Background: The hidden nature of brain injury means that it is often difficult for people to understand the sometimes challenging behaviors that individuals exhibit. The misattribution of these behaviors may lead to a lack of consideration and public censure if the individual is seen as simply misbehaving.

Objective: The aim of this study was to explore the impact of visual cues indicating the presence or absence of brain injury on prejudice, desire for social interaction, and causal attributions of nursing and computing science students.

Method: An independent-groups design was employed in this research, which recruited 190 first-year nursing students and 194 first-year computing science students from a major university in Belfast, UK. A short passage describing an adolescent’s behavior after a brain injury, together with one of three images portraying a young adolescent with a scar, a head dressing, or neither of these, was given to participants. They were then asked to answer questions relating to prejudice, social interaction, locus of control, and causal attributions. The attributional statements suggested that the character’s behavior could be the result of brain injury or adolescence.

Results: Analysis of variance demonstrated a statistically significant difference between the student groups, where nursing students (M = 45.17, SD = 4.69) desired more social interaction with the fictional adolescent than their computer science peers (M = 38.64, SD = 7.69). Further, analysis of variance showed a main effect of image on the attributional statement that described adolescence as a suitable explanation for the character’s lack of self-confidence.

Discussion: Attributions of brain injury were influenced by the presence of a visible but potentially specious indicator of injury. This suggests that survivors of brain injury who do not display any outward indicator may receive less care and face expectations to behave in a manner consistent with the norms of society. If their injury does not allow them to meet with these expectations, they may face public censure and discrimination.

Effects of very low birthweight on brain structure in adulthood

Very-low-birthweight (VLBW) individuals are at high risk of brain injury in the perinatal period. We wished to determine how such early brain lesions affect brain structure in adulthood. Thirty-two VLBW adults (20 female, 12 male) and, 18 term, normal birthweight sibling control individuals (nine female, nine male) underwent structural MRI at a mean age of 23 years 4 months (range 17 to 33 years; SD 3.4). Images were analyzed using an automated tissue segmentation algorithm in order to estimate whole brain tissue class volumes in native space. Images were then warped to a template image in standard space. There was no significant between-group difference in whole brain, greymatter, white matter, or total cerebral spinal fluid (CSF) volumes. However, lateral ventricular volume was significantly increased by 41% in those with VLBW. The ratio of grey to white matter was also significantly increased (by 10%) in those with VLBW. Group comparison maps showed widespread changes in the distribution of grey and white matter, and relative excess of ventricular CSF, in the brains of VLBW individuals. Increased ventricular volume predicted decreased grey matter in subcortical nuclei and limbic cortical structures, and decreased periventricular white matter. We conclude that these diffuse abnormalities of grey and white matter are a consequence,of the interaction of perinatal brain injury and ongoing neurodevelopmental processes.

Structural brain abnormalities in male schizophrenics reflect fronto-temporal dissociation

Background. Many studies have separately reported abnormalities of frontal and temporal lobe structures in schizophrenia, but little is known of structural fronto-temporal associations in this condition. We investigated whether male patients with chronic schizophrenia would show abnormal patterns of correlation between regional brain volumes.

Methods. Structural magnetic resonance images of the brain in 42 patients were compared with 43 matched unaffected controls. We explored the pattern of association between regional brain volumes by correlational analyses, and non-parametrically tested for significance of between-group differences by randomization.

Results. The schizophrenics demonstrated significant volume deficits in several brain regions (left temporal lobe and hippocampus, right dorsolateral prefrontal cortex), and significant volume increases in the ventricular system (third ventricle and left temporal horn of the lateral ventricle). Controls demonstrated large positive correlations (r > 0.4) between prefrontal and temporal lobe regions. By contrast, inter-regional correlations significantly reduced in schizophrenics included those between prefrontal, anterior cingulate and temporal regions, and between posterior cingulate and hippocampus (P < 0.05). The most salient abnormality in patients was a dissociation between prefrontal and superior temporal gyrus volumes (P < 0.01).

Conclusions. These results support the existence of a relative 'fronto-temporal dissociation' in schizophrenia which we suggest may be due to lack of mutually trophic influences during frontal and temporal lobe development.

3D Reconstruction of Medical Images from Slices Automatically Landmarked with Growing Neural Models

In this study, we utilise a novel approach to segment out the ventricular system in a series of high resolution T1-weighted MR images. We present a brain ventricles fast reconstruction method. The method is based on the processing of brain sections and establishing a fixed number of landmarks onto those sections to reconstruct the ventricles 3D surface. Automated landmark extraction is accomplished through the use of the self-organising network, the growing neural gas (GNG), which is able to topographically map the low dimensionality of the network to the high dimensionality of the contour manifold without requiring a priori knowledge of the input space structure. Moreover, our GNG landmark method is tolerant to noise and eliminates outliers. Our method accelerates the classical surface reconstruction and filtering processes. The proposed method offers higher accuracy compared to methods with similar efficiency as Voxel Grid.

Effects of MRI scan acceleration on brain volume measurement consistency.

PURPOSE: To evaluate the effects of recent advances in magnetic resonance imaging (MRI) radiofrequency (RF) coil and parallel imaging technology on brain volume measurement consistency. MATERIALS AND METHODS: In all, 103 whole-brain MRI volumes were acquired at a clinical 3T MRI, equipped with a 12- and 32-channel head coil, using the T1-weighted protocol as employed in the Alzheimer's Disease Neuroimaging Initiative study with parallel imaging accelerations ranging from 1 to 5. An experienced reader performed qualitative ratings of the images. For quantitative analysis, differences in composite width (CW, a measure of image similarity) and boundary shift integral (BSI, a measure of whole-brain atrophy) were calculated. RESULTS: Intra- and intersession comparisons of CW and BSI measures from scans with equal acceleration demonstrated excellent scan-rescan accuracy, even at the highest acceleration applied. Pairs-of-scans acquired with different accelerations exhibited poor scan-rescan consistency only when differences in the acceleration factor were maximized. A change in the coil hardware between compared scans was found to bias the BSI measure. CONCLUSION: The most important findings are that the accelerated acquisitions appear to be compatible with the assessment of high-quality quantitative information and that for highest scan-rescan accuracy in serial scans the acquisition protocol should be kept as consistent as possible over time. J. Magn. Reson. Imaging 2012;36:1234-1240. ©2012 Wiley Periodicals, Inc.

Snapshot gradient-recalled echo-planar images of rat brains at long echo time at 9.4 T.

With improved B 0 homogeneity along with satisfactory gradient performance at high magnetic fields, snapshot gradient-recalled echo-planar imaging (GRE-EPI) would perform at long echo times (TEs) on the order of T2*, which intrinsically allows obtaining strongly T2*-weighted images with embedded substantial anatomical details in ultrashort time. The aim of this study was to investigate the feasibility and quality of long TE snapshot GRE-EPI images of rat brain at 9.4 T. When compensating for B 0 inhomogeneities, especially second-order shim terms, a 200 x 200 microm2 in-plane resolution image was reproducibly obtained at long TE (>25 ms). The resulting coronal images at 30 ms had diminished geometric distortions and, thus, embedded substantial anatomical details. Concurrently with the very consistent stability, such GRE-EPI images should permit to resolve functional data not only with high specificity but also with substantial anatomical details, therefore allowing coregistration of the acquired functional data on the same image data set.

La caractérisation neuromagnétique de l'attention sans réflexion chez les méditants zen dans un contexte d'images visuelles négatives.

L’état d’attention sans réflexion, aussi appelé « mindfulness », a démontré des effets positifs en clinique pour les désordres émotionnels associés à diverses conditions. Le nombre d’études portant sur la caractérisation des substrats neuronaux de cet état attentionnel croît, mais il importe d’investiguer davantage à ce chapitre pour éventuellement améliorer les interventions cliniques. La présente étude compte aider à déterminer, par la magnétoencéphalographie, quelles régions cérébrales sont en corrélation avec le mindfulness chez des experts, i.e. des méditants Zen. Ces derniers cultivent un état dans lequel ils s’abstiennent de rechercher ou de rejeter les phénomènes sensoriels, ce qui en fait d’excellents candidats à la présente étude. Dans un contexte de stimulations visuelles émotionnelles, il fut demandé aux méditants tantôt d’observer les images dans un état de mindfulness (condition expérimentale), tantôt dans un état dit normal (condition contrôle) où aucun effort particulier d’attention n’était requis. Les résultats d’analyse suggèrent que les participants expérimentèrent une intensité émotionnelle moins importante en mindfulness : les cotes subjectives ainsi qu’une réponse magnétique cérébrale reliée aux émotions nommée Potentiel Positif Tardif magnétique (PPTm) suggèrent cela. Cependant, le résultat le plus statistiquement probant dépasse la nature affective des stimuli. Il s’agit d’une diminution temporellement soutenue de l’activité de fréquence gamma au niveau des zones visuelles associatives du lobe temporal droit, sans égard à la nature des images. Également, une suppression de l’activité gamma d’une zone du cortex préfrontal latéral gauche fut observée. Ceci pourrait indiquer une diminution de la conceptualisation des stimuli reliée au langage et aux processus réflectifs du soi.

Where there's smoke, there's fire : the brain reactivity of chronic smokers to anti-smoking stimuli

Contexte: Plusieurs études ont démontré que les indices environnementaux associés à la cigarette peuvent provoquer des envies de consommer (« cravings ») chez les fumeurs, ce qui nuit aux efforts d’abandon de la substance et favorise le maintien du tabagisme. Un bon nombre d’études en imagerie cérébrale ont examiné les bases neurophysiologiques de cette caractéristique clinique. Le tabagisme se caractérise aussi par l’incapacité des représentations négatives de la consommation (méfaits médicaux et sociaux) d’influencer la consommation des fumeurs. Étonnamment toutefois, très peu de travaux de recherche se sont intéressés à examiner les bases neurophysiologiques de cette insouciance envers les méfaits de la cigarette chez les fumeurs. En utilisant l'imagerie cérébrale fonctionnelle, l'objectif de cette étude était: d’examiner la réponse neurophysiologique des fumeurs chroniques à des images qui illustrent les effets négatifs de la cigarette (campagne anti-tabac); d’examiner le caractère affectif de cette réactivité utilisant des conditions contrôles (c.-à-d., images aversives non-liées au tabac et appétitives liées au tabac); d'examiner la connectivité fonctionnelle durant cette tâche entre les systèmes affectifs et exécutifs (une interaction qui peut favoriser ou entraver l'impact des évènements aversifs). Méthodes: 30 fumeurs chroniques ont passé une session de neuroimagerie durant laquelle ils devaient regarder des images appétitives et aversives de cigarettes, des images aversives non-reliées au tabac et des images neutres. Résultats: Les images aversives liés au tabagisme suscitent une plus grande activation dans le cortex médial préfrontal, l'amygdale, le gyrus frontal inférieur et le cortex orbitofrontal latéral en comparaison avec les images neutres, mais une moins grande activation dans des structures médiaux / sous-corticales comparé aux images aversives non-reliés et images appétitives reliées aux tabac. L’activité du système exécutif présente une connectivité fonctionnelle négative avec le système affectif lorsque les images aversives sont liées au tabac, mais pas quand elles ne le sont pas. Conclusions: Le modèle d'activation du cerveau observé suggère qu’il y a un biais dans la réactivité des fumeurs chroniques lorsqu’ils observent des représentations négatives de la consommation du tabac. L’activité du système exécutif cérébral semble promouvoir chez les fumeurs une baisse d’activité dans des régions impliquées dans la genèse d’une réponse physiologique affective; il s’agit d’un mécanisme qui permettrait de réduire l’impact persuasif de ces représentations des méfaits de la cigarette sur la consommation des fumeurs.

Content-Based Image Retrieval of Axial Brain Slices Using a Novel LBP with a Ternary Encoding

Retrieval of similar anatomical structures of brain MR images across patients would help the expert in diagnosis of diseases. In this paper, modified local binary pattern with ternary encoding called modified local ternary pattern (MOD-LTP) is introduced, which is more discriminant and less sensitive to noise in near-uniform regions, to locate slices belonging to the same level from the brain MR image database. The ternary encoding depends on a threshold, which is a user-specified one or calculated locally, based on the variance of the pixel intensities in each window. The variancebased local threshold makes the MOD-LTP more robust to noise and global illumination changes. The retrieval performance is shown to improve by taking region-based moment features of MODLTP and iteratively reweighting the moment features of MOD-LTP based on the user’s feedback. The average rank obtained using iterated and weighted moment features of MOD-LTP with a local variance-based threshold, is one to two times better than rotational invariant LBP (Unay, D., Ekin, A. and Jasinschi, R.S. (2010) Local structure-based region-of-interest retrieval in brain MR images. IEEE Trans. Inf. Technol. Biomed., 14, 897–903.) in retrieving the first 10 relevant images

Spectral clustering independent component analysis for tissue classification from brain MRI

A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases

Brain Tissue Classification from Multispectral MRI by Wavelet based Principal Component Analysis

In this paper, we propose a multispectral analysis system using wavelet based Principal Component Analysis (PCA), to improve the brain tissue classification from MRI images. Global transforms like PCA often neglects significant small abnormality details, while dealing with a massive amount of multispectral data. In order to resolve this issue, input dataset is expanded by detail coefficients from multisignal wavelet analysis. Then, PCA is applied on the new dataset to perform feature analysis. Finally, an unsupervised classification with Fuzzy C-Means clustering algorithm is used to measure the improvement in reproducibility and accuracy of the results. A detailed comparative analysis of classified tissues with those from conventional PCA is also carried out. Proposed method yielded good improvement in classification of small abnormalities with high sensitivity/accuracy values, 98.9/98.3, for clinical analysis. Experimental results from synthetic and clinical data recommend the new method as a promising approach in brain tissue analysis.

Wavelet based Independent Component Analysis for Multispectral Brain Tissue Classification

Multispectral analysis is a promising approach in tissue classification and abnormality detection from Magnetic Resonance (MR) images. But instability in accuracy and reproducibility of the classification results from conventional techniques keeps it far from clinical applications. Recent studies proposed Independent Component Analysis (ICA) as an effective method for source signals separation from multispectral MR data. However, it often fails to extract the local features like small abnormalities, especially from dependent real data. A multisignal wavelet analysis prior to ICA is proposed in this work to resolve these issues. Best de-correlated detail coefficients are combined with input images to give better classification results. Performance improvement of the proposed method over conventional ICA is effectively demonstrated by segmentation and classification using k-means clustering. Experimental results from synthetic and real data strongly confirm the positive effect of the new method with an improved Tanimoto index/Sensitivity values, 0.884/93.605, for reproduced small white matter lesions

Automatic Segmentation Framework for Primary Tumors From Brain MRIs Using Morphological Filtering Techniques

This paper describes a novel framework for automatic segmentation of primary tumors and its boundary from brain MRIs using morphological filtering techniques. This method uses T2 weighted and T1 FLAIR images. This approach is very simple, more accurate and less time consuming than existing methods. This method is tested by fifty patients of different tumor types, shapes, image intensities, sizes and produced better results. The results were validated with ground truth images by the radiologist. Segmentation of the tumor and boundary detection is important because it can be used for surgical planning, treatment planning, textural analysis, 3-Dimensional modeling and volumetric analysis

A semi-automatic method for segmentation and 3D modeling of glioma tumors from brain MRI

This work presents an efficient method for volume rendering of glioma tumors from segmented 2D MRI Datasets with user interactive control, by replacing manual segmentation required in the state of art methods. The most common primary brain tumors are gliomas, evolving from the cerebral supportive cells. For clinical follow-up, the evaluation of the pre- operative tumor volume is essential. Tumor portions were automatically segmented from 2D MR images using morphological filtering techniques. These seg- mented tumor slices were propagated and modeled with the software package. The 3D modeled tumor consists of gray level values of the original image with exact tumor boundary. Axial slices of FLAIR and T2 weighted images were used for extracting tumors. Volumetric assessment of tumor volume with manual segmentation of its outlines is a time-consuming proc- ess and is prone to error. These defects are overcome in this method. Authors verified the performance of our method on several sets of MRI scans. The 3D modeling was also done using segmented 2D slices with the help of a medical software package called 3D DOCTOR for verification purposes. The results were validated with the ground truth models by the Radi- ologist.