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

Content Based Image Retrieval of Brain MR Images across Different Classes

Magnetic Resonance Imaging play a vital role in the decision-diagnosis process of brain MR images. For an accurate diagnosis of brain related problems, the experts mostly compares both T1 and T2 weighted images as the information presented in these two images are complementary. In this paper, rotational and translational invariant form of Local binary Pattern (LBP) with additional gray scale information is used to retrieve similar slices of T1 weighted images from T2 weighted images or vice versa. The incorporation of additional gray scale information on LBP can extract more local texture information. The accuracy of retrieval can be improved by extracting moment features of LBP and reweighting the features based on users’ feedback. Here retrieval is done in a single subject scenario where similar images of a particular subject at a particular level are retrieved, and multiple subjects scenario where relevant images at a particular level across the subjects are retrieved

Photoperiod-dependent plasticity of circadian pacemaker center in the brain of the Madeira cockroach Rhyparobia maderae

To various degrees, insects in nature adapt to and live with two fundamental environmental rhythms around them: (1) the daily rhythm of light and dark, and (2) the yearly seasonal rhythm of the changing photoperiod (length of light per day). It is hypothesized that two biological clocks evolved in organisms on earth which allow them to harmonize successfully with the two environmental rhythms: (1) the circadian clock, which orchestrates circadian rhythms in physiology and behavior, and (2) the photoperiodic clock, which allows for physiological adaptations to changes in photoperiod during the course of the year (insect photoperiodism). The circadian rhythm is endogenous and continues in constant conditions, while photoperiodism requires specific light inputs of a minimal duration. Output pathways from both clocks control neurosecretory cells which regulate growth and reproduction. This dissertation focuses on the question whether different photoperiods change the network and physiology of the circadian clock of an originally equatorial cockroach species. It is assumed that photoperiod-dependent plasticity of the cockroach circadian clock allows for adaptations in physiology and behavior without the need for a separate photoperiodic clock circuit. The Madeira cockroach Rhyparobia maderae is a well established circadian clock model system. Lesion and transplantation studies identified the accessory medulla (aMe), a small neuropil with about 250 neurons, as the cockroach circadian pacemaker. Among them, the pigment-dispersing factor immunoreactive (PDF-ir) neurons anterior to the aMe (aPDFMes) play a key role as inputs to and outputs of the circadian clock system. The aim of my doctoral thesis was to examine whether and how different photoperiods modify the circadian clock system. With immunocytochemical studies, three-dimensional (3D) reconstruction, standardization and Ca2+-imaging technique, my studies revealed that raising cockroaches in different photoperiods changed the neuronal network of the circadian clock (Wei and Stengl, 2011). In addition, different photoperiods affected the physiology of single, isolated circadian pacemaker neurons. This thesis provides new evidence for the involvement of the circadian clock in insect photoperiodism. The data suggest that the circadian pacemaker system of the Madeira cockroach has the plasticity and potential to allow for physiological adaptations to different photoperiods. Therefore, it may express also properties of a photoperiodic clock.

Brain Tumor, Mood Disorder and Encephalopathy

Brain Tumor, Mood Disorder and EncephalopathyWe report a case of a patient was 65 years old, who was admitted with neurologic symptoms ill-defined by imaging findings that initial meningioma wing of the sphenoid, tumor resection, is performed. She presented torpid evolution, progressive neurological deterioration, until her death.

A shift to randomness of brain oscillations in people with autism

BACKGROUND: Resting-state functional magnetic resonance imaging (fMRI) enables investigation of the intrinsic functional organization of the brain. Fractal parameters such as the Hurst exponent, H, describe the complexity of endogenous low-frequency fMRI time series on a continuum from random (H = .5) to ordered (H = 1). Shifts in fractal scaling of physiological time series have been associated with neurological and cardiac conditions. METHODS: Resting-state fMRI time series were recorded in 30 male adults with an autism spectrum condition (ASC) and 33 age- and IQ-matched male volunteers. The Hurst exponent was estimated in the wavelet domain and between-group differences were investigated at global and voxel level and in regions known to be involved in autism. RESULTS: Complex fractal scaling of fMRI time series was found in both groups but globally there was a significant shift to randomness in the ASC (mean H = .758, SD = .045) compared with neurotypical volunteers (mean H = .788, SD = .047). Between-group differences in H, which was always reduced in the ASC group, were seen in most regions previously reported to be involved in autism, including cortical midline structures, medial temporal structures, lateral temporal and parietal structures, insula, amygdala, basal ganglia, thalamus, and inferior frontal gyrus. Severity of autistic symptoms was negatively correlated with H in retrosplenial and right anterior insular cortex. CONCLUSIONS: Autism is associated with a small but significant shift to randomness of endogenous brain oscillations. Complexity measures may provide physiological indicators for autism as they have done for other medical conditions.

The neural response to emotional prosody, as revealed by functional magnetic resonance imaging

Prosody is an important feature of language, comprising intonation, loudness, and tempo. Emotional prosodic processing forms an integral part of our social interactions. The main aim of this study was to use bold contrast fMRI to clarify the normal functional neuroanatomy of emotional prosody, in passive and active contexts. Subjects performed six separate scanning studies, within which two different conditions were contrasted: (1) "pure" emotional prosody versus rest; (2) congruent emotional prosody versus 'neutral' sentences; (3) congruent emotional prosody versus rest; (4) incongruent emotional prosody versus rest; (5) congruent versus incongruent emotional prosody; and (6) an active experiment in which subjects were instructed to either attend to the emotion conveyed by semantic content or that conveyed by tone of voice. Data resulting from these contrasts were analysed using SPM99. Passive listening to emotional prosody consistently activated the lateral temporal lobe (superior and/or middle temporal gyri). This temporal lobe response was relatively right-lateralised with or without semantic information. Both the separate and direct comparisons of congruent and incongruent emotional prosody revealed that subjects used fewer brain regions to process incongruent emotional prosody than congruent. The neural response to attention to semantics, was left lateralised, and recruited an extensive network not activated by attention to emotional prosody. Attention to emotional prosody modulated the response to speech, and induced right-lateralised activity, including the middle temporal gyrus. In confirming the results of lesion and neuropsychological studies, the current study emphasises the importance of the right hemisphere in the processing of emotional prosody, specifically the lateral temporal lobes. (C) 2003 Elsevier Science Ltd. All rights reserved.

How does the brain mediate interpretation of incongruent auditory emotions? The neural response to prosody in the presence of conflicting lexico-semantic cues

We frequently encounter conflicting emotion cues. This study examined how the neural response to emotional prosody differed in the presence of congruent and incongruent lexico-semantic cues. Two hypotheses were assessed: (i) decoding emotional prosody with conflicting lexico-semantic cues would activate brain regions associated with cognitive conflict (anterior cingulate and dorsolateral prefrontal cortex) or (ii) the increased attentional load of incongruent cues would modulate the activity of regions that decode emotional prosody (right lateral temporal cortex). While the participants indicated the emotion conveyed by prosody, functional magnetic resonance imaging data were acquired on a 3T scanner using blood oxygenation level-dependent contrast. Using SPM5, the response to congruent cues was contrasted with that to emotional prosody alone, as was the response to incongruent lexico-semantic cues (for the 'cognitive conflict' hypothesis). The right lateral temporal lobe region of interest analyses examined modulation of activity in this brain region between these two contrasts (for the 'prosody cortex' hypothesis). Dorsolateral prefrontal and anterior cingulate cortex activity was not observed, and neither was attentional modulation of activity in right lateral temporal cortex activity. However, decoding emotional prosody with incongruent lexico-semantic cues was strongly associated with left inferior frontal gyrus activity. This specialist form of conflict is therefore not processed by the brain using the same neural resources as non-affective cognitive conflict and neither can it be handled by associated sensory cortex alone. The recruitment of inferior frontal cortex may indicate increased semantic processing demands but other contributory functions of this region should be explored.

Individual differences in brain structure underpin empathizing–systemizing cognitive styles in male adults

Individual differences in cognitive style can be characterized along two dimensions: ‘systemizing’ (S, the drive to analyze or build ‘rule-based’ systems) and ‘empathizing’ (E, the drive to identify another's mental state and respond to this with an appropriate emotion). Discrepancies between these two dimensions in one direction (S > E) or the other (E > S) are associated with sex differences in cognition: on average more males show an S > E cognitive style, while on average more females show an E > S profile. The neurobiological basis of these different profiles remains unknown. Since individuals may be typical or atypical for their sex, it is important to move away from the study of sex differences and towards the study of differences in cognitive style. Using structural magnetic resonance imaging we examined how neuroanatomy varies as a function of the discrepancy between E and S in 88 adult males from the general population. Selecting just males allows us to study discrepant E-S profiles in a pure way, unconfounded by other factors related to sex and gender. An increasing S > E profile was associated with increased gray matter volume in cingulate and dorsal medial prefrontal areas which have been implicated in processes related to cognitive control, monitoring, error detection, and probabilistic inference. An increasing E > S profile was associated with larger hypothalamic and ventral basal ganglia regions which have been implicated in neuroendocrine control, motivation and reward. These results suggest an underlying neuroanatomical basis linked to the discrepancy between these two important dimensions of individual differences in cognitive style.

Where, when and why brain activation differs for bilinguals and monolinguals during picture naming and reading aloud

Using functional magnetic resonance imaging, we found that when bilinguals named pictures or read words aloud, in their native or nonnative language, activation was higher relative to monolinguals in 5 left hemisphere regions: dorsal precentral gyrus, pars triangularis, pars opercularis, superior temporal gyrus, and planum temporale. We further demonstrate that these areas are sensitive to increasing demands on speech production in monolinguals. This suggests that the advantage of being bilingual comes at the expense of increased work in brain areas that support monolingual word processing. By comparing the effect of bilingualism across a range of tasks, we argue that activation is higher in bilinguals compared with monolinguals because word retrieval is more demanding; articulation of each word is less rehearsed; and speech output needs careful monitoring to avoid errors when competition for word selection occurs between, as well as within,language.

Towards a model-based integration of co-registered electroencephalography/functional magnetic resonance imaging data with realistic neural population meshes

Brain activity can be measured with several non-invasive neuroimaging modalities, but each modality has inherent limitations with respect to resolution, contrast and interpretability. It is hoped that multimodal integration will address these limitations by using the complementary features of already available data. However, purely statistical integration can prove problematic owing to the disparate signal sources. As an alternative, we propose here an advanced neural population model implemented on an anatomically sound cortical mesh with freely adjustable connectivity, which features proper signal expression through a realistic head model for the electroencephalogram (EEG), as well as a haemodynamic model for functional magnetic resonance imaging based on blood oxygen level dependent contrast (fMRI BOLD). It hence allows simultaneous and realistic predictions of EEG and fMRI BOLD from the same underlying model of neural activity. As proof of principle, we investigate here the influence on simulated brain activity of strengthening visual connectivity. In the future we plan to fit multimodal data with this neural population model. This promises novel, model-based insights into the brain's activity in sleep, rest and task conditions.

Is optical imaging spectroscopy a viable measurement technique for the investigation of the negative BOLD phenomenon? A concurrent optical imaging spectroscopy and fMRI study at high field (7T)

Traditionally functional magnetic resonance imaging (fMRI) has been used to map activity in the human brain by measuring increases in the Blood Oxygenation Level Dependent (BOLD) signal. Often accompanying positive BOLD fMRI signal changes are sustained negative signal changes. Previous studies investigating the neurovascular coupling mechanisms of the negative BOLD phenomenon have used concurrent 2D-optical imaging spectroscopy (2D-OIS) and electrophysiology (Boorman et al., 2010). These experiments suggested that the negative BOLD signal in response to whisker stimulation was a result of an increase in deoxy-haemoglobin and reduced multi-unit activity in the deep cortical layers. However, Boorman et al. (2010) did not measure the BOLD and haemodynamic response concurrently and so could not quantitatively compare either the spatial maps or the 2D-OIS and fMRI time series directly. Furthermore their study utilised a homogeneous tissue model in which is predominantly sensitive to haemodynamic changes in more superficial layers. Here we test whether the 2D-OIS technique is appropriate for studies of negative BOLD. We used concurrent fMRI with 2D-OIS techniques for the investigation of the haemodynamics underlying the negative BOLD at 7 Tesla. We investigated whether optical methods could be used to accurately map and measure the negative BOLD phenomenon by using 2D-OIS haemodynamic data to derive predictions from a biophysical model of BOLD signal changes. We showed that despite the deep cortical origin of the negative BOLD response, if an appropriate heterogeneous tissue model is used in the spectroscopic analysis then 2D-OIS can be used to investigate the negative BOLD phenomenon.

A three-compartment model of the hemodynamic response and oxygen delivery to brain

We describe a mathematical model linking changes in cerebral blood flow, blood volume and the blood oxygenation state in response to stimulation. The model has three compartments to take into account the fact that the cerebral blood flow and volume as measured concurrently using laser Doppler flowmetry and optical imaging spectroscopy have contributions from the arterial, capillary as well as the venous compartments of the vasculature. It is an extension to previous one-compartment hemodynamic models which assume that the measured blood volume changes are from the venous compartment only. An important assumption of the model is that the tissue oxygen concentration is a time varying state variable of the system and is driven by the changes in metabolic demand resulting from changes in neural activity. The model takes into account the pre-capillary oxygen diffusion by flexibly allowing the saturation of the arterial compartment to be less than unity. Simulations are used to explore the sensitivity of the model and to optimise the parameters for experimental data. We conclude that the three-compartment model was better than the one-compartment model at capturing the hemodynamics of the response to changes in neural activation following stimulation.

A model of the dynamic relationship between blood flow and volume changes during brain activation

The temporal relationship between changes in cerebral blood flow (CBF) and cerebral blood volume (CBV) is important in the biophysical modeling and interpretation of the hemodynamic response to activation, particularly in the context of magnetic resonance imaging and the blood oxygen level-dependent signal. Grubb et al. (1974) measured the steady state relationship between changes in CBV and CBF after hypercapnic challenge. The relationship CBV proportional to CBFPhi has been used extensively in the literature. Two similar models, the Balloon (Buxton et al., 1998) and the Windkessel (Mandeville et al., 1999), have been proposed to describe the temporal dynamics of changes in CBV with respect to changes in CBF. In this study, a dynamic model extending the Windkessel model by incorporating delayed compliance is presented. The extended model is better able to capture the dynamics of CBV changes after changes in CBF, particularly in the return-to-baseline stages of the response.

Analysis of diffusion tensor magnetic resonance imaging data using principal component analysis

An analysis method for diffusion tensor (DT) magnetic resonance imaging data is described, which, contrary to the standard method (multivariate fitting), does not require a specific functional model for diffusion-weighted (DW) signals. The method uses principal component analysis (PCA) under the assumption of a single fibre per pixel. PCA and the standard method were compared using simulations and human brain data. The two methods were equivalent in determining fibre orientation. PCA-derived fractional anisotropy and DT relative anisotropy had similar signal-to-noise ratio (SNR) and dependence on fibre shape. PCA-derived mean diffusivity had similar SNR to the respective DT scalar, and it depended on fibre anisotropy. Appropriate scaling of the PCA measures resulted in very good agreement between PCA and DT maps. In conclusion, the assumption of a specific functional model for DW signals is not necessary for characterization of anisotropic diffusion in a single fibre.

Magnetic resonance imaging of a randomized controlled trial investigating predictors of recovery following psychological treatment in adolescents with moderate to severe unipolar depression: study protocol for Magnetic Resonance - Improving Mood with Psychoanalytic and Cognitive Therapies (MR-IMPACT)

Background Major depressive disorders (MDD) are a debilitating and pervasive group of mental illnesses afflicting many millions of people resulting in the loss of 110 million working days and more than 2,500 suicides per annum. Adolescent MDD patients attending NHS clinics show high rates of recurrence into adult life. A meta-analysis of recent research shows that psychological treatments are not as efficacious as previously thought. Modest treatment outcomes of approximately 65% of cases responding suggest that aetiological and clinical heterogeneity may hamper the better use of existing therapies and discovery of more effective treatments. Information with respect to optimal treatment choice for individuals is lacking, with no validated biomarkers to aid therapeutic decision-making. Methods/Design Magnetic resonance-Improving Mood with Psychoanalytic and Cognitive Therapies, the MR-IMPACT study, plans to identify brain regions implicated in the pathophysiology of depressions and examine whether there are specific behavioural or neural markers predicting remission and/or subsequent relapse in a subsample of depressed adolescents recruited to the IMPACT randomised controlled trial (Registration # ISRCTN83033550). Discussion MR-IMPACT is an investigative biomarker component of the IMPACT pragmatic effectiveness trial. The aim of this investigation is to identify neural markers and regional indicators of the pathophysiology of and treatment response for MDD in adolescents. We anticipate that these data may enable more targeted treatment delivery by identifying those patients who may be optimal candidates for therapeutic response.