Towards person-centered neuroimaging markers for resilience and vulnerability in Bipolar Disorder

Improved clinical care for Bipolar Disorder (BD) relies on the identification of diagnostic markers that can reliably detect disease-related signals in clinically heterogeneous populations. At the very least, diagnostic markers should be able to differentiate patients with BD from healthy individuals and from individuals at familial risk for BD who either remain well or develop other psychopathology, most commonly Major Depressive Disorder (MDD). These issues are particularly pertinent to the development of translational applications of neuroimaging as they represent challenges for which clinical observation alone is insufficient. We therefore applied pattern classification to task-based functional magnetic resonance imaging (fMRI) data of the n-back working memory task, to test their predictive value in differentiating patients with BD (n=30) from healthy individuals (n=30) and from patients' relatives who were either diagnosed with MDD (n=30) or were free of any personal lifetime history of psychopathology (n=30). Diagnostic stability in these groups was confirmed with 4-year prospective follow-up. Task-based activation patterns from the fMRI data were analyzed with Gaussian Process Classifiers (GPC), a machine learning approach to detecting multivariate patterns in neuroimaging datasets. Consistent significant classification results were only obtained using data from the 3-back versus 0-back contrast. Using contrast, patients with BD were correctly classified compared to unrelated healthy individuals with an accuracy of 83.5%, sensitivity of 84.6% and specificity of 92.3%. Classification accuracy, sensitivity and specificity when comparing patients with BD to their relatives with MDD, were respectively 73.1%, 53.9% and 94.5%. Classification accuracy, sensitivity and specificity when comparing patients with BD to their healthy relatives were respectively 81.8%, 72.7% and 90.9%. We show that significant individual classification can be achieved using whole brain pattern analysis of task-based working memory fMRI data. The high accuracy and specificity achieved by all three classifiers suggest that multivariate pattern recognition analyses can aid clinicians in the clinical care of BD in situations of true clinical uncertainty regarding the diagnosis and prognosis.

Lesion detection performance in an anthropomorphic chest phantom: comparative analysis of low-dose CT data on two hybrid imaging systems

Introduction: multimodality environment; requirement for greater understanding of the imaging technologies used, the limitations of these technologies, and how to best interpret the results; dose optimization; introduction of new techniques; current practice and best practice; incidental findings, in low-dose CT images obtained as part of the hybrid imaging process, are an increasing phenomenon with advancing CT technology; resultant ethical and medico-legal dilemmas; understanding limitations of these procedures important when reporting images and recommending follow-up; free-response observer performance study was used to evaluate lesion detection in low-dose CT images obtained during attenuation correction acquisitions for myocardial perfusion imaging, on two hybrid imaging systems.

Histology-driven data mining of lipid signatures from multiple imaging mass spectrometry analyses: application to human colorectal cancer liver metastasis biopsies.

Imaging mass spectrometry (IMS) represents an innovative tool in the cancer research pipeline, which is increasingly being used in clinical and pharmaceutical applications. The unique properties of the technique, especially the amount of data generated, make the handling of data from multiple IMS acquisitions challenging. This work presents a histology-driven IMS approach aiming to identify discriminant lipid signatures from the simultaneous mining of IMS data sets from multiple samples. The feasibility of the developed workflow is evaluated on a set of three human colorectal cancer liver metastasis (CRCLM) tissue sections. Lipid IMS on tissue sections was performed using MALDI-TOF/TOF MS in both negative and positive ionization modes after 1,5-diaminonaphthalene matrix deposition by sublimation. The combination of both positive and negative acquisition results was performed during data mining to simplify the process and interrogate a larger lipidome into a single analysis. To reduce the complexity of the IMS data sets, a sub data set was generated by randomly selecting a fixed number of spectra from a histologically defined region of interest, resulting in a 10-fold data reduction. Principal component analysis confirmed that the molecular selectivity of the regions of interest is maintained after data reduction. Partial least-squares and heat map analyses demonstrated a selective signature of the CRCLM, revealing lipids that are significantly up- and down-regulated in the tumor region. This comprehensive approach is thus of interest for defining disease signatures directly from IMS data sets by the use of combinatory data mining, opening novel routes of investigation for addressing the demands of the clinical setting.

Fracture imaging within a granitic rock aquifer using multiple-offset single-hole and cross-hole GPR reflection data

The sparsely spaced highly permeable fractures of the granitic rock aquifer at Stang-er-Brune (Brittany, France) form a well-connected fracture network of high permeability but unknown geometry. Previous work based on optical and acoustic logging together with single-hole and cross-hole flowmeter data acquired in 3 neighbouring boreholes (70-100 m deep) has identified the most important permeable fractures crossing the boreholes and their hydraulic connections. To constrain possible flow paths by estimating the geometries of known and previously unknown fractures, we have acquired, processed and interpreted multifold, single- and cross-hole GPR data using 100 and 250 MHz antennas. The GPR data processing scheme consisting of timezero corrections, scaling, bandpass filtering and F-X deconvolution, eigenvector filtering, muting, pre-stack Kirchhoff depth migration and stacking was used to differentiate fluid-filled fracture reflections from source generated noise. The final stacked and pre-stack depth-migrated GPR sections provide high-resolution images of individual fractures (dipping 30-90°) in the surroundings (2-20 m for the 100 MHz antennas; 2-12 m for the 250 MHz antennas) of each borehole in a 2D plane projection that are of superior quality to those obtained from single-offset sections. Most fractures previously identified from hydraulic testing can be correlated to reflections in the single-hole data. Several previously unknown major near vertical fractures have also been identified away from the boreholes.

A filtering method to correct time-lapse 3D ERT data and improve imaging of natural aquifer dynamics

We have developed a processing methodology that allows crosshole ERT (electrical resistivity tomography) monitoring data to be used to derive temporal fluctuations of groundwater electrical resistivity and thereby characterize the dynamics of groundwater in a gravel aquifer as it is infiltrated by river water. Temporal variations of the raw ERT apparent-resistivity data were mainly sensitive to the resistivity (salinity), temperature and height of the groundwater, with the relative contributions of these effects depending on the time and the electrode configuration. To resolve the changes in groundwater resistivity, we first expressed fluctuations of temperature-detrended apparent-resistivity data as linear superpositions of (i) time series of riverwater-resistivity variations convolved with suitable filter functions and (ii) linear and quadratic representations of river-water-height variations multiplied by appropriate sensitivity factors; river-water height was determined to be a reliable proxy for groundwater height. Individual filter functions and sensitivity factors were obtained for each electrode configuration via deconvolution using a one month calibration period and then the predicted contributions related to changes in water height were removed prior to inversion of the temperature-detrended apparent-resistivity data. Applications of the filter functions and sensitivity factors accurately predicted the apparent-resistivity variations (the correlation coefficient was 0.98). Furthermore, the filtered ERT monitoring data and resultant time-lapse resistivity models correlated closely with independently measured groundwater electrical resistivity monitoring data and only weakly with the groundwater-height fluctuations. The inversion results based on the filtered ERT data also showed significantly less inversion artefacts than the raw data inversions. We observed resistivity increases of up to 10% and the arrival time peaks in the time-lapse resistivity models matched those in the groundwater resistivity monitoring data.

Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data.

Microstructure imaging from diffusion magnetic resonance (MR) data represents an invaluable tool to study non-invasively the morphology of tissues and to provide a biological insight into their microstructural organization. In recent years, a variety of biophysical models have been proposed to associate particular patterns observed in the measured signal with specific microstructural properties of the neuronal tissue, such as axon diameter and fiber density. Despite very appealing results showing that the estimated microstructure indices agree very well with histological examinations, existing techniques require computationally very expensive non-linear procedures to fit the models to the data which, in practice, demand the use of powerful computer clusters for large-scale applications. In this work, we present a general framework for Accelerated Microstructure Imaging via Convex Optimization (AMICO) and show how to re-formulate this class of techniques as convenient linear systems which, then, can be efficiently solved using very fast algorithms. We demonstrate this linearization of the fitting problem for two specific models, i.e. ActiveAx and NODDI, providing a very attractive alternative for parameter estimation in those techniques; however, the AMICO framework is general and flexible enough to work also for the wider space of microstructure imaging methods. Results demonstrate that AMICO represents an effective means to accelerate the fit of existing techniques drastically (up to four orders of magnitude faster) while preserving accuracy and precision in the estimated model parameters (correlation above 0.9). We believe that the availability of such ultrafast algorithms will help to accelerate the spread of microstructure imaging to larger cohorts of patients and to study a wider spectrum of neurological disorders.

Design, data management, and population baseline characteristics of the PERFORM magnetic resonance imaging project.

Quantitative information from magnetic resonance imaging (MRI) may substantiate clinical findings and provide additional insight into the mechanism of clinical interventions in therapeutic stroke trials. The PERFORM study is exploring the efficacy of terutroban versus aspirin for secondary prevention in patients with a history of ischemic stroke. We report on the design of an exploratory longitudinal MRI follow-up study that was performed in a subgroup of the PERFORM trial. An international multi-centre longitudinal follow-up MRI study was designed for different MR systems employing safety and efficacy readouts: new T2 lesions, new DWI lesions, whole brain volume change, hippocampal volume change, changes in tissue microstructure as depicted by mean diffusivity and fractional anisotropy, vessel patency on MR angiography, and the presence of and development of new microbleeds. A total of 1,056 patients (men and women ≥ 55 years) were included. The data analysis included 3D reformation, image registration of different contrasts, tissue segmentation, and automated lesion detection. This large international multi-centre study demonstrates how new MRI readouts can be used to provide key information on the evolution of cerebral tissue lesions and within the macrovasculature after atherothrombotic stroke in a large sample of patients.

Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data.

Although cross-sectional diffusion tensor imaging (DTI) studies revealed significant white matter changes in mild cognitive impairment (MCI), the utility of this technique in predicting further cognitive decline is debated. Thirty-five healthy controls (HC) and 67 MCI subjects with DTI baseline data were neuropsychologically assessed at one year. Among them, there were 40 stable (sMCI; 9 single domain amnestic, 7 single domain frontal, 24 multiple domain) and 27 were progressive (pMCI; 7 single domain amnestic, 4 single domain frontal, 16 multiple domain). Fractional anisotropy (FA) and longitudinal, radial, and mean diffusivity were measured using Tract-Based Spatial Statistics. Statistics included group comparisons and individual classification of MCI cases using support vector machines (SVM). FA was significantly higher in HC compared to MCI in a distributed network including the ventral part of the corpus callosum, right temporal and frontal pathways. There were no significant group-level differences between sMCI versus pMCI or between MCI subtypes after correction for multiple comparisons. However, SVM analysis allowed for an individual classification with accuracies up to 91.4% (HC versus MCI) and 98.4% (sMCI versus pMCI). When considering the MCI subgroups separately, the minimum SVM classification accuracy for stable versus progressive cognitive decline was 97.5% in the multiple domain MCI group. SVM analysis of DTI data provided highly accurate individual classification of stable versus progressive MCI regardless of MCI subtype, indicating that this method may become an easily applicable tool for early individual detection of MCI subjects evolving to dementia.

A general linear relaxometry model of R1 using imaging data.

PURPOSE: The longitudinal relaxation rate (R1 ) measured in vivo depends on the local microstructural properties of the tissue, such as macromolecular, iron, and water content. Here, we use whole brain multiparametric in vivo data and a general linear relaxometry model to describe the dependence of R1 on these components. We explore a) the validity of having a single fixed set of model coefficients for the whole brain and b) the stability of the model coefficients in a large cohort. METHODS: Maps of magnetization transfer (MT) and effective transverse relaxation rate (R2 *) were used as surrogates for macromolecular and iron content, respectively. Spatial variations in these parameters reflected variations in underlying tissue microstructure. A linear model was applied to the whole brain, including gray/white matter and deep brain structures, to determine the global model coefficients. Synthetic R1 values were then calculated using these coefficients and compared with the measured R1 maps. RESULTS: The model's validity was demonstrated by correspondence between the synthetic and measured R1 values and by high stability of the model coefficients across a large cohort. CONCLUSION: A single set of global coefficients can be used to relate R1 , MT, and R2 * across the whole brain. Our population study demonstrates the robustness and stability of the model. Magn Reson Med, 2014. © 2014 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. Magn Reson Med 73:1309-1314, 2015. © 2014 Wiley Periodicals, Inc.

Prospective motion correction of 3D echo-planar imaging data for functional MRI using optical tracking.

We evaluated the performance of an optical camera based prospective motion correction (PMC) system in improving the quality of 3D echo-planar imaging functional MRI data. An optical camera and external marker were used to dynamically track the head movement of subjects during fMRI scanning. PMC was performed by using the motion information to dynamically update the sequence's RF excitation and gradient waveforms such that the field-of-view was realigned to match the subject's head movement. Task-free fMRI experiments on five healthy volunteers followed a 2×2×3 factorial design with the following factors: PMC on or off; 3.0mm or 1.5mm isotropic resolution; and no, slow, or fast head movements. Visual and motor fMRI experiments were additionally performed on one of the volunteers at 1.5mm resolution comparing PMC on vs PMC off for no and slow head movements. Metrics were developed to quantify the amount of motion as it occurred relative to k-space data acquisition. The motion quantification metric collapsed the very rich camera tracking data into one scalar value for each image volume that was strongly predictive of motion-induced artifacts. The PMC system did not introduce extraneous artifacts for the no motion conditions and improved the time series temporal signal-to-noise by 30% to 40% for all combinations of low/high resolution and slow/fast head movement relative to the standard acquisition with no prospective correction. The numbers of activated voxels (p<0.001, uncorrected) in both task-based experiments were comparable for the no motion cases and increased by 78% and 330%, respectively, for PMC on versus PMC off in the slow motion cases. The PMC system is a robust solution to decrease the motion sensitivity of multi-shot 3D EPI sequences and thereby overcome one of the main roadblocks to their widespread use in fMRI studies.

Breast imaging reporting and data system (BI-RADS™): como tem sido utilizado?

O Breast Imaging Reporting and Data System (BI-RADS™), do American College of hRadiology, foi concebido para padronizar o laudo mamográfico e reduzir os fatores de confusão na descrição e interpretação das imagens, além de facilitar o monitoramento do resultado final. OBJETIVO: Identificar a maneira como vem sendo utilizado o BI-RADS™, gerando informações que possam auxiliar o Colégio Brasileiro de Radiologia a desenvolver estratégias para aperfeiçoar o seu uso. MATERIAIS E MÉTODOS: Os dados foram coletados na cidade de Goiânia, GO. Foram solicitados os exames de mamografia anteriores a todas as mulheres que se dirigiram ao serviço para realização de mamografia entre janeiro/2003 e junho/2003. Foram incluídos na análise exames anteriores, realizados entre 1/7/2001 e 30/6/2003. RESULTADOS: Foram coletados 104 laudos anteriores, emitidos por 40 radiologistas de 33 diferentes serviços. Dos 104 laudos, 77% (n = 80) utilizavam o BI-RADS™. Destes, apenas 15% (n = 12) eram concisos, nenhum utilizava a estrutura e organização recomendadas pelo sistema, 98,75% (n = 79) não respeitavam o léxico e 65% (n = 51) não faziam recomendação de conduta. CONCLUSÃO: O BI-RADS™, apesar de bastante utilizado, não foi reconhecido como sistema para padronização dos laudos. Foi usado quase exclusivamente como forma de classificação final dos exames.

Breast Imaging Reporting and Data System - BI-RADS®: valor preditivo positivo das categorias 3, 4 e 5. revisão sistemática da literatura

OBJETIVO: Avaliar artigos, na literatura, que verificam o valor preditivo positivo das categorias 3, 4 e 5 do Breast Imaging Reporting and Data System (BI-RADS®). MATERIAIS E MÉTODOS: Foi realizada pesquisa na base de dados Medline utilizando os termos "predictive value" e "BI-RADS". Foram incluídos 11 artigos nesta revisão. RESULTADOS: O valor preditivo positivo das categorias 3, 4 e 5 variou entre 0% e 8%, 4% e 62%, 54% e 100%, respectivamente. Três artigos avaliaram, concomitantemente, os critérios morfológicos das lesões que apresentaram maior valor preditivo positivo na mamografia, sendo nódulo espiculado o critério com maior valor preditivo positivo. CONCLUSÃO: Houve grande variabilidade do valor preditivo positivo das categorias 3, 4 e 5 do BI-RADS® em todos os estudos, porém foram identificadas diferenças metodológicas que limitaram a comparação desses estudos.

Valor preditivo positivo das categorias 3, 4 e 5 do Breast Imaging Reporting and Data System (BI-RADS®)

OBJETIVO: O objetivo deste trabalho foi avaliar o BI-RADS® como fator preditivo de suspeição de malignidade em lesões mamárias não palpáveis nas categorias 3, 4 e 5, correlacionando as mamografias com os resultados histopatológicos através do cálculo do valor preditivo positivo do exame mamográfico. MATERIAIS E MÉTODOS: Trezentas e setenta e uma pacientes encaminhadas a um serviço de referência em tratamento de câncer em Teresina, PI, para realização de exames histopatológicos em mama no período de julho de 2005 a março de 2008, por terem mamografia de categorias 3, 4 ou 5, tiveram seus exames revisados. Das 371 pacientes, 265 foram submetidas a biópsia por agulha grossa e 106, a marcação pré-cirúrgica. RESULTADOS: Em relação às mamografias, 11,32% foram classificadas como categoria 3, 76,28% como categoria 4 e 12,4% como categoria 5. Os resultados histológicos demonstraram 24% de exames positivos para malignidade. Os valores preditivos positivos das categorias 3, 4 e 5 foram, respectivamente, de 7,14%, 16,96% e 82,61%. Foram calculados os valores preditivos positivos, separadamente, para as biópsias percutâneas (7,14%, 15,76%, 76,47%) e para as marcações pré-cirúrgicas (7,14%, 20%, 100%). CONCLUSÃO: Achados malignos foram subestimados pelo laudo radiológico e houve superestimação de achados benignos, o que resultou na realização desnecessária de alguns procedimentos invasivos.