Functional Biomedical Informatics Research Network Research Articles

Studying time-resolved functional connectivity via communication theory: on the complementary nature of phase synchronization and sliding window Pearson correlation.

Time-resolved functional connectivity (trFC) assesses the time-resolved coupling between brain regions using functional magnetic resonance imaging (fMRI) data. This study aims to compare two techniques used to estimate trFC, to investigate their similarities and differences when applied to fMRI data. These techniques are the sliding window Pearson correlation (SWPC), an amplitude-based approach, and phase synchronization (PS), a phase-based technique. To accomplish our objective, we used resting-state fMRI data from the Human Connectome Project (HCP) with 827 subjects (repetition time: 0.7s) and the Function Biomedical Informatics Research Network (fBIRN) with 311 subjects (repetition time: 2s), which included 151 schizophrenia patients and 160 controls. Our simulations reveal distinct strengths in two connectivity methods: SWPC captures high-magnitude, low-frequency connectivity, while PS detects low-magnitude, high-frequency connectivity. Stronger correlations between SWPC and PS align with pronounced fMRI oscillations. For fMRI data, higher correlations between SWPC and PS occur with matched frequencies and smaller SWPC window sizes (∼30s), but larger windows (∼88s) sacrifice clinically relevant information. Both methods identify a schizophrenia-associated brain network state but show different patterns: SWPC highlights low anti-correlations between visual, subcortical, auditory, and sensory-motor networks, while PS shows reduced positive synchronization among these networks. In sum, our findings underscore the complementary nature of SWPC and PS, elucidating their respective strengths and limitations without implying the superiority of one over the other.

Structural white matter abnormalities in Schizophrenia and associations with neurocognitive performance and symptom severity

Schizophrenia is associated with robust white matter (WM) abnormalities but influences of potentially confounding variables and relationships with cognitive performance and symptom severity remain to be fully determined. This study was designed to evaluate WM abnormalities based on diffusion tensor imaging (DTI) in individuals with schizophrenia, and their relationships with cognitive performance and symptom severity. Data from individuals with schizophrenia (SZ; n=138, mean age±SD=39.02±11.82; 105 males) and healthy controls (HC; n=143, mean age±SD=37.07±10.84; 102 males) were collected as part of the Function Biomedical Informatics Research Network Phase 3 study. Fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) were compared between individuals with schizophrenia and healthy controls, and their relationships with neurocognitive performance and symptomatology assessed. Individuals with SZ had significantly lower FA in forceps minor and the left inferior fronto-occipital fasciculus compared to HC. FA in several tracts were associated with speed of processing and attention/vigilance and the severity of the negative symptom alogia. This study suggests that regional WM abnormalities are fundamentally involved in the pathophysiology of schizophrenia and may contribute to cognitive performance deficits and symptom expression observed in schizophrenia.

DNA Sequence Analysis for Brain Disorder Using Deep Learning and Secure Storage

Analysis of brain disorder in the neuroimaging of Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and Computed Tomography (CT) needs to understand the functionalities of the brain and it has been performed using traditional methods. Deep learning algorithms have also been applied in genomics data processing. The brain disorder diseases of Alzheimer, Schizophrenia, and Parkinson are analyzed in this work. The main issue in the traditional algorithm is the improper detection of disorders in the neuroimaging data. This paper presents a deep learning algorithm for the classification of brain disorder using Deep Belief Network (DBN) and securely storing the image using a Deoxyribonucleic Acid (DNA) Sequence-based Joint Photographic Experts Group (JPEG) Zig Zag Encryption Algorithm (DBNJZZ). In this work, DBNJZZ implements an efficient and effective prediction model for disorders using the open-access datasets of Alzheimer's Disease Neuroimaging Initiative (Adni), the Center for Biomedical Research Excellence (Cobre), the Open Access Series of Imaging Studies (Oasis), the Function Biomedical Informatics Research Network (Fbirn), a Parkinson's dataset of 55 patients and 23 subjects with Parkinson's syndromes (Ntua), and the Parkinson's Progression Markers Initiative (Ppmi). This algorithm is implemented and tested using performance metric measures of accuracy, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). DBNJZZ gives better performance with an accuracy of 99.21% and also surpasses previous methods on other measures.

A novel fuzzy rough selection of non-linearly extracted features for schizophrenia diagnosis using fMRI

Background and objectivesSchizophrenia is a severe brain disorder primarily diagnosed through externally observed behavioural symptoms due to the dearth of established clinical tests. Functional magnetic resonance imaging (fMRI) can capture the distortions caused by schizophrenia in the brain activation. Hence, it can be useful for developing a decision model that performs computer-aided diagnosis of schizophrenia. But, fMRI data is huge in dimension. Therefore dimension reduction is indispensable. It is additionally required to identify the discriminative brain regions. Hence, we aim to build an effective decision model that incorporates suitable dimension reduction and also identifies discriminative brain regions. MethodsWe propose a three-phase dimension reduction. First phase involves spatially-constrained fuzzy clustering of 3-dimensional spatial maps (obtained from general linear model and independent component analysis). In the second phase, non-linear features are extracted from each cluster using a generalized discriminant analysis. In the third phase, a novel fuzzy rough feature selection is proposed. The features obtained after the third phase are used for learning a decision model by the help of support vector machine classifier. This complete method is implemented within leave-one-out cross-validation on two balanced datasets (respectively acquired on 1.5Tesla and 3Tesla scanners). Both these datasets are created using Function Biomedical Informatics Research Network multisite data and contain fMRI data acquired during auditory oddball task performed by age-matched schizophrenia patients and healthy subjects. A permutation test is also carried out to ensure that no bias is involved in the learning. ResultsThe results indicate that the proposed method achieves maximum classification accuracy of 97.1% and 98.0% for the two datasets respectively. The proposed method outperforms the state-of-the-art methods. The results of the permutation test show that p-values are lesser than the significance level i.e. 0.05. Therefore, the classifier has found a significant class structure and does not involve any bias. Further, discriminative brain regions are identified and are in agreement with the findings in related literature. ConclusionThe proposed method is able to derive suitable non-linear features and the related brain regions for effective computer-aided diagnosis. The fuzzy and rough set based approaches help in handling uncertainty and ambiguity in real data.

Predictive Imaging Approach May Identify Severity of Schizophrenia Symptoms

Back to table of contents Previous article Next article Clinical and Research NewsFull AccessPredictive Imaging Approach May Identify Severity of Schizophrenia SymptomsNick ZagorskiNick ZagorskiSearch for more papers by this authorPublished Online:14 Sep 2017https://doi.org/10.1176/appi.pn.2017.8b16AbstractNew analysis of MRI scans suggests that differences in brain connectivity can be used to predict how strongly certain symptoms including apathy and thought disorder will manifest.The ability to accurately predict at-risk individuals who will transition to psychosis from those who will not could help clinicians connect patients to care faster. In recent years, researchers have increasingly turned to neuroimaging technology in search of biomarkers for psychosis. iStock/baranozdemirA study by researchers at the University of Alberta in Edmonton and IBM’s TJ Watson Research Center in Yorktown, N.Y., now suggests that differences in brain connectivity may predict not only which patients have schizophrenia, but also the severity of specific symptoms.These findings, which were published in NPJ Schizophrenia, came from an analysis of functional MRI (fMRI) scans from 95 people, including 46 patients with schizophrenia or schizoaffective disorder and 49 matched controls.The participants underwent the fMRI scans while performing a task known as the Auditory Oddball. This test involves listening to a series of repetitive tones and pressing a button whenever a different-pitched, “oddball” tone is heard. This test was chosen because auditory problems are a common schizophrenia symptom.Using a computational approach known as “link-weighting,” the researchers compared the relative strength of brain activity in patients with schizophrenia with that of controls. Link-weighting involves measuring neural signals (or links) between different clusters in the brain (or nodes).On average, patients with schizophrenia had greater activity in several brain regions than controls, particularly in links connecting the thalamus and motor cortex. Based on the differences in link-weights, the researchers could differentiate patients with schizophrenia from controls with 74 percent accuracy. Other predictive studies using brain scans have reached higher accuracies, but study investigator Guillermo Cecchi, Ph.D., director of the computational psychiatry group at IBM Research, noted previous studies employed a single MRI machine for more consistency. The fMRI images used in this study came from the national Function Biomedical Informatics Research Network Data Repository (fBIRN), which collects scans from a wide range of machines employing different types of imaging software. According to Cecchi, this made comparing images much more challenging.“That we could reach 74 percent accuracy when working across different sites is actually encouraging,” he said. “There is room to improve, but this is a first step to developing a model for prediction that could really be universal.”Cecchi and his colleagues also found they could predict the severity of specific symptoms in the schizophrenia patients using the imaging data. They assessed connectivity patterns in relation to patient scores on nine global assessment scales and found five where link-weighting scores were tied to severity: alogia, apathy, attention, bizarre behavior, and formal thought disorder. In contrast, the severity of affective flattening, anhedonia, hallucinations, and delusions did not appear to be associated with these connectivity patterns.What was interesting, noted Cecchi, is that the neural links that predicted symptom severity were not the same ones that showed the strongest difference between the schizophrenia patients and controls. The areas associated with symptom differences included the sensory cortex and precuneus, a brain region involved in visuospatial processing and self-reflection. Cecchi thinks this speaks to the complexity of schizophrenia, as neural connections that do not appear predictive of a disordered state greatly affect how symptoms manifest.This study was supported by IBM, Alberta Innovates Health Solutions, and the Natural Sciences and Engineering Research Council of Canada. The fBIRN is supported by grants from the National Institutes of Health. ■“Learning Stable and Predictive Network-Based Patterns of Schizophrenia and Its Clinical Symptoms” can be accessed here. ISSUES NewArchived

MR Performance Comparison of a PET/MR System Before and After SiPM-Based Time-of-Flight PET Detector Insertion

A silicon photomultiplier (SiPM)-based time-of-flight capable PET detector has been integrated with a 70 cm wide-bore 3T MR scanner for simultaneous whole-body imaging (MR750w, GE Healthcare, Waukesha, WI). After insertion of the PET detector, the final PET/MR bore is 60 cm wide (SIGNA PET/MR, GE Healthcare, Waukesha, WI). The MR performance was compared before and after the PET ring insertion. ${\rm B}_0$ homogeneity, ${\rm B}_1^ + $ uniformity of the body coil along with peak ${\rm B}_1^ + $ , coherent noise, and FBIRN (Function Biomedical Informatics Research Network) tests are used to compare the MR performance. It is shown that ${\rm B}_0$ homogeneity and coherent noise have not changed according to the system specifications. Peak ${\rm B}_1^ + $ is increased by 33% and ${\rm B}_1^ + $ inhomogeneity is increased by 4% after PET ring insertion due to a smaller diameter body coil design. The FBIRN test shows similar temporal stability before and after PET ring insertion. Due to a smaller body coil on the PET/MR system, the signal fluctuation to noise ratio (SFNR) and SNR for body receive coil, are improved by ${\sim} 40\%$ and ${\sim} 160\%$ for Echo Planar Imaging (EPI) and spiral sequences respectively. Comparison using RF- and gradient-intensive clinical sequences shows inserting the PET detectors into the wide-bore MRI has not compromised the MR image quality according to these tests.

Impairment in subcortical suppression in schizophrenia: Evidence from the fBIRN Oddball Task

Schizophrenia patients show widespread impairments in brain activity during oddball tasks, which involve responding to infrequent target stimuli while refraining from responding during continuous non-target stimuli. In a network-based investigation comparing schizophrenia or schizoaffective patients to healthy controls, we sought to clarify which networks were specifically associated with target detection using a multivariate analysis technique that identifies task-specific functional brain networks. We acquired data from the publicly available function biomedical informatics research network collaboration, including 58 patients and 50 controls. Two task-based functional brain networks were identified: (1) a response modulation network including bilateral temporal pole, supramarginal gyrus, striatum, and thalamus, on which patients showed decreased activity relative to controls; and (2) an auditory-motor response activation network, on which patients showed a slower return to baseline than controls, but no difference in peak activation. For both groups, baseline to peak activation of the response modulation network correlated negatively with peak to baseline activity in the response activation network, suggesting a role in suppressing the motor response following targets. Patients' impaired activity in the response modulation network, and subsequent longer return to baseline in the response activation network, correspond with their later and less accurate behavioral performance, suggesting that impairment in suppression of the auditory-motor response activation network could underlie oddball task deficits in schizophrenia. In addition, the magnitude of the activity in the response modulation network was correlated with intensity of delusions of reference, supporting the notion that increased referential ideation is associated with hyperactivity within the subcortical striatal-limbic network. Hum Brain Mapp 37:4640-4653, 2016. © 2016 Wiley Periodicals, Inc.

The Function Biomedical Informatics Research Network Data Repository

The Function Biomedical Informatics Research Network (FBIRN) developed methods and tools for conducting multi-scanner functional magnetic resonance imaging (fMRI) studies. Method and tool development were based on two major goals: 1) to assess the major sources of variation in fMRI studies conducted across scanners, including instrumentation, acquisition protocols, challenge tasks, and analysis methods, and 2) to provide a distributed network infrastructure and an associated federated database to host and query large, multi-site, fMRI and clinical data sets. In the process of achieving these goals the FBIRN test bed generated several multi-scanner brain imaging data sets to be shared with the wider scientific community via the BIRN Data Repository (BDR). The FBIRN Phase 1 data set consists of a traveling subject study of 5 healthy subjects, each scanned on 10 different 1.5 to 4T scanners. The FBIRN Phase 2 and Phase 3 data sets consist of subjects with schizophrenia or schizoaffective disorder along with healthy comparison subjects scanned at multiple sites. In this paper, we provide concise descriptions of FBIRN's multi-scanner brain imaging data sets and details about the BIRN Data Repository instance of the Human Imaging Database (HID) used to publicly share the data.

Mapping pathological changes in brain structure by combining T1- and T2-weighted MR imaging data.

IntroductionA workflow based on the ratio between standardized T1-weighted (T1-w) and T2-weighted (T2-w) MR images has been proposed as a new tool to study brain structure. This approach was previously used to map structural properties in the healthy brain. Here, we evaluate whether the T1-w/T2-w approach can support the assessment of structural impairments in the diseased brain. We use schizophrenia data to demonstrate the potential clinical utility of the technique.MethodsWe analyzed T1-w and T2-w images of 36 schizophrenic patients and 35 age-matched controls. These were collected for the Function Biomedical Informatics Research Network (fBIRN) collaborative project, which had an IRB approval and followed the HIPAA guidelines. We computed T1-w/T2-w images for each individual and compared intensities in schizophrenic and control groups on a voxel-wise basis, as well as in regions of interest (ROIs).ResultsOur results revealed that the T1-w/T2-w image permits to discriminate brain regions showing group-level differences between patients and controls with greater accuracy than conventional T1-w and T2-w images. Both the ROIs and the voxel-wise analysis showed globally reduced gray and white matter values in patients compared to controls. Significantly reduced values were found in regions such as insula, primary auditory cortex, hippocampus, inferior longitudinal fasciculus, and inferior fronto-occipital fasciculus.ConclusionOur findings were consistent with previous meta-analyses in schizophrenia corroborating the hypothesis of a potential “disconnection” syndrome in conjunction with structural alterations in local gray matter regions. Overall, our study suggested that the T1-w/T2-w technique permits to reliably map structural differences between the brains of patients and healthy individuals.

Modeling fMRI data: challenges and opportunities.

We offer an introduction to the five papers that make up this special section. These papers deal with a range of the methodological challenges that face researchers analyzing fMRI data--the spatial, multilevel, and longitudinal nature of the data, the sources of noise, and so on. The papers all provide analyses of data collected by a multi-site consortium, the Function Biomedical Informatics Research Network. Due to the sheer volume of data, univariate procedures are often applied, which leads to a multiple comparisons problem (since the data are necessarily multivariate). The papers in this section include interesting applications, such as a state-space model applied to these data, and conclude with a reflection on basic measurement problems in fMRI. All in all, they provide a good overview of the challenges that fMRI data present to the standard psychometric toolbox, but also to the opportunities they offer for new psychometric modeling.

State-space analysis of working memory in schizophrenia: an fBIRN study.

The neural correlates of working memory (WM) in schizophrenia (SZ) have been extensively studied using the multisite fMRI data acquired by the Functional Biomedical Informatics Research Network (fBIRN) consortium. Although univariate and multivariate analysis methods have been variously employed to localize brain responses under differing task conditions, important hypotheses regarding the representation of mental processes in the spatio-temporal patterns of neural recruitment and the differential organization of these mental processes in patients versus controls have not been addressed in this context. This paper uses a multivariate state-space model (SSM) to analyze the differential representation and organization of mental processes of controls and patients performing the Sternberg Item Recognition Paradigm (SIRP) WM task. The SSM is able to not only predict the mental state of the subject from the data, but also yield estimates of the spatial distribution and temporal ordering of neural activity, along with estimates of the hemodynamic response. The dynamical Bayesian modeling approach used in this study was able to find significant differences between the predictability and organization of the working memory processes of SZ patients versus healthy subjects. Prediction of some stimulus types from imaging data in the SZ group was significantly lower than controls, reflecting a greater level of disorganization/heterogeneity of their mental processes. Moreover, the changes in accuracy of predicting the mental state of the subject with respect to parametric modulations, such as memory load and task duration, may have important implications on the neurocognitive models for WM processes in both SZ and healthy adults. Additionally, the SSM was used to compare the spatio-temporal patterns of mental activity across subjects, in a holistic fashion and to derive a low-dimensional representation space for the SIRP task, in which subjects were found to cluster according to their diagnosis.

Technical performance evaluation of a human brain PET/MRI system

Technical performance evaluation of a human brain PET/MRI system. The magnetic field compatible positron emission tomography (PET) insert is based on avalanche photodiode (APD) arrays coupled with lutetium oxyorthosilicate (LSO) crystals and slip-fits into a slightly modified clinical 3-T MRI system. The mutual interference between the two imaging techniques was minimised by the careful design of the hardware to maintain the quality of the B (0) and B (1) field homogeneity. The signal-to-noise ratio (SNR) and the homogeneity of the MR images were minimally influenced by the presence of the PET. Measurements according to the Function Biomedical Informatics Research Network (FBIRN) protocol proved the combined system's ability to perform functional MRI (fMRI). The performance of the PET insert was evaluated according to the National Electrical Manufacturers Association (NEMA) standard. The noise equivalent count rate (NEC) peaked at 30.7 × 10(3) counts/s at 7.3 kBq/mL. The point source sensitivity was greater than 7 %. The spatial resolution in the centre field of view was less than 3 mm. Patient data sets clearly revealed a noticeably good PET and MR image quality. PET and MRI phantom tests and first patient data exhibit the device's potential for simultaneous multiparametric imaging. • Combination of PET and MRI is a new emerging imaging technology. • Evaluated brain PET/MRI enables uncompromised imaging performance. • PET/MRI aims to provide multiparametric imaging allowing acquisition of morphology and metabolism.

Function biomedical informatics research network recommendations for prospective multicenter functional MRI studies

This report provides practical recommendations for the design and execution of multicenter functional MRI (MC-fMRI) studies based on the collective experience of the Function Biomedical Informatics Research Network (FBIRN). The study was inspired by many requests from the fMRI community to FBIRN group members for advice on how to conduct MC-fMRI studies. The introduction briefly discusses the advantages and complexities of MC-fMRI studies. Prerequisites for MC-fMRI studies are addressed before delving into the practical aspects of carefully and efficiently setting up a MC-fMRI study. Practical multisite aspects include: (i) establishing and verifying scan parameters including scanner types and magnetic fields, (ii) establishing and monitoring of a scanner quality program, (iii) developing task paradigms and scan session documentation, (iv) establishing clinical and scanner training to ensure consistency over time, (v) developing means for uploading, storing, and monitoring of imaging and other data, (vi) the use of a traveling fMRI expert, and (vii) collectively analyzing imaging data and disseminating results. We conclude that when MC-fMRI studies are organized well with careful attention to unification of hardware, software and procedural aspects, the process can be a highly effective means for accessing a desired participant demographics while accelerating scientific discovery.

A Genome-Wide Association Study of Schizophrenia Using Brain Activation as a Quantitative Phenotype

Genome-wide association studies (GWASs) are increasingly used to identify risk genes for complex illnesses including schizophrenia. These studies may require thousands of subjects to obtain sufficient power. We present an alternative strategy with increased statistical power over a case-control study that uses brain imaging as a quantitative trait (QT) in the context of a GWAS in schizophrenia. Sixty-four subjects with chronic schizophrenia and 74 matched controls were recruited from the Functional Biomedical Informatics Research Network (FBIRN) consortium. Subjects were genotyped using the Illumina HumanHap300 BeadArray and were scanned while performing a Sternberg Item Recognition Paradigm in which they learned and then recognized target sets of digits in an functional magnetic resonance imaging protocol. The QT was the mean blood oxygen level-dependent signal in the dorsolateral prefrontal cortex during the probe condition for a memory load of 3 items. Three genes or chromosomal regions were identified by having 2 single-nucleotide polymorphisms (SNPs) each significant at P < 10(-6) for the interaction between the imaging QT and the diagnosis (ROBO1-ROBO2, TNIK, and CTXN3-SLC12A2). Three other genes had a significant SNP at <10(-6) (POU3F2, TRAF, and GPC1). Together, these 6 genes/regions identified pathways involved in neurodevelopment and response to stress. Combining imaging and genetic data from a GWAS identified genes related to forebrain development and stress response, already implicated in schizophrenic dysfunction, as affecting prefrontal efficiency. Although the identified genes require confirmation in an independent sample, our approach is a screening method over the whole genome to identify novel SNPs related to risk for schizophrenia.

Voxel-based Morphometric Multisite Collaborative Study on Schizophrenia

Regional gray matter (GM) abnormalities are well known to exist in patients with chronic schizophrenia. Voxel-based morphometry (VBM) has been previously used on structural magnetic resonance images (MRI) data to characterize these abnormalities. Two multisite schizophrenia studies, the Functional Biomedical Informatics Research Network and the Mind Clinical Imaging Consortium, which include 9 data collection sites, are evaluating the efficacy of pooling structural imaging data across imaging centers. Such a pooling of data could yield the increased statistical power needed to elucidate effects that may not be seen with smaller samples. VBM analyses were performed to evaluate the consistency of patient versus control gray matter concentration (GMC) differences across the study sites, as well as the effects of combining multisite data. Integration of data from both studies yielded a large sample of 503 subjects, including 266 controls and 237 patients diagnosed with schizophrenia, schizoaffective or schizophreniform disorder. The data were analyzed using the combined sample, as well as analyzing each of the 2 multisite studies separately. A consistent pattern of reduced relative GMC in schizophrenia patients compared with controls was found across all study sites. Imaging center-specific effects were evaluated using a region of interest analysis. Overall, the findings support the use of VBM in combined multisite studies. This analysis of schizophrenics and controls from around the United States provides continued supporting evidence for GM deficits in the temporal lobes, anterior cingulate, and frontal regions in patients with schizophrenia spectrum disorders.

Medical Imaging

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