Statistical Parametric Mapping Toolbox Research Articles

Examining differences in brain metabolism associated with childhood maltreatment and suicidal attempt in euthymic patients with bipolar disorder: A PET and Machine Learning Study.

Childhood maltreatment (CM) is a significant risk factor for the development and severity of bipolar disorder (BD) with increased risk of suicide attempts (SA). This study evaluated whether a machine learning algorithm could be trained to predict if a patient with BD has a history of CM or previous SA based on brain metabolism measured by positron emission tomography. Thirty-six euthymic patients diagnosed with BD type I, with and without a history of CM were assessed using the Childhood Trauma Questionnaire. Suicide attempts were assessed through the Mini International Neuropsychiatric Interview (MINI-Plus) and a semi-structured interview. Resting-state positron emission tomography with 18F-fluorodeoxyglucose was conducted, electing only grey matter voxels through the Statistical Parametric Mapping toolbox. Imaging analysis was performed using a supervised machine learning approach following Gaussian Process Classification. Patients were divided into 18 participants with a history of CM and 18 participants without it, along with 18 individuals with previous SA and 18 individuals without such history. The predictions for CM and SA were not significant (accuracy = 41.67%; p = 0.879). Further investigation is needed to improve the accuracy of machine learning, as its predictive qualities could potentially be highly useful in determining histories and possible outcomes of high-risk psychiatric patients.

LeGUI: A Fast and Accurate Graphical User Interface for Automated Detection and Anatomical Localization of Intracranial Electrodes.

Accurate anatomical localization of intracranial electrodes is important for identifying the seizure foci in patients with epilepsy and for interpreting effects from cognitive studies employing intracranial electroencephalography. Localization is typically performed by coregistering postimplant computed tomography (CT) with preoperative magnetic resonance imaging (MRI). Electrodes are then detected in the CT, and the corresponding brain region is identified using the MRI. Many existing software packages for electrode localization chain together separate preexisting programs or rely on command line instructions to perform the various localization steps, making them difficult to install and operate for a typical user. Further, many packages provide solutions for some, but not all, of the steps needed for confident localization. We have developed software, Locate electrodes Graphical User Interface (LeGUI), that consists of a single interface to perform all steps needed to localize both surface and depth/penetrating intracranial electrodes, including coregistration of the CT to MRI, normalization of the MRI to the Montreal Neurological Institute template, automated electrode detection for multiple types of electrodes, electrode spacing correction and projection to the brain surface, electrode labeling, and anatomical targeting. The software is written in MATLAB, core image processing is performed using the Statistical Parametric Mapping toolbox, and standalone executable binaries are available for Windows, Mac, and Linux platforms. LeGUI was tested and validated on 51 datasets from two universities. The total user and computational time required to process a single dataset was approximately 1 h. Automatic electrode detection correctly identified 4362 of 4695 surface and depth electrodes with only 71 false positives. Anatomical targeting was verified by comparing electrode locations from LeGUI to locations that were assigned by an experienced neuroanatomist. LeGUI showed a 94% match with the 482 neuroanatomist-assigned locations. LeGUI combines all the features needed for fast and accurate anatomical localization of intracranial electrodes into a single interface, making it a valuable tool for intracranial electrophysiology research.

Prefrontal Cortical Activation While Doing Mindfulness Task: a Pilot Functional Near-Infrared Spectroscopy Study.

Mindfulness interventions are increasingly used in clinical settings. Neurophysiological mechanisms underlying mindfulness offer objective evidence that can help us evaluate the efficacy of mindfulness. Recent advances in technology have facilitated the use of functional Near-Infrared Spectroscopy (fNIRS) as a light weight, portable, and relatively lower cost neuroimaging device as compared to functional Magnetic Resonance Imaging (fMRI). In contrast to numerous fMRI studies, there are scanty investigations using fNIRS to study mindfulness. Hence, this study was done to investigate the feasibility of using a continuous-wave multichannel fNIRS system to study cerebral cortex activations on a mindfulness task versus a baseline task. NIRS data from 14 healthy Asian subjects were collected. A statistical parametric mapping toolbox specific for statistical analysis of NIRS signal called NIRS_SPM was used to study the activations. The results from group analysis performed on the contrast of the mindfulness versus baseline tasks showed foci of activations on the left and central parts of the prefrontal cortex. The findings are consistent with prevailing fMRI studies and show promise of using fNIRS system for studying real-time neurophysiological cortical activations during mindfulness practice.

A Method to Estimate Brain Volume from Head CT Images and Application to Detect Brain Atrophy in Alzheimer Disease.

Total brain volume and total intracranial volume are important measures for assessing whole-brain atrophy in Alzheimer disease, dementia, and other neurodegenerative diseases. Unlike MR imaging, which has a number of well-validated fully-automated methods, only a handful of methods segment CT images. Available methods either use enhanced CT, do not estimate both volumes, or require formal validation. Reliable computation of total brain volume and total intracranial volume from CT is needed because head CTs are more widely used than head MRIs in the clinical setting. We present an automated head CT segmentation method (CTseg) to estimate total brain volume and total intracranial volume. CTseg adapts a widely used brain MR imaging segmentation method from the Statistical Parametric Mapping toolbox using a CT-based template for initial registration. CTseg was tested and validated using head CT images from a clinical archive. CTseg showed excellent agreement with 20 manually segmented head CTs. The intraclass correlation was 0.97 (P < .001) for total intracranial volume and 0.94 (P < .001) for total brain volume. When CTseg was applied to a cross-sectional Alzheimer disease dataset (58 with Alzheimer disease patients and 58 matched controls), CTseg detected a loss in percentage total brain volume (as a percentage of total intracranial volume) with age (P < .001) as well as a group difference between patients with Alzheimer disease and controls (P < .01). We observed similar results when total brain volume was modeled with total intracranial volume as a confounding variable. In current clinical practice, brain atrophy is assessed by inaccurate and subjective "eyeballing" of CT images. Manual segmentation of head CT images is prohibitively arduous and time-consuming. CTseg can potentially help clinicians to automatically measure total brain volume and detect and track atrophy in neurodegenerative diseases. In addition, CTseg can be applied to large clinical archives for a variety of research studies.

Controlling for lesions, kinematics and physiological noise: impact on fMRI results of spastic post-stroke patients

Functional magnetic resonance imaging (fMRI) is a widely used technique for assessing brain function in both healthy and pathological populations. Some factors, such as motion, physiological noise and lesion presence, can contribute to signal change and confound the fMRI data, but fMRI data processing techniques have been developed to correct for these confounding effects. Fifteen spastic subacute stroke patients underwent fMRI while performing a highly controlled task (i.e. passive extension of their affected and unaffected wrists). We investigated the impact on activation maps of lesion masking during preprocessing and first- and second-level analyses, and of adding wrist extension amplitudes and physiological data as regressors using the Statistical Parametric Mapping toolbox (SPM12).We observed a significant decrease in sensorimotor region activation after the addition of lesion masks and movement/physiological regressors during the processing of stroke patients’ fMRI data. Our results demonstrate that:•The unified segmentation routine results in good normalization accuracy when dealing with stroke lesions regardless of their size;•Adding a group lesion mask during the second-level analysis seems to be a suitable option when none of the patients have lesions in target regions. Otherwise, no masking is acceptable;•Movement amplitude is a significant contributor to the sensorimotor activation observed during passive wrist extension in spastic stroke patients;•Movement features and physiological noise are relevant factors when interpreting for sensorimotor activation in studies of the motor system in patients with brain lesions. They can be added as nuisance covariates during large patient groups’ analyses.

MACS – a new SPM toolbox for model assessment, comparison and selection

BackgroundIn cognitive neuroscience, functional magnetic resonance imaging (fMRI) data are widely analyzed using general linear models (GLMs). However, model quality of GLMs for fMRI is rarely assessed, in part due to the lack of formal measures for statistical model inference. New methodWe introduce a new SPM toolbox for model assessment, comparison and selection (MACS) of GLMs applied to fMRI data. MACS includes classical, information-theoretic and Bayesian methods of model assessment previously applied to GLMs for fMRI as well as recent methodological developments of model selection and model averaging in fMRI data analysis. ResultsThe toolbox – which is freely available from GitHub – directly builds on the Statistical Parametric Mapping (SPM) software package and is easy-to-use, general-purpose, modular, readable and extendable. We validate the toolbox by reproducing model selection and model averaging results from earlier publications. Comparison with existing methodsA previous toolbox for model diagnosis in fMRI has been discontinued and other approaches to model comparison between GLMs have not been translated into reusable computational resources in the past. ConclusionsIncreased attention on model quality will lead to lower false-positive rates in cognitive neuroscience and increased application of the MACS toolbox will increase the reproducibility of GLM analyses and is likely to increase the replicability of fMRI studies.

Neonatal Brain Tissue Classification with Morphological Adaptation and Unified Segmentation.

Measuring the distribution of brain tissue types (tissue classification) in neonates is necessary for studying typical and atypical brain development, such as that associated with preterm birth, and may provide biomarkers for neurodevelopmental outcomes. Compared with magnetic resonance images of adults, neonatal images present specific challenges that require the development of specialized, population-specific methods. This paper introduces MANTiS (Morphologically Adaptive Neonatal Tissue Segmentation), which extends the unified segmentation approach to tissue classification implemented in Statistical Parametric Mapping (SPM) software to neonates. MANTiS utilizes a combination of unified segmentation, template adaptation via morphological segmentation tools and topological filtering, to segment the neonatal brain into eight tissue classes: cortical gray matter, white matter, deep nuclear gray matter, cerebellum, brainstem, cerebrospinal fluid (CSF), hippocampus and amygdala. We evaluated the performance of MANTiS using two independent datasets. The first dataset, provided by the NeoBrainS12 challenge, consisted of coronal T2-weighted images of preterm infants (born ≤30 weeks' gestation) acquired at 30 weeks' corrected gestational age (n = 5), coronal T2-weighted images of preterm infants acquired at 40 weeks' corrected gestational age (n = 5) and axial T2-weighted images of preterm infants acquired at 40 weeks' corrected gestational age (n = 5). The second dataset, provided by the Washington University NeuroDevelopmental Research (WUNDeR) group, consisted of T2-weighted images of preterm infants (born <30 weeks' gestation) acquired shortly after birth (n = 12), preterm infants acquired at term-equivalent age (n = 12), and healthy term-born infants (born ≥38 weeks' gestation) acquired within the first 9 days of life (n = 12). For the NeoBrainS12 dataset, mean Dice scores comparing MANTiS with manual segmentations were all above 0.7, except for the cortical gray matter for coronal images acquired at 30 weeks. This demonstrates that MANTiS' performance is competitive with existing techniques. For the WUNDeR dataset, mean Dice scores comparing MANTiS with manually edited segmentations demonstrated good agreement, where all scores were above 0.75, except for the hippocampus and amygdala. The results show that MANTiS is able to segment neonatal brain tissues well, even in images that have brain abnormalities common in preterm infants. MANTiS is available for download as an SPM toolbox from http://developmentalimagingmcri.github.io/mantis.

Voxel-Based Diagnosis of Alzheimer's Disease Using Classifier Ensembles.

Functional magnetic resonance imaging (fMRI) is one of the most promising noninvasive techniques for early Alzheimer's disease (AD) diagnosis. In this paper, we explore the application of different machine learning techniques to the classification of fMRI data for this purpose. The functional images were first preprocessed using the statistical parametric mapping toolbox to output individual maps of statistically activated voxels. A fast filter was applied afterwards to select voxels commonly activated across demented and nondemented groups. Four feature ranking selection techniques were embedded into a wrapper scheme using an inner-outer loop for the selection of relevant voxels. The wrapper approach was guided by the performance of six pattern recognition models, three of which were ensemble classifiers based on stochastic searches. Final classification performance was assessed from the nested internal and external cross-validation loops taking several voxel sets ordered by importance. Numerical performance was evaluated using statistical tests, and the best combination of voxel selection and classification reached a 97.14% average accuracy. Results repeatedly pointed out Brodmann regions with distinct activation patterns between demented and nondemented profiles, indicating that the machine learning analysis described is a powerful method to detect differences in several brain regions between both groups.

Effect of White Matter Hyperintensity on Medial Temporal Lobe Atrophy in Alzheimer’s Disease

Background: Medial temporal atrophy (MTA) is a recognized marker of Alzheimer’s disease (AD), and white matter hyperintensities (WMH) are frequently observed on MRI of AD. The purpose of this study was to understand the role of WMH in MTA. Methods: Subjects were 94 probable AD patients and 51 cognitively normal subjects. WMH was assessed based on the severity of deep WMH (DWMH) and periventricular WMH (PWMH). Each structural volume was evaluated using the Individual Brain Atlases from the Statistical Parametric Mapping Toolbox. Results: There were no significant differences between subjects with and without WMH in terms of general cognitive function scales. Subjects with AD with WMH had decreased volume in the bilateral orbital frontal gyrus, frontal rectus gyrus, and olfactory gyrus, but not in the medial temporal lobes. After correcting for differences in DWMH, age and Clinical Dementia Rating Scale (CDR), AD with PWMH showed decreased volumes in the bilateral hippocampi. AD with PWMH showed worse scores on the Clinical Dementia Rating-Sum of Boxes and Barthel-ADL, and some frontal executive function tests. Those with DWMH did not show any reductions in the medial temporal lobes. Conclusion: WMH in AD is not associated with medial temporal lobe atrophy, but PWMH is independently correlated with hippocampal volume reduction.

Human brain atlas‐based volumetry and relaxometry: Application to healthy development and natural aging

This work investigated the feasibility and utility of using T2-weighted or dual spin-echo data to provide volume and T2 relaxation time for regional and global gray and white matter using standardized brain templates and anatomically labeled atlases. A total of 130 healthy males and females (age range 15-59 years) were included. Dual echo magnetic resonance imaging data were acquired and analyzed using standardized International Consortium for Human Brain Mapping atlas-based tissue segmentation procedures implemented in the statistical parametric mapping toolbox to obtain the age trajectories of volume and corresponding T2 relaxation time in whole brain white and gray matter, in the caudate nucleus and in the anterior limb of internal capsule. Whole brain gray matter and caudate nucleus volumes linearly decreased with age while whole brain white matter and anterior limb internal capsule volume increased slowly with age bilaterally in males and females. The relation between T2 relaxation time and age of whole brain gray and white matter, and caudate nucleus and anterior limb internal capsule followed a quadratic U-curve. The T2 relaxation times of the human brain followed a U curve both globally and regionally in both white and gray matter.

The differences in brain mechanism for motor inhibition in patients with cerebral palsy: an fMRI study

Event Abstract Back to Event The differences in brain mechanism for motor inhibition in patients with cerebral palsy: an fMRI study H.J Park1*, B.S Seo1 and J.D Lee1 1 Yonsei University, South Korea Cerebral palsy (CP) is one of the most developmental neuropathies occured during the delivery. Even there was great advance in prevention and treatment of CP, the result of this disorder needs life-long care. Most of CP was caused by periventricular leukomalacia(PVL) with vast neuronal cell death around ventricular region. Even brain damage causes wide cognitive deficit in movement, however, recent research did not find etiological factors of this damage. Movement inhibition is one of the core factors in regulating movement. Among various region for this mechanism, SMA is well known for regulating error-related response and movement control. For investigating the functional feature on this area, Go-Nogo task was widely used in many previous reports. Using one nine-minute, event-related Go-Nogo taks, we performed functional magnetic resonance imaging study between patients with cerebral palsy and healthy controls Functional images were obtained using high-resolution 3T MRI scanner (IntraAchieva, Phillips medical system, Best, The Netherlands). Acquired images underwent preprocessing and analysis using statistical parametric mapping toolbox (SPM2, Welcome Department of Cognitive Neurology, UCL, UK). After individual analysis, we performed t tests for group analysis. Also we measured roi-based percentage of signal change in the brain area, which had shown difference in brain activity between two groups For the result, we found the difference in brain activation on bilateral supplementary motor area (BA 6) and anterior cingulate cortex (BA 32) during Nogo vs. Go condition. Also there was negative BOLD signal on these areas In conclusion, the differences in the brain activation are considered as a result of brain damage. Especially, the negative BOLD signal was known to be related with GABA-ergic effect on the brain areas. Therefore, these results will be thought to have reationship with behavioral disorder in patients with cerebral palsy. For the further study, biological markups on these areas using other neuroimaging techniques are required. Conference: 10th International Conference on Cognitive Neuroscience, Bodrum, Turkey, 1 Sep - 5 Sep, 2008. Presentation Type: Poster Presentation Topic: Neuropsychiatric Disorders Citation: Park H, Seo B and Lee J (2008). The differences in brain mechanism for motor inhibition in patients with cerebral palsy: an fMRI study. Conference Abstract: 10th International Conference on Cognitive Neuroscience. doi: 10.3389/conf.neuro.09.2009.01.390 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 17 Dec 2008; Published Online: 17 Dec 2008. * Correspondence: H.J Park, Yonsei University, Seoul, South Korea, seo307@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers H.J Park B.S Seo J.D Lee Google H.J Park B.S Seo J.D Lee Google Scholar H.J Park B.S Seo J.D Lee PubMed H.J Park B.S Seo J.D Lee Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Functional optical signal analysis: a software tool for near-infrared spectroscopy data processing incorporating statistical parametric mapping

Optical topography (OT) relies on the near infrared spectroscopy (NIRS) technique to provide noninvasively a spatial map of functional brain activity. OT has advantages over conventional fMRI in terms of its simple approach to measuring the hemodynamic response, its ability to distinguish between changes in oxy- and deoxy-hemoglobin and the range of human participants that can be readily investigated. We offer a new software tool, functional optical signal analysis (fOSA), for analyzing the spatially resolved optical signals that provides statistical inference capabilities about the distribution of brain activity in space and time and by experimental condition. It does this by mapping the signal into a standard functional neuroimaging analysis software, statistical parametric mapping (SPM), and forms, in effect, a new SPM toolbox specifically designed for NIRS in an OT configuration. The validity of the program has been tested using synthetic data, and its applicability is demonstrated with experimental data.

Nonstationary noise estimation in functional MRI

An important issue in functional MRI analysis is accurate characterisation of the noise processes present in the data. Whilst conventional fMRI noise representations often assume stationarity (or time-invariance) in the noise generating sources, such approaches may serve to suppress important dynamic information about brain function. As an alternative to these fixed temporal assumptions, we present in this paper two time-varying procedures for examining nonstationary noise structure in fMRI data. In the first procedure, we approximate nonstationary behaviour by means of a collection of simple but numerous time-varying parametric models. This is accomplished through the derivation of a locally parametric AutoRegressive (AR) plus drift model which tracks temporal covariance by allowing the model parameters to evolve over time. Before exploring time variation in these parameters, window-widths (bandwidths) that are well suited to the latent time-varying noise structure must be determined. To do this, we employ a bandwidth selection mechanism based on Stein's Unbiased Risk Estimator (SURE) criterion. In the second procedure, we describe the fMRI noise using a nonparametric method based on Functional Data Analysis (FDA). This process generates well-conditioned nonstationary covariance estimates that reflect temporal continuity in the underlying data structure whilst penalizing effective model dimension. We demonstrate both methods on simulated data and investigate the presence of nonstationary noise in resting fMRI data using the whitening capabilities of the locally parametric procedure. We evaluate the comparative behaviour of the stationary and nonstationary AR-based methods on data acquired at 1.5, 3 and 7 T magnetic field strengths and show that incorporation of time variation in the AR parameters leads to an overall decrease in the level of residual structure in the data. The FDA noise modelling technique is formulated within an activation mapping procedure and compared to the SPM (Statistical Parametric Mapping) toolbox on a cognitive face recognition task. Both the SPM and FDA methods show good sensitivity on this task, but we find that inclusion of the nonstationary FDA noise model seems to improve detection power in important task-related medial temporal regions.