Decoding different working memory states during an operation span task from prefrontal fNIRS signals

We propose an effective and practical decoding method of different mental states for potential applications for the design of brain-computer interfaces, prediction of cognitive behaviour, and investigation of cognitive mechanism. Functional near infrared spectroscopy (fNIRS) signals that interrogated the prefrontal and parietal cortices and were evaluated by generalized linear model were recorded when nineteen healthy adults performed the operation span (OSPAN) task. The oxygenated hemoglobin changes during OSPAN, response, and rest periods were classified with a support vector machine (SVM). The relevance vector regression algorithm was utilized for prediction of cognitive performance based on multidomain features of fNIRS signals from the OSPAN task. We acquired decent classification accuracies for OSPAN vs. response (above 91.2%) and for OSPAN vs. rest (above 94.7%). Eight of the ten cognitive testing scores could be predicted from the combination of OSPAN and response features, which indicated the brain hemodynamic responses contain meaningful information suitable for predicting cognitive performance.     

Predicting treatment response in depressed and non-depressed chronic pain patients

Although it has often been observed that chronic pain and depression are associated, there have been few systematic comparisons of chronic pain patients with and without depression. In the study reported in this article, depressed and non-depressed chronic pain patients were found to be quite similar with respect to demographic, pain-related, and treatment response variables. The primary aim of the study, however, was to examine the hypothesis that treatment response in these two groups of patients would be predicted by different patterns of variables. In non-depressed patients, beneficial response to treatment was related to a greater number of treatment visits, not receiving workmen's compensation, fewer previous types of treatment, and low back pain. As predicted, a different pattern of predictors of treatment response was found for the depressed patients, who were more likely to benefit when they were employed at the beginning of treatment and when their pain was of shorter duration. These results suggest that activity and active involvement in treatment are particularly important with chronic pain patients who are depressed. In addition, they suggest that the best prediction of treatment response in future research on chronic pain patients may be achieved by dividing patients into groups based on psychological characteristics.     

Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface

There has been an increase in research interest for brain-computer interface (BCI) technology as an alternate mode of communication and environmental control for the disabled, such as patients suffering from amyotrophic lateral sclerosis (ALS), brainstem stroke and spinal cord injury. Disabled patients with appropriate physical care and cognitive ability to communicate with their social environment continue to live with a reasonable quality of life over extended periods of time. Near-infrared spectroscopy is a non-invasive technique which utilizes light in the near-infrared range (700 to 1000 nm) to determine cerebral oxygenation, blood flow and metabolic status of localized regions of the brain. In this paper, we describe a study conducted to test the feasibility of using multichannel NIRS in the development of a BCI. We used a continuous wave 20-channel NIRS system over the motor cortex of 5 healthy volunteers to measure oxygenated and deoxygenated hemoglobin changes during left-hand and right-hand motor imagery. We present results of signal analysis indicating that there exist distinct patterns of hemodynamic responses which could be utilized in a pattern classifier towards developing a BCI. We applied two different pattern recognition algorithms separately, Support Vector Machines (SVM) and Hidden Markov Model (HMM), to classify the data offline. SVM classified left-hand imagery from right-hand imagery with an average accuracy of 73% for all volunteers, while HMM performed better with an average accuracy of 89%. Our results indicate potential application of NIRS in the development of BCIs. We also discuss here future extension of our system to develop a word speller application based on a cursor control paradigm incorporating online pattern classification of single-trial NIRS data.     

MRI纹理分析评价注意缺陷多动障碍

目的 观察MRI纹理分析诊断注意缺陷多动障碍(ADHD)及分型的效果.方法 基于纽约大学医学中心公开MRI数据选取88例ADHD患者(ADHD组)及67名健康受试者(对照组),将ADHD组分为注意力缺陷为主型(ADHD-D亚组(n=32)和混合型(ADHD-C)亚组(n=56),提取并比较受试者脑白质和脑灰质的纹理特征...     

Rapid detection of hysteromyoma and cervical cancer based on serum surface-enhanced Raman spectroscopy and a support vector machine

In this study, we investigated the feasibility of using surface-enhanced Raman spectroscopy (SERS) combined with a support vector machine (SVM) algorithm to discriminate hysteromyoma and cervical cancer from healthy volunteers rapidly. SERS spectra of serum samples were recorded from 30 hysteromyoma patients, 36 cervical cancer patients as well as 30 healthy subjects. SVM was used to establish the classification models, and three types of kernel functions, namely linear, polynomial, and Gaussian radial basis function (RBF), were utilized for comparison. When the polynomial kernel function was employed, the overall diagnostic accuracy for classifying the three groups could achieve 86.5%. In addition, when the optimal kernel function was selected, the diagnostic accuracy for identifying healthy versus hysteromyoma, healthy versus cervical cancer, and hysteromyoma versus cervical cancer reached 98.3%, 93.9%, and 90.9%, respectively. The current results indicate that serum SERS technology, together with the SVM algorithm, is expected to become a clinical tool for rapid screening of hysteromyoma and cervical cancer.     

Depression assessment after traumatic brain injury: an empirically based classification method

OBJECTIVES: To describe the patterns of depression in patients with traumatic brain injury (TBI), to evaluate the psychometric properties of the Neurobehavioral Functioning Inventory (NFI) Depression Scale, and to classify empirically NFI Depression Scale scores. DESIGN: Depressive symptoms were characterized by using the NFI Depression Scale, the Beck Depression Inventory (BDI), and the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) Depression Scale. SETTING: An outpatient clinic within a Traumatic Brain Injury Model Systems center. PARTICIPANTS: A demographically diverse sample of 172 outpatients with TBI, evaluated between 1996 and 2000. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: The NFI, BDI, and MMPI-2 Depression Scale. The Cronbach alpha, analysis of variance, Pearson correlations, and canonical discriminant function analysis were used to examine the psychometric properties of the NFI Depression Scale. RESULTS: Patients with TBI most frequently reported problems with frustration (81%), restlessness (73%), rumination (69%), boredom (66%), and sadness (66%) with the NFI Depression Scale. The percentages of patients classified as depressed with the BDI and the NFI Depression Scale were 37% and 30%, respectively. The Cronbach alpha for the NFI Depression Scale was.93, indicating a high degree of internal consistency. As hypothesized, NFI Depression Scale scores correlated highly with BDI (r=.765) and MMPI-2 Depression Scale T scores (r=.752). The NFI Depression Scale did not correlate significantly with the MMPI-2 Hypomania Scale, thus showing discriminant validity. Normal and clinically depressed BDI scores were most likely to be accurately predicted by the NFI Depression Scale, with 81% and 87% of grouped cases, respectively, correctly classified. Normal and depressed MMPI-2 Depression Scale scores were accurately predicted by the NFI Depression Scale, with 75% and 83% of grouped cases correctly classified, respectively. Patients' NFI Depression Scale scores were mapped to the corresponding BDI categories, and 3 NFI score classifications emerged: minimally depressed (13-28), borderline depressed (29-42), and clinically depressed (43-65). CONCLUSIONS: Our study provided further evidence that screening for depression should be a standard component of TBI assessment protocols. Between 30% and 38% of patients with TBI were classified as depressed with the NFI Depression Scale and the BDI, respectively. Our findings also provided empirical evidence that the NFI Depression Scale is a useful tool for classifying postinjury depression.     

Prognostic prediction of therapeutic response in depression using high-field MR imaging

Despite significant advances in the treatment of major depression, there is a high degree of variability in how patients respond to treatment. Approximately 70% of patients show some improvement following standard antidepressant treatment and are classified as having non-refractory depressive disorder (NDD), while the remaining 30% of patients do not respond to treatment and are classified as having refractory depressive disorder (RDD). At present, there are no objective, neurological markers which can be used to identify individuals with depression and predict clinical outcome. We therefore examined the diagnostic and prognostic potential of pre-treatment structural neuroanatomy using support vector machine (SVM). Sixty-one drug-na?ve adults suffering from depression and 42 healthy volunteers were scanned using structural magnetic resonance imaging (sMRI). Patients then received standard antidepressant medication (either tricyclic, typical serotonin-norepinephrine reuptake inhibitor or typical selective serotonin reuptake inhibitor). Based on clinical outcome, we selected two groups of RDD (n=23) and NDD (n=23) patients matched for age, sex and pre-treatment severity of depression. Diagnostic accuracy of gray matter was 67.39% for RDD (p=0.01) and 76.09% for NDD (p<0.001), while diagnostic accuracy of white matter was 58.70% for RDD (p=0.13) and 84.65% for NDD (p<0.001). SVM applied to gray matter correctly distinguished between RDD and NDD patients with an accuracy of 69.57% (p=0.006); in contrast, SVM applied to white matter predicted clinical outcome with an accuracy of 65.22% (p=0.02). These results indicate that both gray and white matter have diagnostic and prognostic potential in major depression and may provide an initial step towards the use of biological markers to inform clinical treatment. Future studies will benefit from the integration of structural neuroimaging with other imaging modalities as well as genetic, clinical and cognitive information.     

抑郁症功能磁共振成像脑网络研究进展

近年来,功能磁共振成像技术的发展使得研究者可以无创地研究活体脑组织结构和功能特点,结合多种脑成像技术研究大尺度的脑结构和功能网络,为研究某些疾病的发生和发展机制提供了可靠的方法。目前抑郁症患者的磁共振脑网络研究还处在初步探索阶段,脑网络的拓扑属性在某种程度上对抑郁症患者的早期诊断和鉴别诊断起到一定的辅助作用,同时也可以作为一个衡量抑郁症严重程度的生理指标。脑结构和功能的异常模式也可作为敏感特征用于诊断相关脑疾病,因此本文将从抑郁症层面综述几个主要的磁共振脑网络的结构和功能方面的研究成果。     

抑郁症患者交感神经皮肤反应与事件相关电位P300的探讨

目的探讨交感神经皮肤反应（SSR）与事件相关电位P300对抑郁症患者的诊断价值。方法本研究对46例抑郁症患者（抑郁症组）及42例正常健康者（正常对照组）分别进行SSR及事件相关电位P300检测，并对其结果进行比较，同时对各指标间的相关性进行分析。结果抑郁症组患者中SSR和事件相关电位P300检测的异常率分别为84．8％（39／46）和89．1％（41／46），两者间异常吻合率为78．3％（36／46）。抑郁症组患者通过SSR测定后发现，其SSR潜伏期和波幅值分别较正常对照组延长和降低，差异具有统计学意义（P〈0．01）；抑郁症组患者经事件相关电位测定后发现，其N2、P3波潜伏期和P3波幅分别较正常对照组延长和降低，差异亦具有统计学意义（P〈0．01）。进一步分析后发现，事件相关电位成分中N2、P3波潜伏期与SSR潜伏期间以及P3波波幅与SSR波幅间均呈正相关（P〈0．01），而N2、P3波潜伏期与SSR波幅间以及P3波波幅与SSR潜伏期间均呈负相关（P〈0．01）。结论交感神经皮肤反应和事件相关电位P300可作为抑郁症患者的辅助诊断指标应用于临床实践中。     

Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI

Recently, a functional disconnectivity hypothesis of schizophrenia has been proposed for the physiological explanation of behavioral syndromes of this complex mental disorder. In this paper, we aim at further examining whether syndromes of schizophrenia could be decoded by some special spatiotemporal patterns of resting-state functional connectivity. We designed a data-driven classifier based on machine learning to extract highly discriminative functional connectivity features and to discriminate schizophrenic patients from healthy controls. The proposed classifier consisted of two separate steps. First, we used feature selection based on a correlation coefficient method to extract highly discriminative regions and construct the optimal feature set for classification. Then, an unsupervised-learning classifier combining low-dimensional embedding and self-organized clustering of fMRI was trained to discriminate schizophrenic patients from healthy controls. The performance of the classifier was tested using a leave-one-out cross-validation strategy. The experimental results demonstrated not only high classification accuracy (93.75% for schizophrenic patients, 75.0% for healthy controls), but also good generalization and stability with respect to the number of extracted features. In addition, some functional connectivities between certain brain regions of the cerebellum and frontal cortex were found to exhibit the highest discriminative power, which might provide further evidence for the cognitive dysmetria hypothesis of schizophrenia. This primary study demonstrated that machine learning could extract exciting new information from the resting-state activity of a brain with schizophrenia, which might have potential ability to improve current diagnosis and treatment evaluation of schizophrenia.     

Cognitive control and brain resources in major depression: an fMRI study using the n-back task

Several neuroimaging studies have reported 'hypofrontality' in depressed patients performing a cognitive challenge compared to control subjects. Hypofrontality in depression is likely associated with an impaired behavioral performance. It is unclear whether this impaired performance is the consequence or the cause of hypofrontality. Consequently, we proposed to compare the cerebral activity of depressed patients and healthy subjects while controlling for the level of performance. Ten individuals meeting DSM-IV criteria for Major Depression and 10 healthy controls were tested with a verbal version of the n-back task during fMRI scanning. The working memory load was manipulated across the experiment (1,2,3-back) to increase the cognitive demands. fMRI data were acquired on a 1.5-T GE scanner and analyzed using SPM99 software. We did not find any difference between groups in both performance and reaction times for each level of complexity of the n-back task. Depressed patients and control subjects showed bilateral activation of the lateral prefrontal cortex, anterior cingulate and parietal cortex. Activation of these regions was modulated by the complexity of the task. Within this n-back neural network, depressed patients showed greater activation of the lateral prefrontal cortex and the anterior cingulate compared to healthy subjects. This study provides evidence that depressed patients need greater activation within the same neural network to maintain a similar level of performance as controls during a working memory task. Our findings suggest that depression may impair the cognitive capacity of depressed patients by recruiting more brain resources than controls during cognitive control.     

Identifying patients with obsessive-compulsive disorder using whole-brain anatomy

Structural neuroimaging studies have reported a variety of brain alterations between groups of obsessive-compulsive disorder (OCD) patients and healthy controls. However, the large heterogeneity in discrete anatomical measures that exists among patients prevents a clear discrimination of single patients from healthy subjects. This reduces the potential clinical applicability of structural neuroimaging studies. In the present study we assessed the feasibility of identifying OCD patients on the basis of whole-brain anatomical alterations. Whole-brain magnetic resonance images were collected from two consecutive samples of OCD outpatients (n=72 and n=30), and control subjects (n=72 and n=30). We computed the whole-brain (voxel-wise) pattern of structural difference between OCD patients and control subjects at the group level. A single expression value of this difference pattern was calculated for each subject, expressing their degree of 'OCD-like' anatomical alteration. Accuracy of patient classification based on these expression values was assessed using two validation approaches. Firstly, using a cross-validation method, we obtained a high classification accuracy (average of the sensitivity and specificity indices) of 93.1%. In a second assessment, which classified new groups of OCD patients and control subjects, overall accuracy was lower at 76.6%. Individual expression values for OCD patients were significantly correlated with overall symptom severity as measured by the Y-BOCS scale. Our results suggest that OCD patients can be identified on the basis of whole-brain structural alterations, although the accuracy of our approach may be limited by the inherent variability of psychiatric populations. Nevertheless, the anatomical characterization of individual patients may ultimately provide the psychiatrist with relevant biological information.     

Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data

In the present study, we applied the Support Vector Machine (SVM) algorithm to perform multivariate classification of brain states from whole functional magnetic resonance imaging (fMRI) volumes without prior selection of spatial features. In addition, we did a comparative analysis between the SVM and the Fisher Linear Discriminant (FLD) classifier. We applied the methods to two multisubject attention experiments: a face matching and a location matching task. We demonstrate that SVM outperforms FLD in classification performance as well as in robustness of the spatial maps obtained (i.e. discriminating volumes). In addition, the SVM discrimination maps had greater overlap with the general linear model (GLM) analysis compared to the FLD. The analysis presents two phases: during the training, the classifier algorithm finds the set of regions by which the two brain states can be best distinguished from each other. In the next phase, the test phase, given an fMRI volume from a new subject, the classifier predicts the subject's instantaneous brain state.     

Investigating the predictive value of whole-brain structural MR scans in autism: A pattern classification approach

Autistic spectrum disorder (ASD) is accompanied by subtle and spatially distributed differences in brain anatomy that are difficult to detect using conventional mass-univariate methods (e.g., VBM). These require correction for multiple comparisons and hence need relatively large samples to attain sufficient statistical power. Reports of neuroanatomical differences from relatively small studies are thus highly variable. Also, VBM does not provide predictive value, limiting its diagnostic value.Here, we examined neuroanatomical networks implicated in ASD using a whole-brain classification approach employing a support vector machine (SVM) and investigated the predictive value of structural MRI scans in adults with ASD. Subsequently, results were compared between SVM and VBM. We included 44 male adults; 22 diagnosed with ASD using “gold-standard” research interviews and 22 healthy matched controls.SVM identified spatially distributed networks discriminating between ASD and controls. These included the limbic, frontal-striatal, fronto-temporal, fronto-parietal and cerebellar systems. SVM applied to gray matter scans correctly classified ASD individuals at a specificity of 86.0% and a sensitivity of 88.0%. Cases (68.0%) were correctly classified using white matter anatomy. The distance from the separating hyperplane (i.e., the test margin) was significantly related to current symptom severity. In contrast, VBM revealed few significant between-group differences at conventional levels of statistical stringency.We therefore suggest that SVM can detect subtle and spatially distributed differences in brain networks between adults with ASD and controls. Also, these differences provide significant predictive power for group membership, which is related to symptom severity.     

Preliminary structural MRI based brain classification of chronic pelvic pain: A MAPP network study

Neuroimaging studies have shown that changes in brain morphology often accompany chronic pain conditions. However, brain biomarkers that are sensitive and specific to chronic pelvic pain (CPP) have not yet been adequately identified. Using data from the Trans-MAPP Research Network, we examined the changes in brain morphology associated with CPP. We used a multivariate pattern classification approach to detect these changes and to identify patterns that could be used to distinguish participants with CPP from age-matched healthy controls. In particular, we used a linear support vector machine (SVM) algorithm to differentiate gray matter images from the 2 groups. Regions of positive SVM weight included several regions within the primary somatosensory cortex, pre-supplementary motor area, hippocampus, and amygdala were identified as important drivers of the classification with 73% overall accuracy. Thus, we have identified a preliminary classifier based on brain structure that is able to predict the presence of CPP with a good degree of predictive power. Our regional findings suggest that in individuals with CPP, greater gray matter density may be found in the identified distributed brain regions, which are consistent with some previous investigations in visceral pain syndromes. Future studies are needed to improve upon our identified preliminary classifier with integration of additional variables and to assess whether the observed differences in brain structure are unique to CPP or generalizable to other chronic pain conditions.     

Effect of outliers and nonhealthy individuals on reference interval estimation.

BACKGROUND: Improvement in reference interval estimation using a new outlier detection technique, even with a physician-determined healthy sample, is examined. The effect of including physician-determined nonhealthy individuals in the sample is evaluated. METHODS: Traditional data transformation coupled with robust and exploratory outlier detection methodology were used in conjunction with various reference interval determination techniques. A simulation study was used to examine the effects of outliers on known reference intervals. Physician-defined healthy groups with and without nonhealthy individuals were compared on real data. RESULTS: With 5% outliers in simulated samples, the described outlier detection techniques had narrower reference intervals. Application of the technique to real data provided reference intervals that were, on average, 10% narrower than those obtained when outlier detection was not used. Only 1.6% of the samples were identified as outliers and removed from reference interval determination in both the healthy and combined samples. CONCLUSIONS: Even in healthy samples, outliers may exist. Combining traditional and robust statistical techniques provide a good method of identifying outliers in a reference interval setting. Laboratories in general do not have a well-defined healthy group from which to compute reference intervals. The effect of nonhealthy individuals in the computation increases reference interval width by approximately 10%. However, there is a large deviation among analytes.     

The association between depression and widowhood and nutritional status in older adults

This study aimed to investigate the association of depression and widowhood on the nutritional status of older adults. A cross-sectional study of community-dwelling older adults in the rural United States was conducted. Dietary intake was measured via questionnaires. Depression status was classified by asking participants if they have ever been diagnosed with the condition, or by review of medical records. The final sample consisted of 1065 participants with 141 (13.2%) depressed, 384 (36.1%) widowed, and 67 (6.3%) both depressed and widowed. Mean caloric intake for total study population was low; widows and widowers had the lowest energy consumption among all groups. Greater intake of several nutrients was observed in depressed and/or widowed subjects. Nutritional services, such as congregate and home delivered meal programs, were not identified as significant contributors to the nutritional intake in older adults who were depressed, widowed, or both. Health care professionals may contribute to meal-based nutrition programs by offering their assistance in aspects of nutritional education and counseling for the promotion of healthy aging.     

Self-calibrated brain network estimation and joint non-convex multi-task learning for identification of early Alzheimer's disease

Detection of early stages of Alzheimer's disease (AD) (i.e., mild cognitive impairment (MCI)) is important to maximize the chances to delay or prevent progression to AD. Brain connectivity networks inferred from medical imaging data have been commonly used to distinguish MCI patients from normal controls (NC). However, existing methods still suffer from limited performance, and classification remains mainly based on single modality data. This paper proposes a new model to automatically diagnosing MCI (early MCI (EMCI) and late MCI (LMCI)) and its earlier stages (i.e., significant memory concern (SMC)) by combining low-rank self-calibrated functional brain networks and structural brain networks for joint multi-task learning. Specifically, we first develop a new functional brain network estimation method. We introduce data quality indicators for self-calibration, which can improve data quality while completing brain network estimation, and perform correlation analysis combined with low-rank structure. Second, functional and structural connected neuroimaging patterns are integrated into our multi-task learning model to select discriminative and informative features for fine MCI analysis. Different modalities are best suited to undertake distinct classification tasks, and similarities and differences among multiple tasks are best determined through joint learning to determine most discriminative features. The learning process is completed by non-convex regularizer, which effectively reduces the penalty bias of trace norm and approximates the original rank minimization problem. Finally, the most relevant disease features classified using a support vector machine (SVM) for MCI identification. Experimental results show that our method achieves promising performance with high classification accuracy and can effectively discriminate between different sub-stages of MCI.     

A framework for the automatic detection and characterization of brain malformations: Validation on the corpus callosum

In this paper, we extend the one-class Support Vector Machine (SVM) and the regularized discriminative direction analysis to the Multiple Kernel (MK) framework, providing an effective analysis pipeline for the detection and characterization of brain malformations, in particular those affecting the corpus callosum.The detection of the brain malformations is currently performed by visual inspection of MRI images, making the diagnostic process sensible to the operator experience and subjectiveness. The method we propose addresses these problems by automatically reproducing the neuroradiologist’s approach. One-class SVMs are appropriate to cope with heterogeneous brain abnormalities that are considered outliers. The MK framework allows to efficiently combine the different geometric features that can be used to describe brain structures. Moreover, the regularized discriminative direction analysis is exploited to highlight the specific malformative patterns for each patient.We performed two different experiments. Firstly, we tested the proposed method to detect the malformations of the corpus callosum on a 104 subject dataset. Results showed that the proposed pipeline can classify the subjects with an accuracy larger than 90% and that the discriminative direction analysis can highlight a wide range of malformative patterns (e.g., local, diffuse, and complex abnormalities). Secondly, we compared the diagnosis of four neuroradiologists on a dataset of 128 subjects. The diagnosis was performed both in blind condition and using the classifier and the discriminative direction outputs. Results showed that the use of the proposed pipeline as an assisted diagnosis tool improves the inter-subject variability of the diagnosis.Finally, a graphical representation of the discriminative direction analysis was proposed to enhance the interpretability of the results and provide the neuroradiologist with a tool to fully and clearly characterize the patient malformations at single-subject level.