Multivariate Pattern Analysis Approach Research Articles

Using multivariate pattern analysis to increase effect sizes for event-related potential analyses.

Multivariate pattern analysis (MVPA) approaches can be applied to the topographic distribution of event-related potential (ERP) signals to "decode" subtly different stimulus classes, such as different faces or different orientations. These approaches are extremely sensitive, and it seems possible that they could also be used to increase effect sizes and statistical power in traditional paradigms that ask whether an ERP component differs in amplitude across conditions. To assess this possibility, we leveraged the open-source ERP CORE data set and compared the effect sizes resulting from conventional univariate analyses of mean amplitude with two MVPA approaches (support vector machine decoding and the cross-validated Mahalanobis distance, both of which are easy to compute using open-source software). We assessed these approaches across seven widely studied ERP components (N170, N400, N2pc, P3b, lateral readiness potential, error related negativity, and mismatch negativity). Across all components, we found that multivariate approaches yielded effect sizes that were as large or larger than the effect sizes produced by univariate approaches. These results indicate that researchers could obtain larger effect sizes, and therefore greater statistical power, by using multivariate analysis of topographic voltage patterns instead of traditional univariate analyses in many ERP studies.

Single-trial visually evoked potentials predict both individual choice and market outcomes

A central assumption in the behavioral sciences is that choice behavior generalizes enough across individuals that measurements from a sampled group can predict the behavior of the population. Following from this assumption, the unit of behavioral sampling or measurement for most neuroimaging studies is the individual; however, cognitive neuroscience is increasingly acknowledging a dissociation between neural activity that predicts individual behavior and that which predicts the average or aggregate behavior of the population suggesting a greater importance of individual differences than is typically acknowledged. For instance, past work has demonstrated that some, but not all, of the neural activity observed during value-based decision-making is able to predict not just individual subjects’ choices but also the success of products on large, online marketplaces—even when those two behavioral outcomes deviate from one another—suggesting that some neural component processes of decision-making generalize to aggregate market responses more readily across individuals than others do. While the bulk of such research has highlighted affect-related neural responses (i.e. in the nucleus accumbens) as a better predictor of group-level behavior than frontal cortical activity associated with the integration of more idiosyncratic choice components, more recent evidence has implicated responses in visual cortical regions as strong predictors of group preference. Taken together, these findings suggest a role of neural responses during early perception in reinforcing choice consistency across individuals and raise fundamental scientific questions about the role sensory systems in value-based decision-making processes. We use a multivariate pattern analysis approach to show that single-trial visually evoked electroencephalographic (EEG) activity can predict individual choice throughout the post-stimulus epoch; however, a nominally sparser set of activity predicts the aggregate behavior of the population. These findings support an account in which a subset of the neural activity underlying individual choice processes can scale to predict behavioral consistency across people, even when the choice behavior of the sample does not match the aggregate behavior of the population.

Resting-state functional connectivity correlates of antipsychotic treatment in unmedicated schizophrenia.

Antipsychotics may modulate the resting state functional connectivity(rsFC) to improve clinical symptoms in schizophrenia(Sz). Existing literature has potential confounders like past medication effects and evaluating preselected regions/networks. We aimed to evaluate connectivity pattern changes with antipsychotics in unmedicated Sz using Multivariate pattern analysis(MVPA), a data-driven technique for whole-brain connectome analysis. Forty-seven unmedicated patients with Sz(DSM-IV-TR) underwent clinical evaluation and neuroimaging at baseline and after 3-months of antipsychotic treatment. Resting-state functional MRI was analysed using group-MVPA to derive 5-components. The brain region with significant connectivity pattern changes with antipsychotics was identified, and post-hoc seed-to-voxel analysis was performed to identify connectivity changes and their association with symptom changes. Connectome-MVPA analysis revealed the connectivity pattern of a cluster localised to left anterior cingulate and paracingulate gyri (ACC/PCG) (peak coordinates:x=-04,y=+30,z=+26;k=12;cluster-pFWE=0.002) to differ significantly after antipsychotics. Specifically, its connections with clusters of precuneus/posterior cingulate cortex(PCC) and left inferior temporal gyrus(ITG) correlated with improvement in positive and negative symptoms scores, respectively. ACC/PCG, a hub of the default mode network, seems to mediate the antipsychotic effects in unmedicated Sz. Evaluating causality models with data from randomised controlled design using the MVPA approach would further enhance our understanding of therapeutic connectomics in Sz.

The impact of visual dysfunctions in recent-onset psychosis and clinical high-risk state for psychosis

Subtle subjective visual dysfunctions (VisDys) are reported by about 50% of patients with schizophrenia and are suggested to predict psychosis states. Deeper insight into VisDys, particularly in early psychosis states, could foster the understanding of basic disease mechanisms mediating susceptibility to psychosis, and thereby inform preventive interventions. We systematically investigated the relationship between VisDys and core clinical measures across three early phase psychiatric conditions. Second, we used a novel multivariate pattern analysis approach to predict VisDys by resting-state functional connectivity within relevant brain systems. VisDys assessed with the Schizophrenia Proneness Instrument (SPI-A), clinical measures, and resting-state fMRI data were examined in recent-onset psychosis (ROP, n = 147), clinical high-risk states of psychosis (CHR, n = 143), recent-onset depression (ROD, n = 151), and healthy controls (HC, n = 280). Our multivariate pattern analysis approach used pairwise functional connectivity within occipital (ON) and frontoparietal (FPN) networks implicated in visual information processing to predict VisDys. VisDys were reported more often in ROP (50.34%), and CHR (55.94%) than in ROD (16.56%), and HC (4.28%). Higher severity of VisDys was associated with less functional remission in both CHR and ROP, and, in CHR specifically, lower quality of life (Qol), higher depressiveness, and more severe impairment of visuospatial constructability. ON functional connectivity predicted presence of VisDys in ROP (balanced accuracy 60.17%, p = 0.0001) and CHR (67.38%, p = 0.029), while in the combined ROP + CHR sample VisDys were predicted by FPN (61.11%, p = 0.006). These large-sample study findings suggest that VisDys are clinically highly relevant not only in ROP but especially in CHR, being closely related to aspects of functional outcome, depressiveness, and Qol. Findings from multivariate pattern analysis support a model of functional integrity within ON and FPN driving the VisDys phenomenon and being implicated in core disease mechanisms of early psychosis states.

Identification of radiographic characteristics associated with pain in hallux valgus patients: A preliminary machine learning study.

ObjectiveTo investigate the association between the structural deformity and foot pain in hallux valgus (HV) patients using a multi-variate pattern analysis (MVPA) approach.MethodsPlain radiographic metrics were calculated from 36 painful and 36 pain-free HV feet. In analysis 1, univariate analyses were performed to investigate the clinical and radiographic differences between painful and pain-free HV. In analysis 2, we investigated the pattern differences for radiographic metrics between these two groups using a MVPA approach utilizing a support vector machine. In analysis 3, sequential backward selection and exhaustive search were performed as a feature-selection procedure to identify an optimal feature subtype. In analysis 4, hierarchical clustering analysis was used to identify the optimal radiographic HV subtype associated with pain in HV.ResultsWe found that: (1) relative to feet with pain-free HV, the painful ones exhibited a higher hallux valgus angle, i.e., the magnitude of distal metatarsal and phalangeal deviation; (2) painful HV could be accurately differentiated from pain-free HV via MVPA. Using sequential backward selection and exhaustive search, a 5-feature subset was identified with optimal performance for classifying HV as either painful or pain-free; and (3) by applying hierarchical clustering analysis, a radiographic subtype with an 80% pain incidence was identified.ConclusionThe pain in HV is multifactorial and associated with a radiographic pattern measured by various angles on plain radiographs. The combination of hallux valgus angle, inter-phalangeal angle, distal metatarsal articular angle, metatarsal cuneiform angle and metatarsal protrusion distance showed the optimal classification performance between painful and pain-free HV.

Decoding violated sensory expectations from the auditory cortex of anaesthetised mice: Hierarchical recurrent neural network depicts separate 'danger' and 'safety' units.

The ability to respond appropriately to sensory information received from the external environment is among the most fundamental capabilities of central nervous systems. In the auditory domain, processes underlying this behaviour are studied by measuring auditory‐evoked electrophysiology during sequences of sounds with predetermined regularities. Identifying neural correlates of ensuing auditory novelty responses is supported by research in experimental animals. In the present study, we reanalysed epidural field potential recordings from the auditory cortex of anaesthetised mice during frequency and intensity oddball stimulation. Multivariate pattern analysis (MVPA) and hierarchical recurrent neural network (RNN) modelling were adopted to explore these data with greater resolution than previously considered using conventional methods. Time‐wise and generalised temporal decoding MVPA approaches revealed previously underestimated asymmetry between responses to sound‐level transitions in the intensity oddball paradigm, in contrast with tone frequency changes. After training, the cross‐validated RNN model architecture with four hidden layers produced output waveforms in response to simulated auditory inputs that were strongly correlated with grand‐average auditory‐evoked potential waveforms (r 2 > .9). Units in hidden layers were classified based on their temporal response properties and characterised using principal component analysis and sample entropy. These demonstrated spontaneous alpha rhythms, sound onset and offset responses and putative ‘safety’ and ‘danger’ units activated by relatively inconspicuous and salient changes in auditory inputs, respectively. The hypothesised existence of corresponding biological neural sources is naturally derived from this model. If proven, this could have significant implications for prevailing theories of auditory processing.

Representation of social content in dorsomedial prefrontal cortex underlies individual differences in agreeableness trait

Personality traits reflect key aspects of individual variability in different psychological domains. Understanding the mechanisms that give rise to these differences requires an exhaustive investigation of the behaviors associated with such traits, and their underlying neural sources. Here we investigated the mechanisms underlying agreeableness, one of the five major dimensions of personality, which has been linked mainly to socio-cognitive functions. In particular, we examined whether individual differences in the neural representations of social information are related to differences in agreeableness of individuals. To this end, we adopted a multivariate representational similarity approach that captured within single individuals the activation pattern similarity of social and non-social content, and tested its relation to the agreeableness trait in a hypothesis-driven manner. The main result confirmed our prediction: processing social and non-social content led to similar patterns of activation in individuals with low agreeableness, while in more agreeable individuals these patterns were more dissimilar. Critically, this association between agreeableness and encoding similarity of social and random content was significant only in the dorsomedial prefrontal cortex, a brain region consistently involved during attributions of mental states. The present finding reveals the link between neural mechanisms underlying social information processing and agreeableness, a personality trait highly related to socio-cognitive abilities, thereby providing a step forward in characterizing its neural determinants. Furthermore, it emphasizes the advantage of multivariate pattern analysis approaches in capturing and understanding the neural sources of individual variations.

Audiovisual Representations of Valence: a Cross-study Perspective.

Hedonic valence describes the pleasantness or unpleasantness of psychological states elicited by stimuli and is conceived as a fundamental building block of emotional experience. Multivariate pattern analysis approaches contribute to the study of valence representation by allowing identification of valence from distributed patterns of activity. However, the issue of construct validity arises in that there is always a possibility that classification results from a single study are driven by factors other than valence, such as the idiosyncrasies of the stimuli. In this work, we identify valence across participants from six different fMRI studies that used auditory, visual, or audiovisual stimuli, thus increasing the likelihood that classification is driven by valence and not by the specifics of the experimental paradigm of a particular study. The studies included a total of 93 participants and differed on stimuli, task, trial duration, number of participants, and scanner parameters. In a leave-one-study-out cross-validation procedure, we trained the classifiers on fMRI data from five studies and predicted valence, positive or negative, for each of the participants in the left-out study. Whole-brain classification demonstrated a reliable distinction between positive and negative valence states (72% accuracy). In a searchlight analysis, the representation of valence was localized to the right postcentral and supramarginal gyri, left superior frontal and middle frontal cortices, and right pregenual anterior cingulate and superior medial frontal cortices. The demonstrated cross-study classification of valence enhances the construct validity and generalizability of the findings from the combined studies.

Gray matter changes in chronic heavy cannabis users: a voxel-level study using multivariate pattern analysis approach.

Recent structural MRI studies on gray matter (GM) volumes using group-level mass-univariate statistical analysis suggest that chronic and heavy cannabis exposure may affect brain region-based morphology. In this prospective study, we use a multivariate pattern analysis approach to investigate the voxel-level change of GM densities in chronic heavy cannabis users. Principal component analysis and linear support vector machine are used in this study, resulting in an 88.1% separation between chronic heavy cannabis users (N = 20) and non-cannabis healthy controls (HCs, N = 22) through leave-one-out cross-validation. The model's discriminative pattern showed that GM density decreases in the part of middle frontal gyrus, inferior frontal gyrus, middle temporal gyrus, inferior temporal gyrus and left occipital lobe in heavy cannabis users with respect to HCs and increases in the part of lentiform nucleus, left cerebellum and right parietal lobe. These results suggest that GM densities alteration has taken place on chronic heavy cannabis users compared with HCs at voxel level.

Understanding multivariate brain activity: Evaluating the effect of voxelwise noise correlations on population codes in functional magnetic resonance imaging.

Previous studies in neurophysiology have shown that neurons exhibit trial-by-trial correlated activity and that such noise correlations (NCs) greatly impact the accuracy of population codes. Meanwhile, multivariate pattern analysis (MVPA) has become a mainstream approach in functional magnetic resonance imaging (fMRI), but it remains unclear how NCs between voxels influence MVPA performance. Here, we tackle this issue by combining voxel-encoding modeling and MVPA. We focus on a well-established form of NC, tuning-compatible noise correlation (TCNC), whose sign and magnitude are systematically related to the tuning similarity between two units. We show that this form of voxelwise NCs can improve MVPA performance if NCs are sufficiently strong. We also confirm these results using standard information-theoretic analyses in computational neuroscience. In the same theoretical framework, we further demonstrate that the effects of noise correlations at both the neuronal level and the voxel level may manifest differently in typical fMRI data, and their effects are modulated by tuning heterogeneity. Our results provide a theoretical foundation to understand the effect of correlated activity on population codes in macroscopic fMRI data. Our results also suggest that future fMRI research could benefit from a closer examination of the correlational structure of multivariate responses, which is not directly revealed by conventional MVPA approaches.

Spatial Information of Somatosensory Stimuli in the Brain: Multivariate Pattern Analysis of Functional Magnetic Resonance Imaging Data.

Background Multivoxel pattern analysis has provided new evidence on somatotopic representation in the human brain. However, the effects of stimulus modality (e.g., penetrating needle versus non-penetrating touch) and level of classification (e.g., multiclass versus binary classification) on patterns of brain activity encoding spatial information of body parts have not yet been studied. We hypothesized that performance of brain-based prediction models may vary across the types of stimuli, and neural patterns of voxels in the SI and parietal cortex would significantly contribute to the prediction of stimulated locations. Objective We aimed to (1) test whether brain responses to tactile stimuli could distinguish among stimulated locations on the body surface, (2) investigate whether the stimulus modality and number of classes affect classification performance, and (3) localize brain regions encoding the spatial information of somatosensory stimuli. Methods Fifteen healthy participants completed two functional magnetic resonance imaging (MRI) scans and were stimulated via the insertion of acupuncture needles or by non-invasive touch stimuli (5.46-sized von Frey filament). Participants received the stimuli at four different locations on the upper and lower limbs (two sites each) for 5 min while blood-oxygen-level-dependent activity (BOLD) was measured using 3-Tesla MRI. We performed multivariate pattern analysis (MVPA) using parameter estimate images of each trial for each participant and the support vector classifier (SVC) function, and the prediction accuracy and other MVPA outcomes were evaluated using stratified five-fold cross validation. We estimated the significance of the classification accuracy using a permutation test with randomly labeled training data (n = 10,000). Searchlight analysis was conducted to identify brain regions associated with significantly higher accuracy compared to predictions based on chance as obtained from a random classifier. Results For the four-class classification (classifying four stimulated points on the body), SVC analysis of whole-brain beta values in response to acupuncture stimulation was able to discriminate among stimulated locations (mean accuracy, 0.31; q < 0.01). The searchlight analysis found that values related to the right primary somatosensory cortex (SI) and intraparietal sulcus were significantly more accurate than those due to chance (p < 0.01). On the other hand, the same classifier did not predict stimulated locations accurately for touch stimulation (mean accuracy, 0.25; q = 0.66). For binary classification (discriminating between two stimulated body parts, i.e., the arm or leg), the SVC algorithm successfully predicted the stimulated body parts for both acupuncture (mean accuracy, 0.63; q < 0.001) and touch stimulation (mean accuracy, 0.60; q < 0.01). Searchlight analysis revealed that predictions based on the right SI, primary motor cortex (MI), paracentral gyrus, and superior frontal gyrus were significantly more accurate compared to predictions based on chance (p < 0.05). Conclusion Our findings suggest that the SI, as well as the MI, intraparietal sulcus, paracentral gyrus, and superior frontal gyrus, is responsible for the somatotopic representation of body parts stimulated by tactile stimuli. The MVPA approach for identifying neural patterns encoding spatial information of somatosensory stimuli may be affected by the stimulus type (penetrating needle versus non-invasive touch) and the number of classes (classification of four small points on the body versus two large body parts). Future studies with larger samples will identify stimulus-specific neural patterns representing stimulated locations, independent of subjective tactile perception and emotional responses. Identification of distinct neural patterns of body surfaces will help in improving neural biomarkers for pain and other sensory percepts in the future.

Associations of volumes and patterns of physical activity with metabolic health in children: A multivariate pattern analysis approach

Physical activity (PA) favorably affects metabolic health in children, but it is unclear how total volumes versus patterns (bouts and breaks) of PA relate to health. By means of multivariate pattern analysis that can handle collinear variables, we determined the associations of PA volumes and patterns with children's metabolic health using different epoch settings. A sample of 841 Norwegian children (age 10.2 ± 0.3 years) provided in 2014 data on accelerometry (ActiGraph GT3X+), using epoch settings of 1, 10, and 60 s and several indices of metabolic health used to create a composite metabolic health score. We created 355 PA indices covering the whole intensity and bout duration spectrum, and used multivariate pattern analysis to analyze the data. Findings showed that bouts of PA added information about childhood health beyond total volumes of PA for all epoch settings. Yet, associations of PA patterns with metabolic health were completely dependent on the epoch settings used. Vigorous PA was strongly associated with metabolic health, while associations of light and moderate PA were weak to moderate, and associations of sedentary time with metabolic health was non-existing. Short intermittent bursts of PA were favorably associated with children's metabolic health, whereas associations of prolonged bouts were weak. This study is the first to determine the multivariate physical activity association pattern related to metabolic health in children across the whole PA intensity and bout duration spectrum. The findings challenge our understanding of PA patterns, and are of major importance for the analysis of accelerometry data.

Sentential negation of abstract and concrete conceptual categories: a brain decoding multivariate pattern analysis study.

We rarely use abstract and concrete concepts in isolation but rather embedded within a linguistic context. To examine the modulatory impact of the linguistic context on conceptual processing, we isolated the case of sentential negation polarity, in which an interaction occurs between the syntactic operator not and conceptual information in the negation's scope. Previous studies suggested that sentential negation of concrete action-related concepts modulates activation in the fronto-parieto-temporal action representation network. In this functional magnetic resonance imaging study, we examined the influence of negation on a wider spectrum of meanings, by factorially manipulating sentence polarity (affirmative, negative) and fine-grained abstract (mental state, emotion, mathematics) and concrete (related to mouth, hand, leg actions) conceptual categories. We adopted a multivariate pattern analysis approach, and tested the accuracy of a machine learning classifier in discriminating brain activation patterns associated to the factorial manipulation. Searchlight analysis was used to localize the discriminating patterns. Overall, the neural processing of affirmative and negative sentences with either an abstract or concrete content could be accurately predicted by means of multivariate classification. We suggest that sentential negation polarity modulates brain activation in distributed representational semantic networks, through the functional mediation of syntactic and cognitive control systems.This article is part of the theme issue 'Varieties of abstract concepts: development, use and representation in the brain'.

Dynamic pattern decoding of source-reconstructed MEG or EEG data: Perspective of multivariate pattern analysis and signal leakage

Recently, an increasing number of studies have employed multivariate pattern analysis (MVPA) rather than univariate analysis for the dynamic pattern decoding of event-related responses recorded with a MEG/EEG sensor. The use of the MVPA approach for source-reconstructed MEG/EEG data is uncommon. For these data, we need to consider the source orientation information and the signal leakage among brain regions. In the present study, we evaluate the perspective of the MVPA approach in the context of source orientation information and signal leakage in source-reconstructed MEG data. We perform face vs. tool object category decoding (FvsT-OCD) of event-related responses from single or multiple voxels from a brain region using a univariate analysis approach and/or the MVPA approach. We also propose and perform symmetric signal leakage correction of source-reconstructed data using an independent component analysis-based approach. FvsT-OCD using single voxel information shows higher sensitivity for the MVPA approach than univariate analysis, as the MVPA approach efficiently utilizes information on all three dipole orientations and is less affected by inter-subject variability. The MVPA approach shows higher sensitivity for FvsT-OCD when considering information from multiple voxels than for a single voxel in a brain region. This finding suggests that the MVPA approach captures the latent multivoxel distributed pattern. However, the results may be partly or entirely attributable to signal leakage between brain regions, as the sensitivity is substantially reduced after signal leakage correction. A consideration of signal leakage is therefore essential during the evaluation of MVPA outcomes.

An MVPA method based on sparse representation for pattern localization in fMRI data analysis

Multivariate pattern analysis (MVPA) approach applied to neuroimaging data, such as functional magnetic resonance imaging (fMRI) data, has received a great deal of attention because of its sensitivity to distinguishing patterns of neural activities associated with different stimuli or cognitive states. Generally, when using MVPA approach to decode the mental states or stimuli, a set of discriminative variables (e.g., voxels) is first selected. However, in most of existing MVPA methods, the selected variables do not contain all informative variables, since these selected variables are sufficient for decoding. In this paper, we propose a multivariate pattern analysis method based on sparse representation for decoding the brain states and localizing category-specific brain activation areas corresponding to two experimental conditions/tasks at the same time. Unlike traditional MVPA approaches, this method is designed to find informative variables as many as possible. We applied the proposed method to two judgement experiments: a gender discrimination and an emotion discrimination task, data analysis results demonstrate its effectiveness and potential applications.

Motion-Induced Position Shifts Activate Early Visual Cortex.

The ability to correctly determine the position of objects in space is a fundamental task of the visual system. The perceived position of briefly presented static objects can be influenced by nearby moving contours, as demonstrated by various illusions collectively known as motion-induced position shifts. Here we use a stimulus that produces a particularly strong effect of motion on perceived position. We test whether several regions-of-interest (ROIs), at different stages of visual processing, encode the perceived rather than retinotopically veridical position. Specifically, we collect functional MRI data while participants experience motion-induced position shifts and use a multivariate pattern analysis approach to compare the activation patterns evoked by illusory position shifts with those evoked by matched physical shifts. We find that the illusory perceived position is represented at the earliest stages of the visual processing stream, including primary visual cortex. Surprisingly, we found no evidence of percept-based encoding of position in visual areas beyond area V3. This result suggests that while it is likely that higher-level visual areas are involved in position encoding, early visual cortex also plays an important role.

Group-regularized individual prediction: theory and application to pain

Multivariate pattern analysis (MVPA) has become an important tool for identifying brain representations of psychological processes and clinical outcomes using fMRI and related methods. Such methods can be used to predict or ‘decode’ psychological states in individual subjects. Single-subject MVPA approaches, however, are limited by the amount and quality of individual-subject data. In spite of higher spatial resolution, predictive accuracy from single-subject data often does not exceed what can be accomplished using coarser, group-level maps, because single-subject patterns are trained on limited amounts of often-noisy data. Here, we present a method that combines population-level priors, in the form of biomarker patterns developed on prior samples, with single-subject MVPA maps to improve single-subject prediction. Theoretical results and simulations motivate a weighting based on the relative variances of biomarker-based prediction—based on population-level predictive maps from prior groups—and individual-subject, cross-validated prediction. Empirical results predicting pain using brain activity on a trial-by-trial basis (single-trial prediction) across 6 studies (N=180 participants) confirm the theoretical predictions. Regularization based on a population-level biomarker—in this case, the Neurologic Pain Signature (NPS)—improved single-subject prediction accuracy compared with idiographic maps based on the individuals' data alone. The regularization scheme that we propose, which we term group-regularized individual prediction (GRIP), can be applied broadly to within-person MVPA-based prediction. We also show how GRIP can be used to evaluate data quality and provide benchmarks for the appropriateness of population-level maps like the NPS for a given individual or study.

Default network connectivity decodes brain states with simulated microgravity.

With great progress of space navigation technology, it becomes possible to travel beyond Earth's gravity. So far, it remains unclear whether the human brain can function normally within an environment of microgravity and confinement. Particularly, it is a challenge to figure out some neuroimaging-based markers for rapid screening diagnosis of disrupted brain function in microgravity environment. In this study, a 7-day -6° head down tilt bed rest experiment was used to simulate the microgravity, and twenty healthy male participants underwent resting-state functional magnetic resonance imaging scans at baseline and after the simulated microgravity experiment. We used a multivariate pattern analysis approach to distinguish the brain states with simulated microgravity from normal gravity based on the functional connectivity within the default network, resulting in an accuracy of no less than 85% via cross-validation. Moreover, most discriminative functional connections were mainly located between the limbic system and cortical areas and were enhanced after simulated microgravity, implying a self-adaption or compensatory enhancement to fulfill the need of complex demand in spatial navigation and motor control functions in microgravity environment. Overall, the findings suggest that the brain states in microgravity are likely different from those in normal gravity and that brain connectome could act as a biomarker to indicate the brain state in microgravity.

Predicting clinical responses in major depression using intrinsic functional connectivity.

There has been increasing interest in multivariate pattern analysis (MVPA) as a means of distinguishing psychiatric patients from healthy controls using brain imaging. However, it remains unclear whether MVPA methods can accurately estimate the medication status of psychiatric patients. This study aims to develop an MVPA approach to accurately predict the antidepressant medication status of individuals with major depression on the basis of whole-brain resting-state functional connectivity MRI (rs-fcMRI). We investigated data from rs-fcMRI of 24 medication-naive depressed patients, 16 out of whom subsequently underwent antidepressant treatment and achieved clinical recovery, and 29 demographically similar controls. By training a linear support vector machine classifier and combining it with principal component analysis, the medication-naive patients were identified from the healthy controls with 100% accuracy. In addition, we found reliable correlations between MVPA prediction scores and clinical symptom severity. Moreover, the most discriminative functional connections were located within or across the cerebellum and default mode, affective, and sensorimotor networks, indicating that these networks may play important roles in major depression. Most importantly, only ∼30% of these discriminative connections were normalized in clinically recovered patients after antidepressant treatment. The current study may not only show the feasibility of estimating medication status by MVPA of whole-brain rs-fcMRI data in major depression but also shed new light on the pathological mechanism of this disorder.

Multivariate Analysis of Single-Sweep Evoked Brain Potentials for Pharmaco-Electroencephalography

Background and Aims: Current findings on altered evoked potentials (EPs) caused by morphine are based on common alterations for a group of subjects after drug administration. However, this ignores the analysis of individual responses, which may explain the clinical differences in efficacy. Therefore, we explored the individual responses to morphine in terms of the altered single-sweep characteristics in a placebo-controlled crossover study. To account for multifactorial mechanisms, several characteristics were assessed simultaneously by multivariate pattern analysis (MVPA). Methods: EPs were recorded from 62 channels and obtained before and after morphine and placebo administration during repeated electrical stimulations of the oesophagus in 12 healthy males. Additionally, the pain detection threshold was recorded to reflect the subjective analgesic effect in each subject. The characteristics of the sweeps were extracted by a multivariate matching pursuit algorithm with Gabor atoms implemented with a variable amplitude and constant phase across the sweeps. The single-sweep amplitudes were used as input to an MVPA algorithm to discriminate individual responses. The accuracy of the MVPA for each individual subject was used for correlation analysis of the analgesic effect. Results: The mean classification accuracy when discriminating pre- and posttreatment morphine responses was 72% (p = 0.01). The individual classification accuracy was positively correlated to the analgesic effect of morphine (p = 0.03). Furthermore, the 2 posttreatment responses were classified and validated by the classification of the 2 pretreatment responses (p = 0.001). Conclusions: The alterations in the single-sweep EPs after morphine reflect the analgesic effect. The MVPA approach is a novel methodology for monitoring the individual efficacy of analgesics.