Multivariate Patterns of Posterior Cortical Activity Differentiate Forms of Emotional Distancing.

Distancing is an effective tactic for emotion regulation, which can take several forms depending on the type(s) of psychological distance being manipulated to modify affect. We recently proposed a neurocognitive model of emotional distancing, but it is unknown how its specific forms are instantiated in the brain. Here, we presented healthy young adults (N = 34) with aversive pictures during functional magnetic resonance imaging to directly compare behavioral performance and brain activity across spatial, temporal, and objective forms of distancing. We found emotion regulation performance to be largely comparable across these forms. A conjunction analysis of activity associated with these forms yielded a high degree of overlap, encompassing regions of the default mode and frontoparietal networks as predicted by our model. A multivariate pattern classification further revealed distributed patches of posterior cortical activation that discriminated each form from one another. These findings not only confirm aspects of our overarching model but also elucidate a novel role for cortical regions in and around the parietal lobe in selectively supporting spatial, temporal, and social cognitive processes to distance oneself from an emotional encounter. These regions may provide new targets for brain-based interventions for emotion dysregulation.

Longitudinal identification of clinically distinct neurophenotypes in young children with fragile X syndrome.

Fragile X syndrome (FXS), due to mutations of the FMR1 gene, is the most common known inherited cause of developmental disability. The cognitive, behavioral, and neurological phenotypes observed in affected individuals can vary considerably, making it difficult to predict outcomes and determine the need for interventions. We sought to examine early structural brain growth as a potential marker for identification of clinically meaningful subgroups. Participants included 42 very young boys with FXS who completed a T1-weighted anatomical MRI and cognitive/behavioral assessment at two longitudinal time points, with mean ages of 2.89 y and 4.91 y. Topological data analysis (TDA), an unsupervised approach to multivariate pattern analysis, was applied to the longitudinal anatomical data to identify coherent but heretofore unknown subgroups. TDA revealed two large subgroups within the study population based solely on longitudinal MRI data. Post hoc comparisons of cognition, adaptive functioning, and autism severity scores between these groups demonstrated that one group was consistently higher functioning on all measures at both time points, with pronounced and significant unidirectional differences (P < 0.05 for time point 1 and/or time point 2 for each measure). These results support the existence of two longitudinally defined, neuroanatomically distinct, and clinically relevant phenotypes among boys with FXS. If confirmed by additional analyses, such information may be used to predict outcomes and guide design of targeted therapies. Furthermore, TDA of longitudinal anatomical MRI data may represent a useful method for reliably and objectively defining subtypes within other neuropsychiatric disorders.

Age-related differences in task-induced brain activation is not task specific: Multivariate pattern generalization between metacognition, cognition and perception.

Adolescence is associated with widespread maturation of brain structures and functional connectivity profiles that shift from local to more distributed and better integrated networks, which are active during a variety of cognitive tasks. Nevertheless, the approach to examine task-induced developmental brain changes is function-specific, leaving the question open whether functional maturation is specific to the particular cognitive demands of the task used, or generalizes across different tasks. In the present study we examine the hypothesis that functional brain maturation is driven by global changes in how the brain handles cognitive demands. Multivariate pattern classification analysis (MVPA) was used to examine whether age discriminative task-induced activation patterns generalize across a wide range of information processing levels. 25 young (13-years old) and 22 old (17-years old) adolescents performed three conceptually different tasks of metacognition, cognition and visual processing. MVPA applied within each task indicated that task-induced brain activation is consistent and reliably different between ages 13 and 17. These age-discriminative activation patterns proved to be common across the different tasks used, despite the differences in cognitive demands and brain structures engaged by each of the three tasks. MVP classifiers trained to detect age-discriminative patterns in brain activation during one task were significantly able to decode age from brain activation maps during execution of other tasks with accuracies between 63 and 75%. The results emphasize that age-specific characteristics of task-induced brain activation have to be understood at the level of brain-wide networks that show maturational changes in their organization and processing efficacy during adolescence.

Performance monitoring beyond choice tasks: The time course of force execution monitoring investigated by event-related potentials and multivariate pattern analysis.

Accurate force production is an essential motor function which, in most cases, requires continuous performance monitoring. Unlike choice-response tasks with two response alternatives, the accuracy in a force production paradigm is defined as an area between an upper and lower limit on the force continuum. In the present study, we investigated the neural mechanisms underlying force production. We used a force production task in which the participants (n = 48) were asked to exert a brief force pulse within a specific force range. This allowed: (1) investigation of action monitoring activity during force execution using response-locked and feedback-locked event-related potential (ERP) components known to be involved in error monitoring; (2) multivariate pattern analysis (MVPA) for ERPs. We found that the different force production ranges (characterised as too low, correct, and too high with respect to the target force range) showed no clear error-specific variations in the ERP components of interest. MVPA, on the other hand, allowed for successful classification, not only between the correct and the incorrect outcome conditions, but also between the two incorrect outcome conditions. This suggests that the classifier identified neural patterns reflecting the force magnitude rather than the correctness of a response. Moreover, additional support-vector regression (SVR) analyses showed that single-trial response parameters (i.e. peak force and time-to-peak) could be decoded from the brain activity pattern starting from 140 ms (for peak force) and 270 ms (for time-to-peak) before the response onset. These results indicate that the motor program defined the magnitude and timing of the force pulse before response execution, while the correctness of that response (in relation to the "default force" required) was not yet foreshadowed in neural signals. Finally, this study presents the first evidence of a post-error force adjustment mechanism, for which participants produced a higher force in trials after under-producing the required force, and a lower force in trials after over-producing the required force.

Crossmodal Classification of Mu Rhythm Activity during Action Observation and Execution Suggests Specificity to Somatosensory Features of Actions.

The alpha mu rhythm (8-13 Hz) has been considered to reflect mirror neuron activity because it is attenuated by both action observation and action execution. The putative link between mirror neuron system activity and the mu rhythm has been used to study the involvement of the mirror system in a wide range of socio-cognitive processes and clinical disorders. However, previous research has failed to convincingly demonstrate the specificity of the mu rhythm, meaning that it is unclear whether the mu rhythm reflects mirror neuron activity. It also remains unclear whether mu rhythm suppression during action observation reflects the processing of motor or tactile information. In an attempt to assess the validity of the mu rhythm as a measure of mirror neuron activity, we used crossmodal pattern classification to assess the specificity of EEG mu rhythm response to action varying in terms of action type (whole-hand or precision grip), concurrent tactile stimulation (stimulation or no stimulation), or object use (transitive or intransitive actions) in 20 human participants. The main results reveal that above-chance crossmodal classification of mu rhythm activity was obtained in the central channels for tactile stimulation and action transitivity but not for action type. Furthermore, traditional univariate analyses applied to the same data were insensitive to differences between conditions. By calling into question the relationship between mirror system activity and the mu rhythm, these results have important implications for the use and interpretation of mu rhythm activity.SIGNIFICANCE STATEMENT The central alpha mu rhythm oscillation is a widely used measure of the human mirror neuron system that has been used to make important claims concerning cognitive functioning in health and in disease. Here, we used a novel multivariate analytical approach to show that crossmodal EEG mu rhythm responses primarily index the somatosensory features of actions, suggesting that the mu rhythm is not a valid measure of mirror neuron activity. Results may lead to the revision of the conclusions of many previous studies using this measure, and to the transition toward a theory of mu rhythm function that is more consistent with current models of sensory processing in the self and in others.

Dynamic population codes of multiplexed stimulus features in primate area MT.

The middle-temporal area (MT) of primate visual cortex is critical in the analysis of visual motion. Single-unit studies suggest that the response dynamics of neurons within area MT depend on stimulus features, but how these dynamics emerge at the population level, and how feature representations interact, is not clear. Here, we used multivariate classification analysis to study how stimulus features are represented in the spiking activity of populations of neurons in area MT of marmoset monkey. Using representational similarity analysis we distinguished the emerging representations of moving grating and dot field stimuli. We show that representations of stimulus orientation, spatial frequency, and speed are evident near the onset of the population response, while the representation of stimulus direction is slower to emerge and sustained throughout the stimulus-evoked response. We further found a spatiotemporal asymmetry in the emergence of direction representations. Representations for high spatial frequencies and low temporal frequencies are initially orientation dependent, while those for high temporal frequencies and low spatial frequencies are more sensitive to motion direction. Our analyses reveal a complex interplay of feature representations in area MT population response that may explain the stimulus-dependent dynamics of motion vision.NEW & NOTEWORTHY Simultaneous multielectrode recordings can measure population-level codes that previously were only inferred from single-electrode recordings. However, many multielectrode recordings are analyzed using univariate single-electrode analysis approaches, which fail to fully utilize the population-level information. Here, we overcome these limitations by applying multivariate pattern classification analysis and representational similarity analysis to large-scale recordings from middle-temporal area (MT) in marmoset monkeys. Our analyses reveal a dynamic interplay of feature representations in area MT population response.

Spatial scale and distribution of neurovascular signals underlying decoding of orientation and eye of origin from fMRI data.

Multivariate pattern analysis of functional magnetic resonance imaging (fMRI) data is widely used, yet the spatial scales and origin of neurovascular signals underlying such analyses remain unclear. We compared decoding performance for stimulus orientation and eye of origin from fMRI measurements in human visual cortex with predictions based on the columnar organization of each feature and estimated the spatial scales of patterns driving decoding. Both orientation and eye of origin could be decoded significantly above chance in early visual areas (V1-V3). Contrary to predictions based on a columnar origin of response biases, decoding performance for eye of origin in V2 and V3 was not significantly lower than that in V1, nor did decoding performance for orientation and eye of origin differ significantly. Instead, response biases for both features showed large-scale organization, evident as a radial bias for orientation, and a nasotemporal bias for eye preference. To determine whether these patterns could drive classification, we quantified the effect on classification performance of binning voxels according to visual field position. Consistent with large-scale biases driving classification, binning by polar angle yielded significantly better decoding performance for orientation than random binning in V1-V3. Similarly, binning by hemifield significantly improved decoding performance for eye of origin. Patterns of orientation and eye preference bias in V2 and V3 showed a substantial degree of spatial correlation with the corresponding patterns in V1, suggesting that response biases in these areas originate in V1. Together, these findings indicate that multivariate classification results need not reflect the underlying columnar organization of neuronal response selectivities in early visual areas.NEW & NOTEWORTHY Large-scale response biases can account for decoding of orientation and eye of origin in human early visual areas V1-V3. For eye of origin this pattern is a nasotemporal bias; for orientation it is a radial bias. Differences in decoding performance across areas and stimulus features are not well predicted by differences in columnar-scale organization of each feature. Large-scale biases in extrastriate areas are spatially correlated with those in V1, suggesting biases originate in primary visual cortex.

The Representation of Object-Directed Action and Function Knowledge in the Human Brain.

The appropriate use of everyday objects requires the integration of action and function knowledge. Previous research suggests that action knowledge is represented in frontoparietal areas while function knowledge is represented in temporal lobe regions. Here we used multivoxel pattern analysis to investigate the representation of object-directed action and function knowledge while participants executed pantomimes of familiar tool actions. A novel approach for decoding object knowledge was used in which classifiers were trained on one pair of objects and then tested on a distinct pair; this permitted a measurement of classification accuracy over and above object-specific information. Region of interest (ROI) analyses showed that object-directed actions could be decoded in tool-preferring regions of both parietal and temporal cortex, while no independently defined tool-preferring ROI showed successful decoding of object function. However, a whole-brain searchlight analysis revealed that while frontoparietal motor and peri-motor regions are engaged in the representation of object-directed actions, medial temporal lobe areas in the left hemisphere are involved in the representation of function knowledge. These results indicate that both action and function knowledge are represented in a topographically coherent manner that is amenable to study with multivariate approaches, and that the left medial temporal cortex represents knowledge of object function.

Classification based hypothesis testing in neuroscience: Below-chance level classification rates and overlooked statistical properties of linear parametric classifiers.

Multivariate pattern analysis (MVPA) has recently become a popular tool for data analysis. Often, classification accuracy as quantified by correct classification rate (CCR) is used to illustrate the size of the effect under investigation. However, we show that in low sample size (LSS), low effect size (LES) data, which is typical in neuroscience, the distribution of CCRs from cross-validation of linear MVPA is asymmetric and can show classification rates considerably below what would be expected from chance classification. Conversely, the mode of the distribution in these cases is above expected chance levels, leading to a spuriously high number of above chance CCRs. This unexpected distribution has strong implications when using MVPA for hypothesis testing. Our analyses warrant the conclusion that CCRs do not well reflect the size of the effect under investigation. Moreover, the skewness of the null-distribution precludes the use of many standard parametric tests to assess significance of CCRs. We propose that MVPA results should be reported in terms of P values, which are estimated using randomization tests. Also, our results show that cross-validation procedures using a low number of folds, e.g. twofold, are generally more sensitive, even though the average CCRs are often considerably lower than those obtained using a higher number of folds. Hum Brain Mapp 37:1842-1855, 2016. © 2016 Wiley Periodicals, Inc.

Shared and distinct contributions of rostrolateral prefrontal cortex to analogical reasoning and episodic memory retrieval.

Rostrolateral prefrontal cortex (RLPFC) is widely appreciated to support higher cognitive functions, including analogical reasoning and episodic memory retrieval. However, these tasks have typically been studied in isolation, and thus it is unclear whether they involve common or distinct RLPFC mechanisms. Here, we introduce a novel functional magnetic resonance imaging (fMRI) task paradigm to compare brain activity during reasoning and memory tasks while holding bottom-up perceptual stimulation and response demands constant. Univariate analyses on fMRI data from twenty participants identified a large swath of left lateral prefrontal cortex, including RLPFC, that showed common engagement on reasoning trials with valid analogies and memory trials with accurately retrieved source details. Despite broadly overlapping recruitment, multi-voxel activity patterns within left RLPFC reliably differentiated these two trial types, highlighting the presence of at least partially distinct information processing modes. Functional connectivity analyses demonstrated that while left RLPFC showed consistent coupling with the fronto-parietal control network across tasks, its coupling with other cortical areas varied in a task-dependent manner. During the memory task, this region strengthened its connectivity with the default mode and memory retrieval networks, whereas during the reasoning task it coupled more strongly with a nearby left prefrontal region (BA 45) associated with semantic processing, as well as with a superior parietal region associated with visuospatial processing. Taken together, these data suggest a domain-general role for left RLPFC in monitoring and/or integrating task-relevant knowledge representations and showcase how its function cannot solely be attributed to episodic memory or analogical reasoning computations.

Individualized differential diagnosis of schizophrenia and mood disorders using neuroanatomical biomarkers.

Magnetic resonance imaging-based markers of schizophrenia have been repeatedly shown to separate patients from healthy controls at the single-subject level, but it remains unclear whether these markers reliably distinguish schizophrenia from mood disorders across the life span and generalize to new patients as well as to early stages of these illnesses. The current study used structural MRI-based multivariate pattern classification to (i) identify and cross-validate a differential diagnostic signature separating patients with first-episode and recurrent stages of schizophrenia (n = 158) from patients with major depression (n = 104); and (ii) quantify the impact of major clinical variables, including disease stage, age of disease onset and accelerated brain ageing on the signature's classification performance. This diagnostic magnetic resonance imaging signature was then evaluated in an independent patient cohort from two different centres to test its generalizability to individuals with bipolar disorder (n = 35), first-episode psychosis (n = 23) and clinically defined at-risk mental states for psychosis (n = 89). Neuroanatomical diagnosis was correct in 80% and 72% of patients with major depression and schizophrenia, respectively, and involved a pattern of prefronto-temporo-limbic volume reductions and premotor, somatosensory and subcortical increments in schizophrenia versus major depression. Diagnostic performance was not influenced by the presence of depressive symptoms in schizophrenia or psychotic symptoms in major depression, but earlier disease onset and accelerated brain ageing promoted misclassification in major depression due to an increased neuroanatomical schizophrenia likeness of these patients. Furthermore, disease stage significantly moderated neuroanatomical diagnosis as recurrently-ill patients had higher misclassification rates (major depression: 23%; schizophrenia: 29%) than first-episode patients (major depression: 15%; schizophrenia: 12%). Finally, the trained biomarker assigned 74% of the bipolar patients to the major depression group, while 83% of the first-episode psychosis patients and 77% and 61% of the individuals with an ultra-high risk and low-risk state, respectively, were labelled with schizophrenia. Our findings suggest that neuroanatomical information may provide generalizable diagnostic tools distinguishing schizophrenia from mood disorders early in the course of psychosis. Disease course-related variables such as age of disease onset and disease stage as well alterations of structural brain maturation may strongly impact on the neuroanatomical separability of major depression and schizophrenia.

The basis of orientation decoding in human primary visual cortex: fine- or coarse-scale biases?

Orientation signals in human primary visual cortex (V1) can be reliably decoded from the multivariate pattern of activity as measured with functional magnetic resonance imaging (fMRI). The precise underlying source of these decoded signals (whether by orientation biases at a fine or coarse scale in cortex) remains a matter of some controversy, however. Freeman and colleagues (J Neurosci 33: 19695-19703, 2013) recently showed that the accuracy of decoding of spiral patterns in V1 can be predicted by a voxel's preferred spatial position (the population receptive field) and its coarse orientation preference, suggesting that coarse-scale biases are sufficient for orientation decoding. Whether they are also necessary for decoding remains an open question, and one with implications for the broader interpretation of multivariate decoding results in fMRI studies.

Individual prediction of chronic motor outcome in the acute post-stroke stage: Behavioral parameters versus functional imaging.

Several neurobiological factors have been found to correlate with functional recovery after brain lesions. However, predicting the individual potential of recovery remains difficult. Here we used multivariate support vector machine (SVM) classification to explore the prognostic value of functional magnetic resonance imaging (fMRI) to predict individual motor outcome at 4-6 months post-stroke. To this end, 21 first-ever stroke patients with hand motor deficits participated in an fMRI hand motor task in the first few days post-stroke. Motor impairment was quantified assessing grip force and the Action Research Arm Test. Linear SVM classifiers were trained to predict good versus poor motor outcome of unseen new patients. We found that fMRI activity acquired in the first week post-stroke correctly predicted the outcome for 86% of all patients. In contrast, the concurrent assessment of motor function provided 76% accuracy with low sensitivity (<60%). Furthermore, the outcome of patients with initially moderate impairment and high outcome variability could not be predicted based on motor tests. In contrast, fMRI provided 87.5% prediction accuracy in these patients. Classifications were driven by activity in ipsilesional motor areas and contralesional cerebellum. The accuracy of subacute fMRI data (two weeks post-stroke), age, time post-stroke, lesion volume, and location were at 50%-chance-level. In conclusion, multivariate decoding of fMRI data with SVM early after stroke enables a robust prediction of motor recovery. The potential for recovery is influenced by the initial dysfunction of the active motor system, particularly in those patients whose outcome cannot be predicted by behavioral tests.

Classifying the wandering mind: revealing the affective content of thoughts during task-free rest periods.

Many powerful human emotional thoughts are generated in the absence of a precipitating event in the environment. Here, we tested whether we can decode the valence of internally driven, self-generated thoughts during task-free rest based on neural similarities with task-related affective mental states. We acquired functional magnetic resonance imaging (fMRI) data while participants generated positive and negative thoughts as part of an attribution task (Session A) and while they reported the occurrence of comparable mental states during task-free rest periods (Session B). With the use of multivariate pattern analyses (MVPA), we identified response patterns in the medial orbitofrontal cortex (mOFC) that encode the affective content of thoughts that are generated in response to an external experimental cue. Importantly, these task driven response patterns reliably predicted the occurrence of affective thoughts generated during unconstrained rest periods recorded one week apart. This demonstrates that at least certain elements of task-cued and task-free affective experiences rely on a common neural code. Furthermore, our findings reveal the role that the mOFC plays in determining the affective tone of unconstrained thoughts. More generally, our results suggest that MVPA is an important methodological tool for attempts to understand unguided subject driven mental states such as mind-wandering and daydreaming based on neural similarities with task-based experiences.

Decoding the individual finger movements from single-trial functional magnetic resonance imaging recordings of human brain activity.

Multivariate pattern classification analysis (MVPA) has been applied to functional magnetic resonance imaging (fMRI) data to decode brain states from spatially distributed activation patterns. Decoding upper limb movements from non-invasively recorded human brain activation is crucial for implementing a brain-machine interface that directly harnesses an individual's thoughts to control external devices or computers. The aim of this study was to decode the individual finger movements from fMRI single-trial data. Thirteen healthy human subjects participated in a visually cued delayed finger movement task, and only one slight button press was performed in each trial. Using MVPA, the decoding accuracy (DA) was computed separately for the different motor-related regions of interest. For the construction of feature vectors, the feature vectors from two successive volumes in the image series for a trial were concatenated. With these spatial-temporal feature vectors, we obtained a 63.1% average DA (84.7% for the best subject) for the contralateral primary somatosensory cortex and a 46.0% average DA (71.0% for the best subject) for the contralateral primary motor cortex; both of these values were significantly above the chance level (20%). In addition, we implemented searchlight MVPA to search for informative regions in an unbiased manner across the whole brain. Furthermore, by applying searchlight MVPA to each volume of a trial, we visually demonstrated the information for decoding, both spatially and temporally. The results suggest that the non-invasive fMRI technique may provide informative features for decoding individual finger movements and the potential of developing an fMRI-based brain-machine interface for finger movement.

Multivariate pattern analysis reveals subtle brain anomalies relevant to the cognitive phenotype in neurofibromatosis type 1.

Neurofibromatosis Type 1 (NF1) is a common genetic condition associated with cognitive dysfunction. However, the pathophysiology of the NF1 cognitive deficits is not well understood. Abnormal brain structure, including increased total brain volume, white matter (WM) and grey matter (GM) abnormalities have been reported in the NF1 brain. These previous studies employed univariate model-driven methods preventing detection of subtle and spatially distributed differences in brain anatomy. Multivariate pattern analysis allows the combination of information from multiple spatial locations yielding a discriminative power beyond that of single voxels. Here we investigated for the first time subtle anomalies in the NF1 brain, using a multivariate data-driven classification approach. We used support vector machines (SVM) to classify whole-brain GM and WM segments of structural T1 -weighted MRI scans from 39 participants with NF1 and 60 non-affected individuals, divided in children/adolescents and adults groups. We also employed voxel-based morphometry (VBM) as a univariate gold standard to study brain structural differences. SVM classifiers correctly classified 94% of cases (sensitivity 92%; specificity 96%) revealing the existence of brain structural anomalies that discriminate NF1 individuals from controls. Accordingly, VBM analysis revealed structural differences in agreement with the SVM weight maps representing the most relevant brain regions for group discrimination. These included the hippocampus, basal ganglia, thalamus, and visual cortex. This multivariate data-driven analysis thus identified subtle anomalies in brain structure in the absence of visible pathology. Our results provide further insight into the neuroanatomical correlates of known features of the cognitive phenotype of NF1.

Decoding the motion aftereffect in human visual cortex.

In the motion aftereffect (MAE), adapting to a moving stimulus causes a subsequently presented stationary stimulus to appear to move in the opposite direction. Recently, the neural basis of the motion aftereffect has received considerable interest, and a number of brain areas have been implicated in the generation of the illusory motion. Here, we use functional magnetic resonance imaging in combination with multivariate pattern classification to directly compare the neural activity evoked during the observation of both real and illusory motions. We show that the perceived illusory motion is not encoded in the same way as real motion in the same direction. Instead, suppression of the adapted direction of motion results in a shift of the population response of motion sensitive neurons in area MT+, resulting in activation patterns that are in fact more similar to real motion in orthogonal, rather than opposite directions. Although robust motion selectivity was observed in visual areas V1, V2, V3, and V4, this MAE-specific modulation of the population response was only observed in area MT+. Implications for our understanding of the motion aftereffect, and models of motion perception in general, are discussed.

Distributed effects of methylphenidate on the network structure of the resting brain: a connectomic pattern classification analysis.

Methylphenidate is a psychostimulant medication that produces improvements in functions associated with multiple neurocognitive systems. To investigate the potentially distributed effects of methylphenidate on the brain's intrinsic network architecture, we coupled resting state imaging with multivariate pattern classification. In a within-subject, double-blind, placebo-controlled, randomized, counterbalanced, cross-over design, 32 healthy human volunteers received either methylphenidate or placebo prior to two fMRI resting state scans separated by approximately one week. Resting state connectomes were generated by placing regions of interest at regular intervals throughout the brain, and these connectomes were submitted for support vector machine analysis. We found that methylphenidate produces a distributed, reliably detected, multivariate neural signature. Methylphenidate effects were evident across multiple resting state networks, especially visual, somatomotor, and default networks. Methylphenidate reduced coupling within visual and somatomotor networks. In addition, default network exhibited decoupling with several task positive networks, consistent with methylphenidate modulation of the competitive relationship between these networks. These results suggest that connectivity changes within and between large-scale networks are potentially involved in the mechanisms by which methylphenidate improves attention functioning.

The effects of stimulus inversion on the neural representations of Chinese character and face recognition.

This study investigates whether stimulus inversion influences neural responses of Chinese character recognition similarly to its effect on face recognition in category-selective and object-related brain areas using functional magnetic resonance imaging. Participants performed a one-back matching task for simple (one radical) and compound (two radicals) Chinese characters and faces with upright and inverted orientations. Inverted stimuli produced slower response times with stronger activity within the fusiform gyrus (FG) than upright stimuli for faces and Chinese characters. While common inversion-related activation was identified in the left FG among stimulus types, we observed a significant inter-regional correlation between the left FG and the intraparietal sulcus for face inversion. Importantly, analyses of region-of-interest (ROI) multivariate pattern classification showed that classifiers trained on face inversion can decode the representations of character inversion in the character-selective ROI. However, this was not true for face inversion in face-selective ROIs when the classifiers were trained on characters. Similar activity patterns for character and face inversion were observed in the object-related ROIs. We also showed higher decoding accuracy for upright stimuli in the face-selective ROI than in the character-selective ROI but this was not true for inverted ones or when patterns were examined in the object-related ROIs. Together, our results support shared and distinct configural representations for character and face recognition in category-selective and object-related brain areas.

Glucocorticoid regulation and neuroanatomy in fragile x syndrome.

Fragile X syndrome (FXS) is the leading known inherited cause for intellectual disability. Due to mutations in the FMR1 gene, affected individuals are at risk for serious cognitive and behavioral symptoms and developmental disability. Clinical presentation varies considerably, and investigation of genetic factors not directly related to FMR1 may help better understand variability. The present study examined the BclI polymorphism of the glucocorticoid receptor gene NR3C1 in 43 individuals with FXS (28 females, age 16 to 25). Females with FXS who presented with one or more G alleles demonstrated attenuated symptoms of anxiety/depression (p = 0.038) and externalizing behaviors (p = 0.042) relative to individuals with the C/C allele. In the combined sample (males and females) structural neuroimaging data differentiated individuals with a G allele from those with the C/C genotype (p < 0.001). Key components of anxiety/fear neurocircuitry (amygdala, insula) contributed more (relative to other regions) to the model differentiating groups. These results indicate that GR polymorphisms are associated with an altered pattern of behavioral and brain development in FXS. This information is important for understanding and treating mood disorders and altered brain development among individuals with FXS. With further research, these findings could be informative for understanding anxiety and mood disorders more broadly.