Hyperthymic temperament and brightness preference in healthy subjects: Further evidence for involvement of left inferior orbitofrontal cortex in hyperthymic temperament

Background

Hyperthymic temperament has been generally accepted as one of premorbid temperament of bipolar disorders. Although several studies indicate that subjects with hyperthymic temperament receive more illuminance, our recent study suggests that the threshold of brightness and darkness judgment is not different between more and less hyperthymic subjects, and that hyperthymic temperament may be associated with left inferior orbitofrontal cortex, which has been reported to be associated with bipolar disorder. Therefore, at the next stage, it can be hypothesized that hyperthymic subjects may prefer brightness (i.e., heliotropism) and thereby seek illuminance, and that percent signal changes of left inferior orbitofrontal cortex during the preference task may be associated with hyperthymic temperament scores.

Methods

We compared brightness preference and un-preference between more and less hyperthymic subjects, and investigated percent signal changes of left inferior orbitofrontal cortex during brightness preference judgment, brightness un-preference judgment, and control task by using functional Magnetic Resonance Imaging (fMRI).

Results

There were significant differences in brightness preference judgment and un-preference judgment, showing that more hyperthymic subjects preferred brighter illuminace levels and un-preferred darker illuminance levels than less hyperthymic subjects. Moreover, fMRI signal changes of left inferior orbitofrontal cortex was significantly and negatively associated with hyperthymic temperament scores.

Limitations

It is unknown why left but not right inferior orbitofrontal cortex was associated with hyperthymic temperament scores.

Conclusions

The present findings suggest that more hyperthymic subjects may prefer brightness and un-prefer darkness than less hyperthymic subjects (i.e., heliotropism), and reconfirm that hyperthymic temperament may be associated with left inferior orbitofrontal cortex, which have been reported to be associated with bipolar disorders.     

Meta-analytic evidence for neuroimaging models of depression: State or trait?

Background

Major Depressive Disorder (MDD) is a leading cause of disease burden worldwide. With the rapid growth of neuroimaging research on relatively small samples, meta-analytic techniques are becoming increasingly important. Here, we aim to clarify the support in fMRI literature for three leading neurobiological models of MDD: limbic–cortical, cortico–striatal and the default mode network.

Methods

Searches of PubMed and Web of Knowledge, and manual searches, were undertaken in early 2011. Data from 34 case-control comparisons (n=1165) and 6 treatment studies (n=105) were analysed separately with two meta-analytic methods for imaging data: Activation Likelihood Estimation and Gaussian-Process Regression.

Results

There was broad support for limbic–cortical and cortico–striatal models in the case-control data. Evidence for the role of the default mode network was weaker. Treatment-sensitive regions were primarily in lateral frontal areas.

Limitations

In any meta-analysis, the increase in the statistical power of the inference comes with the risk of aggregating heterogeneous study pools. While we believe that this wide range of paradigms allows identification of key regions of dysfunction in MDD (regardless of task), we attempted to minimise such risks by employing GPR, which models such heterogeneity.

Conclusions

The focus of treatment effects in frontal areas indicates that dysregulation here may represent a biomarker of treatment response. Since the dysregulation in many subcortical regions in the case-control comparisons appeared insensitive to treatment, we propose that these act as trait vulnerability markers, or perhaps treatment insensitivity. Our findings allow these models of MDD to be applied to fMRI literature with some confidence.     

Improving the sensitivity of cluster‐based statistics for functional magnetic resonance imaging data

Because of the high dimensionality of neuroimaging data, identifying a statistical test that is both valid and maximally sensitive is an important challenge. Here, we present a combination of two approaches for functional magnetic resonance imaging (fMRI) data analysis that together result in substantial improvements of the sensitivity of cluster‐based statistics. The first approach is to create novel cluster definitions that optimize sensitivity to plausible effect patterns. The second is to adopt a new approach to combine test statistics with different sensitivity profiles, which we call the min(p) method. These innovations are made possible by using the randomization inference framework. In this article, we report on a set of simulations and analyses of real task fMRI data that demonstrate (a) that the proposed methods control the false‐alarm rate, (b) that the sensitivity profiles of cluster‐based test statistics vary depending on the cluster defining thresholds and cluster definitions, and (c) that the min(p) method for combining these test statistics results in a drastic increase of sensitivity (up to fivefold), compared to existing fMRI analysis methods. This increase in sensitivity is not at the expense of the spatial specificity of the inference.     

Expert Tool Users Show Increased Differentiation between Visual Representations of Hands and Tools

The idea that when we use a tool we incorporate it into the neural representation of our body (embodiment) has been a major inspiration for philosophy, science, and engineering. While theoretically appealing, there is little direct evidence for tool embodiment at the neural level. Using functional magnetic resonance imaging (fMRI) in male and female human subjects, we investigated whether expert tool users (London litter pickers: n = 7) represent their expert tool more like a hand (neural embodiment) or less like a hand (neural differentiation), as compared with a group of tool novices (n = 12). During fMRI scans, participants viewed first-person videos depicting grasps performed by either a hand, litter picker, or a non-expert grasping tool. Using representational similarity analysis (RSA), differences in the representational structure of hands and tools were measured within occipitotemporal cortex (OTC). Contrary to the neural embodiment theory, we find that the experts group represent their own tool less like a hand (not more) relative to novices. Using a case-study approach, we further replicated this effect, independently, in five of the seven individual expert litter pickers, as compared with the novices. An exploratory analysis in left parietal cortex, a region implicated in visuomotor representations of hands and tools, also indicated that experts do not visually represent their tool more similar to hands, compared with novices. Together, our findings suggest that extensive tool use leads to an increased neural differentiation between visual representations of hands and tools. This evidence provides an important alternative framework to the prominent tool embodiment theory.SIGNIFICANCE STATEMENT It is commonly thought that tool use leads to the assimilation of the tool into the neural representation of the body, a process referred to as embodiment. Here, we demonstrate that expert tool users (London litter pickers) neurally represent their own tool less like a hand (not more), compared with novices. Our findings advance our current understanding for how experience shapes functional organization in high-order visual cortex. Further, this evidence provides an alternative framework to the prominent tool embodiment theory, suggesting instead that experience with tools leads to more distinct, separable hand and tool representations.     

Deep Artificial Neural Networks Reveal a Distributed Cortical Network Encoding Propositional Sentence-Level Meaning

Understanding how and where in the brain sentence-level meaning is constructed from words presents a major scientific challenge. Recent advances have begun to explain brain activation elicited by sentences using vector models of word meaning derived from patterns of word co-occurrence in text corpora. These studies have helped map out semantic representation across a distributed brain network spanning temporal, parietal, and frontal cortex. However, it remains unclear whether activation patterns within regions reflect unified representations of sentence-level meaning, as opposed to superpositions of context-independent component words. This is because models have typically represented sentences as “bags-of-words” that neglect sentence-level structure. To address this issue, we interrogated fMRI activation elicited as 240 sentences were read by 14 participants (9 female, 5 male), using sentences encoded by a recurrent deep artificial neural-network trained on a sentence inference task (InferSent). Recurrent connections and nonlinear filters enable InferSent to transform sequences of word vectors into unified “propositional” sentence representations suitable for evaluating intersentence entailment relations. Using voxelwise encoding modeling, we demonstrate that InferSent predicts elements of fMRI activation that cannot be predicted by bag-of-words models and sentence models using grammatical rules to assemble word vectors. This effect occurs throughout a distributed network, which suggests that propositional sentence-level meaning is represented within and across multiple cortical regions rather than at any single site. In follow-up analyses, we place results in the context of other deep network approaches (ELMo and BERT) and estimate the degree of unpredicted neural signal using an “experiential” semantic model and cross-participant encoding.SIGNIFICANCE STATEMENT A modern-day scientific challenge is to understand how the human brain transforms word sequences into representations of sentence meaning. A recent approach, emerging from advances in functional neuroimaging, big data, and machine learning, is to computationally model meaning, and use models to predict brain activity. Such models have helped map a cortical semantic information-processing network. However, how unified sentence-level information, as opposed to word-level units, is represented throughout this network remains unclear. This is because models have typically represented sentences as unordered “bags-of-words.” Using a deep artificial neural network that recurrently and nonlinearly combines word representations into unified propositional sentence representations, we provide evidence that sentence-level information is encoded throughout a cortical network, rather than in a single region.     

Disrupted Intersubject Variability Architecture in Functional Connectomes in Schizophrenia

Schizophrenia (SCZ) is a highly heterogeneous disorder with remarkable intersubject variability in clinical presentations. Previous neuroimaging studies in SCZ have primarily focused on identifying group-averaged differences in the brain connectome between patients and healthy controls (HCs), largely neglecting the intersubject differences among patients. We acquired whole-brain resting-state functional MRI data from 121 SCZ patients and 183 HCs and examined the intersubject variability of the functional connectome (IVFC) in SCZ patients and HCs. Between-group differences were determined using permutation analysis. Then, we evaluated the relationship between IVFC and clinical variables in SCZ. Finally, we used datasets of patients with bipolar disorder (BD) and major depressive disorder (MDD) to assess the specificity of IVFC alteration in SCZ. The whole-brain IVFC pattern in the SCZ group was generally similar to that in HCs. Compared with the HC group, the SCZ group exhibited higher IVFC in the bilateral sensorimotor, visual, auditory, and subcortical regions. Moreover, altered IVFC was negatively correlated with age of onset, illness duration, and Brief Psychiatric Rating Scale scores and positively correlated with clinical heterogeneity. Although the SCZ shared altered IVFC in the visual cortex with BD and MDD, the alterations of IVFC in the sensorimotor, auditory, and subcortical cortices were specific to SCZ. The alterations of whole-brain IVFC in SCZ have potential implications for the understanding of the high clinical heterogeneity of SCZ and the future individualized clinical diagnosis and treatment of this disease.     

Bayesian estimation of maximum entropy model for individualized energy landscape analysis of brain state dynamics

The pairwise maximum entropy model (MEM) for resting state functional MRI (rsfMRI) has been used to generate energy landscape of brain states and to explore nonlinear brain state dynamics. Researches using MEM, however, has mostly been restricted to fixed‐effect group‐level analyses, using concatenated time series across individuals, due to the need for large samples in the parameter estimation of MEM. To mitigate the small sample problem in analyzing energy landscapes for individuals, we propose a Bayesian estimation of individual MEM using variational Bayes approximation (BMEM). We evaluated the performances of BMEM with respect to sample sizes and prior information using simulation. BMEM showed advantages over conventional maximum likelihood estimation in reliably estimating model parameters for individuals with small sample data, particularly utilizing the empirical priors derived from group data. We then analyzed individual rsfMRI of the Human Connectome Project to show the usefulness of MEM in differentiating individuals and in exploring neural correlates for human behavior. MEM and its energy landscape properties showed high subject specificity comparable to that of functional connectivity. Canonical correlation analysis identified canonical variables for MEM highly associated with cognitive scores. Inter‐individual variations of cognitive scores were also reflected in energy landscape properties such as energies, occupation times, and basin sizes at local minima. We conclude that BMEM provides an efficient method to characterize dynamic properties of individuals using energy landscape analysis of individual brain states.