Andrea Mechelli

Comparing dynamic causal models

This article describes the use of Bayes factors for comparing dynamic causal models (DCMs). DCMs are used to make inferences about effective connectivity from functional magnetic resonance imaging (fMRI) data. These inferences, however, are contingent upon assumptions about model structure, that is, the connectivity pattern between the regions included in the model. Given the current lack of detailed knowledge on anatomical connectivity in the human brain, there are often considerable degrees of freedom when defining the connectional structure of DCMs. In addition, many plausible scientific hypotheses may exist about which connections are changed by experimental manipulation, and a formal procedure for directly comparing these competing hypotheses is highly desirable. In this article, we show how Bayes factors can be used to guide choices about model structure, both concerning the intrinsic connectivity pattern and the contextual modulation of individual connections. The combined use of Bayes factors and DCM thus allows one to evaluate competing scientific theories about the architecture of large-scale neural networks and the neuronal interactions that mediate perception and cognition.

Neurolinguistics: Structural plasticity in the bilingual brain

Humans have a unique ability to learn more than one language — a skill that is thought to be mediated by functional (rather than structural) plastic changes in the brain. Here we show that learning a second language increases the density of grey matter in the left inferior parietal cortex and that the degree of structural reorganization in this region is modulated by the proficiency attained and the age at acquisition. This relation between grey-matter density and performance may represent a general principle of brain organization.

Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: A critical review

Standard univariate analysis of neuroimaging data has revealed a host of neuroanatomical and functional differences between healthy individuals and patients suffering a wide range of neurological and psychiatric disorders. Significant only at group level however these findings have had limited clinical translation, and recent attention has turned toward alternative forms of analysis, including Support-Vector-Machine (SVM). A type of machine learning, SVM allows categorisation of an individuals previously unseen data into a predefined group using a classification algorithm, developed on a training data set. In recent years, SVM has been successfully applied in the context of disease diagnosis, transition prediction and treatment prognosis, using both structural and functional neuroimaging data. Here we provide a brief overview of the method and review those studies that applied it to the investigation of Alzheimers disease, schizophrenia, major depression, bipolar disorder, presymptomatic Huntingtons disease, Parkinsons disease and autistic spectrum disorder. We conclude by discussing the main theoretical and practical challenges associated with the implementation of this method into the clinic and possible future directions.

The hubs of the human connectome are generally implicated in the anatomy of brain disorders

See Sporns (doi:10.1093/brain/awu148) for a scientific commentary on this article. Brain networks contain a minority of highly connected hub nodes with high topological value and biological cost. Using network analysis of DTI data from healthy volunteers, and meta-analyses of published MRI studies in 26 brain disorders, Crossley et al. show that lesions across disorders tend to be concentrated at hubs.

Structural Covariance in the Human Cortex

The morphology of the human cortex varies remarkably across individuals, regardless of overall brain size. It is currently unclear whether related cortical regions covary in gray matter density, as a result of mutually trophic influences or common experience-related plasticity. We acquired a structural magnetic resonance imaging scan from 172 subjects and extracted the regional gray matter densities from 12 readily identifiable regions of interest involved in sensorimotor or higher-order cognitive functions. We then used these values to predict regional densities in the remaining areas of the cortex, using voxel-based morphometry. This revealed patterns of positive and negative covariance that provide insight into the topographical organization of multiple cortical regions. We report that the gray matter density of a region is a good predictor of the density of the homotopic region in the contralateral hemisphere, with the striking exception of primary visual cortex. Whereas some regions express patterns of regional covariance that are mirror symmetrical relative to the interhemispheric fissure, other regions express asymmetric patterns of regional covariance. Finally, patterns of covariance are remarkably consistent between males and females, with the exception of the left amygdala, which is positively associated with the left and right anterior inferior temporal cortex in males and with the right angular gyrus in females. Our study establishes that the density of different cortical regions is coordinated within an individual. The coordinated variations we report are likely to be determined by both genetic and environmental factors and may be the basis for differences in individual behavior.

Neuroimaging Studies of Word and Pseudoword Reading: Consistencies, Inconsistencies, and Limitations

Several functional neuroimaging studies have compared words and pseudowords to test different cognitive models of reading. There are difficulties with this approach, however, because cognitive models do not make clear-cut predictions at the neural level. Therefore, results can only be interpreted on the basis of prior knowledge of cognitive anatomy. Furthermore, studies comparing words and pseudowords have produced inconsistent results. The inconsistencies could reflect false-positive results due to the low statistical thresholds applied or confounds from nonlexical aspects of the stimuli. Alternatively, they may reflect true effects that are inconsistent across subjects; dependent on experimental parameters such as stimulus rate or duration; or not replicated across studies because of insufficient statistical power. In this fMRI study, we investigate consistent and inconsistent differences between word and pseudoword reading in 20 subjects, and distinguish between effects associated with increases and decreases in activity relative to fixation. In addition, the interaction of word type with stimulus duration is explored. We find that words and pseudowords activate the same set of regions relative to fixation, and within this system, there is greater activation for pseudowords than words in the left frontal operculum, left posterior inferior temporal gyrus, and the right cerebellum. The only effects of words relative to pseudowords consistent over subjects are due to decreases in activity for pseudowords relative to fixation; and there are no significant interactions between word type and stimulus duration. Finally, we observe inconsistent but highly significant effects of word type at the individual subject level. These results (i) illustrate that pseudowords place increased demands on areas that have previously been linked to lexical retrieval, and (ii) highlight the importance of including one or more baselines to qualify word type effects. Furthermore, (iii) they suggest that inconsistencies observed in the previous literature may result from effects arising from a small number of subjects only.

Short-term Effects of Antipsychotic Treatment on Cerebral Function in Drug-Naive First-Episode Schizophrenia Revealed by “Resting State” Functional Magnetic Resonance Imaging

CONTEXT Most of what we know about antipsychotic drug effects is at the receptor level, distal from the neural system effects that mediate their clinical efficacy. Studying cerebral function in antipsychotic-naive patients with schizophrenia before and after pharmacotherapy can enhance understanding of the therapeutic mechanisms of these clinically effective treatments. OBJECTIVE To examine alterations of regional and neural network function in antipsychotic-naive patients with first-episode schizophrenia before and after treatment with second-generation antipsychotic medication. DESIGN Case-control study. SETTING Huaxi MR Research Center and Mental Health Centre of the West China Hospital. PARTICIPANTS Thirty-four antipsychotic-naive patients with first-episode schizophrenia were scanned using gradient-echo echo-planar imaging while in a resting state. After 6 weeks of antipsychotic treatment, patients were rescanned. Thirty-four matched healthy control subjects were studied at baseline for comparison purposes. MAIN OUTCOME MEASURES The amplitude of low-frequency fluctuations (ALFF) of blood oxygen level-dependent signals, believed to reflect spontaneous neural activity, was used to characterize regional cerebral function. Functional connectivity across brain regions was evaluated using a seed voxel correlation approach and an independent component analysis. Changes in these measures after treatment were examined to characterize effects of antipsychotic drugs on regional function and functional integration. RESULTS After short-term treatment with second-generation antipsychotic medications, patients showed increased ALFF, particularly in the bilateral prefrontal and parietal cortex, left superior temporal cortex, and right caudate nucleus. Increased regional ALFF was associated with a reduction of clinical symptoms, and a widespread attenuation in functional connectivity was observed that was correlated with increased regional ALFF. CONCLUSIONS We demonstrate for the first time, to our knowledge, that widespread increased regional synchronous neural activity occurs after antipsychotic therapy, accompanied by decreased integration of function across widely distributed neural networks. These findings contribute to the understanding of the complex systems-level effects of antipsychotic drugs.

Size matters: Increased grey matter in boys with conduct problems and callous–unemotional traits

Brain imaging studies of adults with psychopathy have identified structural and functional abnormalities in limbic and prefrontal regions that are involved in emotion recognition, decision-making, morality and empathy. Among children with conduct problems, a small subgroup presents callous-unemotional traits thought to be antecedents of psychopathy. No structural brain imaging study has examined this subgroup of children. The present study used voxel-based morphometry to compare whole brain grey matter volumes and concentrations of boys with elevated levels of callous-unemotional conduct problems and typically developing boys and explored four a priori regions of interest. sMRI scans were collected from 23 boys with elevated levels of callous-unemotional conduct problems (mean age = 11 years 8 months) and 25 typically developing boys (mean age = 11 years 6 months) selected from a community sample of children. Data were analysed using optimized voxel-based morphometry. Study-specific probability maps were created and four a priori regions of interest identified (orbitofrontal, anterior cingulate and anterior insular cortices and amygdala). Both grey matter volume and concentration were examined controlling for cognitive ability and hyperactivity-inattention symptoms. Boys with callous-unemotional conduct problems, as compared with typically developing boys, presented increased grey matter concentration in the medial orbitofrontal and anterior cingulate cortices, as well as increased grey matter volume and concentration in the temporal lobes bilaterally. These findings may indicate a delay in cortical maturation in several brain areas implicated in decision making, morality and empathy in boys with callous-unemotional conduct problems.

Abnormal amygdala-prefrontal effective connectivity to happy faces differentiates bipolar from major depression.

BACKGROUND Bipolar disorder is frequently misdiagnosed as major depressive disorder, delaying appropriate treatment and worsening outcome for many bipolar individuals. Emotion dysregulation is a core feature of bipolar disorder. Measures of dysfunction in neural systems supporting emotion regulation might therefore help discriminate bipolar from major depressive disorder. METHODS Thirty-one depressed individuals-15 bipolar depressed (BD) and 16 major depressed (MDD), DSM-IV diagnostic criteria, ages 18-55 years, matched for age, age of illness onset, illness duration, and depression severity-and 16 age- and gender-matched healthy control subjects performed two event-related paradigms: labeling the emotional intensity of happy and sad faces, respectively. We employed dynamic causal modeling to examine significant among-group alterations in effective connectivity (EC) between right- and left-sided neural regions supporting emotion regulation: amygdala and orbitomedial prefrontal cortex (OMPFC). RESULTS During classification of happy faces, we found profound and asymmetrical differences in EC between the OMPFC and amygdala. Left-sided differences involved top-down connections and discriminated between depressed and control subjects. Furthermore, greater medication load was associated with an amelioration of this abnormal top-down EC. Conversely, on the right side the abnormality was in bottom-up EC that was specific to bipolar disorder. These effects replicated when we considered only female subjects. CONCLUSIONS Abnormal, left-sided, top-down OMPFC-amygdala and right-sided, bottom-up, amygdala-OMPFC EC during happy labeling distinguish BD and MDD, suggesting different pathophysiological mechanisms associated with the two types of depression.

Reading and reading disturbance

Recent functional neuroimaging studies are generating novel insights into our knowledge of skilled and disturbed reading. In neurologically normal subjects, a double dissociation in neural activation in response to reading words and pseudowords has been revealed that corresponds to that observed in the comparison of semantic and phonological tasks. In patients with acquired dyslexia, functional imaging is demonstrating re-organisation within the reading system; in developmental dyslexia, functional imaging is being used to identify the impact of rehabilitation. Together, these findings have implications for cognitive models of reading that have previously relied on input from behavioural data.