A voxel based morphometry study investigating brain structural changes in first episode psychosis.

Schizophrenia (SCZ) and bipolar disorder (BP) are associated with neuropathological brain changes, which are believed to disrupt connectivity between brain processes and may have common properties. Patients at first psychotic episode are unique, as one can assess brain alterations at illness inception, when many confounders are reduced or absent. SCZ (N=25) and BP (N=24) patients were recruited in a regional first episode psychosis MRI study. VBM methods were used to study gray matter (GM) and white matter (WM) differences between patient groups and case by case matched controls. For both groups, deficits identified are more discrete than those typically reported in later stages of illness. SCZ patients showed some evidence of GM loss in cortical areas but most notable were in limbic structures such as hippocampus, thalamus and striatum and cerebellum. Consistent with disturbed neural connectivity WM alterations were also observed in limbic structures, the corpus callosum and many subgyral and sublobar regions in the parietal, temporal and frontal lobes. BP patients displayed less evidence of volume changes overall, compared to normal healthy participants, but those changes observed were primarily in WM areas which overlapped with regions identified in SCZ, including thalamus and cerebellum and subgyral and sublobar sites. At first episode of psychosis there is evidence of a neuroanatomical overlap between SCZ and BP with respect to brain structural changes, consistent with disturbed neural connectivity. There are also important differences however in that SCZ displays more extensive structural alteration.

Volume reduction of the corpus callosum and its relationship with deficits in interhemispheric transfer of information in recent-onset psychosis

The present study aimed to investigate the presence of corpus callosum (CC) volume deficits in a population-based recent-onset psychosis (ROP) sample, and whether CC volume relates to interhemispheric communication deficits. For this purpose, we used voxel-based morphometry comparisons of magnetic resonance imaging data between ROP (n = 122) and healthy control (n = 94) subjects. Subgroups (38 ROP and 39 controls) were investigated for correlations between CC volumes and performance on the Crossed Finger Localization Test (CFLT). Significant CC volume reductions in ROP subjects versus controls emerged after excluding substance misuse and non-right-handedness. CC reductions retained significance in the schizophrenia subgroup but not in affective psychoses subjects. There were significant positive correlations between CC volumes and CFLT scores in ROP subjects, specifically in subtasks involving interhemispheric communication. From these results, we can conclude that CC volume reductions are present in association with ROP. The relationship between such deficits and CFLT performance suggests that interhemispheric communication impairments are directly linked to CC abnormalities in ROP. (C) 2010 Elsevier Ireland Ltd. All rights reserved.

Cognitive performance is related to cortical grey matter volumes in early stages of schizophrenia: A population-based study of first-episode psychosis

Background: Neuropsychological deficits have been reported in association with first-episode psychosis (FEP). Reductions in grey matter (GM) volumes have been documented in FEP subjects compared to healthy controls. However, the possible inter-relationship between the findings of those two lines of research has been scarcely investigated.

Objective: To investigate the relationship between neuropsychological deficits and GM volume abnormalities in a population-based sample of FEP patients compared to healthy controls from the same geographical area.

Methods: FEP patients (n = 88) and control subjects (n = 86) were evaluated by neuropsychological assessment (Controlled Oral Word Association Test, forward and backward digit span tests) and magnetic resonance imaging using voxel-based morphometry.

Results: Single-group analyses showed that prefrontal and temporo-parietal GM volumes correlated significantly (p < 0.05, corrected) with cognitive performance in FEP patients. A similar pattern of direct correlations between neocortical GM volumes and cognitive impairment was seen in the schizophrenia subgroup (n = 48). In the control group, cognitive performance was directly correlated with GM volume in the right dorsal anterior cingulate cortex and inversely correlated with parahippocampal gyral volumes bilaterally. Interaction analyses with "group status" as a predictor variable showed significantly greater positive correlation within the left inferior prefrontal cortex (BA46) in the FEP group relative to controls, and significantly greater negative correlation within the left parahippocampal gyrus in the control group relative to FEP patients.

Conclusion: Our results indicate that cognitive deficits are directly related to brain volume abnormalities in frontal and temporo-parietal cortices in FEP subjects, most specifically in inferior portions of the dorsolateral prefrontal cortex. (C) 2009 Elsevier B.V. All rights reserved.

Lateralisation of language function in young adults born very preterm

Objective: To explore, using functional magnetic resonance imaging (MRI), the functional organisation of phonological processing in young adults born very preterm.

Subjects: Six right handed male subjects with radiological evidence of thinning of the corpus callosum were selected from a cohort of very preterm subjects. Six normal right handed male volunteers acted as controls.

Method: Blood oxygenation level dependent contrast echoplanar images were acquired over five minutes at 1.5 T while subjects performed the tasks. During the ON condition, subjects were visually presented with pairs of non-words and asked to press a key when a pair of words rhymed (phonological processing). This task alternated with the OFF condition, which required subjects to make letter case judgments of visually presented pairs of consonant letter strings (orthographic processing). Generic brain activation maps were constructed from individual images by sinusoidal regression and non-parametric testing. Between group differences in the mean power of experimental response were identified on a voxel wise basis by analysis of variance.

Results: Compared with controls, the subjects with thinning of the corpus callosum showed significantly reduced power of response in the left hemisphere, including the peristriate cortex and the cerebellum, as well as in the right parietal association area. Significantly increased power of response was observed in the right precentral gyrus and the right supplementary motor area.

Conclusions: The data show evidence of increased frontal and decreased occipital activation in male subjects with neurodevelopmental thinning of the corpus callosum, which may be due to the operation of developmental compensatory mechanisms.

Representations of specific acoustic patterns in the auditory cortex and hippocampus

Previous behavioural studies have shown that repeated presentation of a randomly chosen acoustic pattern leads to the unsupervised learning of some of its specific acoustic features. The objective of our study was to determine the neural substrate for the representation of freshly learnt acoustic patterns. Subjects first performed a behavioural task that resulted in the incidental learning of three different noise-like acoustic patterns. During subsequent high-resolution functional magnetic resonance imaging scanning, subjects were then exposed again to these three learnt patterns and to others that had not been learned. Multi-voxel pattern analysis was used to test if the learnt acoustic patterns could be 'decoded' from the patterns of activity in the auditory cortex and medial temporal lobe. We found that activity in planum temporale and the hippocampus reliably distinguished between the learnt acoustic patterns. Our results demonstrate that these structures are involved in the neural representation of specific acoustic patterns after they have been learnt.

Cross-participant modelling based on joint or disjoint feature selection: an fMRI conceptual decoding study

Multivariate classification techniques have proven to be powerful tools for distinguishing experimental conditions in single sessions of functional magnetic resonance imaging (fMRI) data. But they are vulnerable to a considerable penalty in classification accuracy when applied across sessions or participants, calling into question the degree to which fine-grained encodings are shared across subjects. Here, we introduce joint learning techniques, where feature selection is carried out using a held-out subset of a target dataset, before training a linear classifier on a source dataset. Single trials of functional MRI data from a covert property generation task are classified with regularized regression techniques to predict the semantic class of stimuli. With our selection techniques (joint ranking feature selection (JRFS) and disjoint feature selection (DJFS)), classification performance during cross-session prediction improved greatly, relative to feature selection on the source session data only. Compared with JRFS, DJFS showed significant improvements for cross-participant classification. And when using a groupwise training, DJFS approached the accuracies seen for prediction across different sessions from the same participant. Comparing several feature selection strategies, we found that a simple univariate ANOVA selection technique or a minimal searchlight (one voxel in size) is appropriate, compared with larger searchlights.

Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns

In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.