Voxel-based magnetic resonance imaging investigation of poor and preserved clinical insight in people with schizophrenia

AIM To define regional grey-matter abnormalities in schizophrenia patients with poor insight (Insight-), relative to patients with preserved clinical insight (Insight+), and healthy controls. METHODS Forty stable schizophrenia outpatients (20 Insight- and 20 Insight+) and 20 healthy controls underwent whole brain magnetic resonance imaging (MRI). Insight in all patients was assessed using the Birchwood Insight Scale (BIS; a self-report measure). The two patient groups were pre-selected to match on most clinical and demographic parameters but, by design, they had markedly distinct BIS scores. Voxel-based morphometry employed in SPM8 was used to examine group differences in grey matter volumes across the whole brain. RESULTS The three participant groups were comparable in age [F(2,57) = 0.34, P = 0.71] and the patient groups did not differ in age at illness onset [t(38) = 0.87, P = 0.39]. Insight- and Insight+ patient groups also did not differ in symptoms on the Positive and Negative Syndromes scale (PANSS): Positive symptoms [t(38) = 0.58, P = 0.57], negative symptoms [t(38) = 0.61, P = 0.55], general psychopathology [t(38) = 1.30, P = 0.20] and total PANSS scores [t(38) = 0.21, P = 0.84]. The two patient groups, as expected, varied significantly in the level of BIS-assessed insight [t(38) = 12.11, P 0.20) from each other. CONCLUSION Our findings demonstrate a clear association between poor clinical insight and smaller fronto-temporal, occipital and cerebellar grey matter volumes in stable long-term schizophrenia patients

Cognitive decline in Parkinson disease

This is the author accepted manuscript. the final version is available from Nature Research via the DOI in this recordDementia is a frequent problem encountered in advanced stages of Parkinson disease (PD). In recent years, research has focused on the pre-dementia stages of cognitive impairment in PD, including mild cognitive impairment (MCI). Several longitudinal studies have shown that MCI is a harbinger of dementia in PD, although the course is variable, and stabilization of cognition — or even reversal to normal cognition — is not uncommon. In addition to limbic and cortical spread of Lewy pathology, several other mechanisms are likely to contribute to cognitive decline in PD, and a variety of biomarker studies, some using novel structural and functional imaging techniques, have documented in vivo brain changes associated with cognitive impairment. The evidence consistently suggests that low cerebrospinal fluid levels of amyloid-β42, a marker of comorbid Alzheimer disease (AD), predict future cognitive decline and dementia in PD. Emerging genetic evidence indicates that in addition to the APOE*ε4 allele (an established risk factor for AD), GBA mutations and SCNA mutations and triplications are associated with cognitive decline in PD, whereas the findings are mixed for MAPT polymorphisms. Cognitive enhancing medications have some effect in PD dementia, but no convincing evidence that progression from MCI to dementia can be delayed or prevented is available, although cognitive training has shown promising results.National Institute for Health Research (NIHR)Royal SocietyWolfson Foundatio

The psychosis spectrum in Parkinson disease

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordIn 2007, the clinical and research profile of illusions, hallucinations, delusions and related symptoms in Parkinson disease (PD) was raised with the publication of a consensus definition of PD psychosis. Symptoms that were previously deemed benign and clinically insignificant were incorporated into a continuum of severity, leading to the rapid expansion of literature focusing on clinical aspects, mechanisms and treatment. Here, we review this literature and the evolving view of PD psychosis. Key topics include the prospective risk of dementia in individuals with PD psychosis, and the causal and modifying effects of PD medication. We discuss recent developments, including recognition of an increase in the prevalence of psychosis with disease duration, addition of new visual symptoms to the psychosis continuum, and identification of frontal executive, visual perceptual and memory dysfunction at different disease stages. In addition, we highlight novel risk factors-for example, autonomic dysfunction-that have emerged from prospective studies, structural MRI evidence of frontal, parietal, occipital and hippocampal involvement, and approval of pimavanserin for the treatment of PD psychosis. The accumulating evidence raises novel questions and directions for future research to explore the clinical management and biomarker potential of PD psychosis.National Institute for Health Research (NIHR

Guidelines for the recording and evaluation of pharmaco-EEG data in man: the International Pharmaco-EEG Society (IPEG)

The International Pharmaco-EEG Society (IPEG) presents updated guidelines summarising the requirements for the recording and computerised evaluation of pharmaco-EEG data in man. Since the publication of the first pharmaco-EEG guidelines in 1982, technical and data processing methods have advanced steadily, thus enhancing data quality and expanding the palette of tools available to investigate the action of drugs on the central nervous system (CNS), determine the pharmacokinetic and pharmacodynamic properties of novel therapeutics and evaluate the CNS penetration or toxicity of compounds. However, a review of the literature reveals inconsistent operating procedures from one study to another. While this fact does not invalidate results per se, the lack of standardisation constitutes a regrettable shortcoming, especially in the context of drug development programmes. Moreover, this shortcoming hampers reliable comparisons between outcomes of studies from different laboratories and hence also prevents pooling of data which is a requirement for sufficiently powering the validation of novel analytical algorithms and EEG-based biomarkers. The present updated guidelines reflect the consensus of a global panel of EEG experts and are intended to assist investigators using pharmaco-EEG in clinical research, by providing clear and concise recommendations and thereby enabling standardisation of methodology and facilitating comparability of data across laboratories

Mapping depression in schizophrenia: A functional magnetic resonance imaging study

Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. Depressive symptoms are common in schizophrenia, often left untreated, and associated with a high relapse rate, suicidal ideation, increased mortality, reduced social adjustment and poor quality of life. The neural mechanisms underlying depression in psychosis are poorly understood. Given reports of altered brain response to negative facial affect in depressive disorders, we examined brain response to emotive facial expressions in relation to levels of depression in people with psychosis. Seventy outpatients (final N = 63) and 20 healthy participants underwent functional magnetic resonance imaging during an implicit affect processing task involving presentation of facial expressions of fear, anger, happiness as well as neutral expressions and a (no face) control condition. All patients completed Beck Depression Inventory (BDI-II) and had their symptoms assessed on the Positive and Negative Syndrome Scale (PANSS). In patients, depression (BDI-II) scores associated positively with activation of the left thalamus, extending to the putamen-globus pallidus, insula, inferior-middle frontal and para-post-pre-central gyri during fearful expressions. Furthermore, patients with moderate-to-severe depression had significantly higher activity in these brain regions during fearful expressions relative to patients with no, minimal, or mild depression and healthy participants. The study provides first evidence of enhanced brain response to fearful facial expressions, which signal an uncertain source of threat in the environment, in patients with psychosis and a high level of self-reported depression

Migraine aura: retracting particle-like waves in weakly susceptible cortex

Cortical spreading depression (SD) has been suggested to underlie migraine aura. Despite a precise match in speed, the spatio-temporal patterns of SD and aura symptoms on the cortical surface ordinarily differ in aspects of size and shape. We show that this mismatch is reconciled by utilizing that both pattern types bifurcate from an instability point of generic reaction-diffusion models. To classify these spatio-temporal pattern we suggest a susceptibility scale having the value [sigma]=1 at the instability point. We predict that human cortex is only weakly susceptible to SD ([sigma]<1), and support this prediction by directly matching visual aura symptoms with anatomical landmarks using fMRI retinotopic mapping. We discuss the increased dynamical repertoire of cortical tissue close to [sigma]=1, in particular, the resulting implications on migraine pharmacology that is hitherto tested in the regime ([sigma]>>1), and potentially silent aura occurring below a second bifurcation point at [sigma]=0 on the susceptible scale

Charles Bonnet Syndrome:Evidence for a Generative Model in the Cortex?

Several theories propose that the cortex implements an internal model to explain, predict, and learn about sensory data, but the nature of this model is unclear. One condition that could be highly informative here is Charles Bonnet syndrome (CBS), where loss of vision leads to complex, vivid visual hallucinations of objects, people, and whole scenes. CBS could be taken as indication that there is a generative model in the brain, specifically one that can synthesise rich, consistent visual representations even in the absence of actual visual input. The processes that lead to CBS are poorly understood. Here, we argue that a model recently introduced in machine learning, the deep Boltzmann machine (DBM), could capture the relevant aspects of (hypothetical) generative processing in the cortex. The DBM carries both the semantics of a probabilistic generative model and of a neural network. The latter allows us to model a concrete neural mechanism that could underlie CBS, namely, homeostatic regulation of neuronal activity. We show that homeostatic plasticity could serve to make the learnt internal model robust against e.g. degradation of sensory input, but overcompensate in the case of CBS, leading to hallucinations. We demonstrate how a wide range of features of CBS can be explained in the model and suggest a potential role for the neuromodulator acetylcholine. This work constitutes the first concrete computational model of CBS and the first application of the DBM as a model in computational neuroscience. Our results lend further credence to the hypothesis of a generative model in the brain

Spatio-Temporal Brain Mapping of Motion-Onset VEPs Combined with fMRI and Retinotopic Maps

Neuroimaging studies have identified several motion-sensitive visual areas in the human brain, but the time course of their activation cannot be measured with these techniques. In the present study, we combined electrophysiological and neuroimaging methods (including retinotopic brain mapping) to determine the spatio-temporal profile of motion-onset visual evoked potentials for slow and fast motion stimuli and to localize its neural generators. We found that cortical activity initiates in the primary visual area (V1) for slow stimuli, peaking 100 ms after the onset of motion. Subsequently, activity in the mid-temporal motion-sensitive areas, MT+, peaked at 120 ms, followed by peaks in activity in the more dorsal area, V3A, at 160 ms and the lateral occipital complex at 180 ms. Approximately 250 ms after stimulus onset, activity fast motion stimuli was predominant in area V6 along the parieto-occipital sulcus. Finally, at 350 ms (100 ms after the motion offset) brain activity was visible again in area V1. For fast motion stimuli, the spatio-temporal brain pattern was similar, except that the first activity was detected at 70 ms in area MT+. Comparing functional magnetic resonance data for slow vs. fast motion, we found signs of slow-fast motion stimulus topography along the posterior brain in at least three cortical regions (MT+, V3A and LOR)

Differential Development of Human Brain White Matter Tracts

Neuroscience is increasingly focusing on developmental factors related to human structural and functional connectivity. Unfortunately, to date, diffusion-based imaging approaches have only contributed modestly to these broad objectives, despite the promise of diffusion-based tractography. Here, we report a novel data-driven approach to detect similarities and differences among white matter tracts with respect to their developmental trajectories, using 64-direction diffusion tensor imaging. Specifically, using a cross-sectional sample comprising 144 healthy individuals (7 to 48 years old), we applied k-means cluster analysis to separate white matter voxels based on their age-related trajectories of fractional anisotropy. Optimal solutions included 5-, 9- and 14-clusters. Our results recapitulate well-established tracts (e.g., internal and external capsule, optic radiations, corpus callosum, cingulum bundle, cerebral peduncles) and subdivisions within tracts (e.g., corpus callosum, internal capsule). For all but one tract identified, age-related trajectories were curvilinear (i.e., inverted ‘U-shape’), with age-related increases during childhood and adolescence followed by decreases in middle adulthood. Identification of peaks in the trajectories suggests that age-related losses in fractional anisotropy occur as early as 23 years of age, with mean onset at 30 years of age. Our findings demonstrate that data-driven analytic techniques may be fruitfully applied to extant diffusion tensor imaging datasets in normative and neuropsychiatric samples