Abstract
Sex differences in the human brain are of interest for many reasons: for example, there are sex differences in the observed prevalence of psychiatric disorders and in some psychological ...traits that brain differences might help to explain. We report the largest single-sample study of structural and functional sex differences in the human brain (2750 female, 2466 male participants; mean age 61.7 years, range 44-77 years). Males had higher raw volumes, raw surface areas, and white matter fractional anisotropy; females had higher raw cortical thickness and higher white matter tract complexity. There was considerable distributional overlap between the sexes. Subregional differences were not fully attributable to differences in total volume, total surface area, mean cortical thickness, or height. There was generally greater male variance across the raw structural measures. Functional connectome organization showed stronger connectivity for males in unimodal sensorimotor cortices, and stronger connectivity for females in the default mode network. This large-scale study provides a foundation for attempts to understand the causes and consequences of sex differences in adult brain structure and function.
The Predictive Coding Account of Psychosis Sterzer, Philipp; Adams, Rick A.; Fletcher, Paul ...
Biological psychiatry (1969),
11/2018, Letnik:
84, Številka:
9
Journal Article
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Fueled by developments in computational neuroscience, there has been increasing interest in the underlying neurocomputational mechanisms of psychosis. One successful approach involves predictive ...coding and Bayesian inference. Here, inferences regarding the current state of the world are made by combining prior beliefs with incoming sensory signals. Mismatches between prior beliefs and incoming signals constitute prediction errors that drive new learning. Psychosis has been suggested to result from a decreased precision in the encoding of prior beliefs relative to the sensory data, thereby garnering maladaptive inferences. Here, we review the current evidence for aberrant predictive coding and discuss challenges for this canonical predictive coding account of psychosis. For example, hallucinations and delusions may relate to distinct alterations in predictive coding, despite their common co-occurrence. More broadly, some studies implicate weakened prior beliefs in psychosis, and others find stronger priors. These challenges might be answered with a more nuanced view of predictive coding. Different priors may be specified for different sensory modalities and their integration, and deficits in each modality need not be uniform. Furthermore, hierarchical organization may be critical. Altered processes at lower levels of a hierarchy need not be linearly related to processes at higher levels (and vice versa). Finally, canonical theories do not highlight active inference—the process through which the effects of our actions on our sensations are anticipated and minimized. It is possible that conflicting findings might be reconciled by considering these complexities, portending a framework for psychosis more equipped to deal with its many manifestations.
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•Autism and related disorders display an overlap in presentation and neuropathology.•Impaired synaptic, metabolic, proliferation, apoptosis and immune pathways are repeatedly ...implicated.•Dysregulated non-coding RNAs and aberrant epigenetic profiles are consistently reported.•Strong evidence for GABAergic, glutamatergic and glial dysfunction is provided.•The cerebellum and frontal cortex are most consistently implicated.
Post-mortem studies allow for the direct investigation of brain tissue in those with autism and related disorders. Several review articles have focused on aspects of post-mortem abnormalities but none has brought together the entire post-mortem literature. Here, we systematically review the evidence from post-mortem studies of autism, and of related disorders that present with autistic features. The literature consists of a small body of studies with small sample sizes, but several remarkably consistent findings are evident. Cortical layering is largely undisturbed, but there are consistent reductions in minicolumn numbers and aberrant myelination. Transcriptomics repeatedly implicate abberant synaptic, metabolic, proliferation, apoptosis and immune pathways. Sufficient replicated evidence is available to implicate non-coding RNA, aberrant epigenetic profiles, GABAergic, glutamatergic and glial dysfunction in autism pathogenesis. Overall, the cerebellum and frontal cortex are most consistently implicated, sometimes revealing distinct region-specific alterations. The literature on related disorders such as Rett syndrome, Fragile X and copy number variations (CNVs) predisposing to autism is particularly small and inconclusive. Larger studies, matched for gender, developmental stage, co-morbidities and drug treatment are required.
The usefulness of current psychiatric classification, which is based on ICD/DSM categorical diagnoses, remains questionable. A promising alternative has been put forward as the “transdiagnostic” ...approach. This is expected to cut across existing categorical diagnoses and go beyond them, to improve the way we classify and treat mental disorders. This systematic review explores whether self‐defining transdiagnostic research meets such high expectations. A multi‐step Web of Science literature search was performed according to an a priori protocol, to identify all studies that used the word “transdiagnostic” in their title, up to May 5, 2018. Empirical variables which indexed core characteristics were extracted, complemented by a bibliometric and conceptual analysis. A total of 111 studies were included. Most studies were investigating interventions, followed by cognition and psychological processes, and neuroscientific topics. Their samples ranged from 15 to 91,199 (median 148) participants, with a mean age from 10 to more than 60 (median 33) years. There were several methodological inconsistencies relating to the definition of the gold standard (DSM/ICD diagnoses), of the outcome measures and of the transdiagnostic approach. The quality of the studies was generally low and only a few findings were externally replicated. The majority of studies tested transdiagnostic features cutting across different diagnoses, and only a few tested new classification systems beyond the existing diagnoses. About one fifth of the studies were not transdiagnostic at all, because they investigated symptoms and not disorders, a single disorder, or because there was no diagnostic information. The bibliometric analysis revealed that transdiagnostic research largely restricted its focus to anxiety and depressive disorders. The conceptual analysis showed that transdiagnostic research is grounded more on rediscoveries than on true innovations, and that it is affected by some conceptual biases. To date, transdiagnostic approaches have not delivered a credible paradigm shift that can impact classification and clinical care. Practical “TRANSD”iagnostic recommendations are proposed here to guide future research in this field.
Background It is well established that schizophrenia is associated with structural brain abnormalities, but whether these are static or progress over time remains controversial. Methods A systematic ...review of longitudinal volumetric studies using region-of-interest structural magnetic resonance imaging in patients with schizophrenia and healthy control subjects. The percentage change in volume between scans for each brain region of interest was obtained, and data were combined using random effects meta-analysis. Results Twenty-seven studies were included in the meta-analysis, with 928 patients and 867 control subjects, and 32 different brain regions of interest. Subjects with schizophrenia showed significantly greater decreases over time in whole brain volume, whole brain gray matter, frontal gray and white matter, parietal white matter, and temporal white matter volume, as well as larger increases in lateral ventricular volume, than healthy control subjects. The time between baseline and follow-up magnetic resonance imaging scans ranged from 1 to 10 years. The differences between patients and control subjects in annualized percentage volume change were −.07% for whole brain volume, −.59% for whole brain gray matter, −.32% for frontal white matter, −.32% for parietal white matter, −.39% for temporal white matter, and +.36% for bilateral lateral ventricles. Conclusions These findings suggest that schizophrenia is associated with progressive structural brain abnormalities, affecting both gray and white matter. We found no evidence to suggest progressive medial temporal lobe involvement but did find evidence that this may be partly explained by heterogeneity between studies in patient age and illness duration. The causes and clinical correlates of these progressive brain changes should now be the focus of investigation.
Recent theories propose that schizophrenia/schizotypy and autistic spectrum disorder are related to impairments in Bayesian inference that is, how the brain integrates sensory information ...(likelihoods) with prior knowledge. However existing accounts fail to clarify: (i) how proposed theories differ in accounts of ASD vs. schizophrenia and (ii) whether the impairments result from weaker priors or enhanced likelihoods. Here, we directly address these issues by characterizing how 91 healthy participants, scored for autistic and schizotypal traits, implicitly learned and combined priors with sensory information. This was accomplished through a visual statistical learning paradigm designed to quantitatively assess variations in individuals' likelihoods and priors. The acquisition of the priors was found to be intact along both traits spectra. However, autistic traits were associated with more veridical perception and weaker influence of expectations. Bayesian modeling revealed that this was due, not to weaker prior expectations, but to more precise sensory representations.
Aims:
We aim to identify the prevalence and management strategies of nine clinically important categories of antipsychotic adverse effects, namely: extrapyramidal symptoms; sedation; weight gain; ...type II diabetes; hyperprolactinaemia; metabolic syndrome, dyslipidaemia; sexual dysfunction; and cardiovascular effects.
Background:
Antipsychotic drugs are widely prescribed for schizophrenia and other mental disorders. The adverse effects of antipsychotics are common, with a potential negative impact on adherence and engagement. Despite this, the scientific study of the prevalence or management of adverse antipsychotic effects is a neglected area.
Method:
A systematic review was undertaken using pre-defined search criteria and three databases, with hand searching of citations and references. Inclusion was agreed on by two independent researchers after review of abstracts or full text. Quality analysis of included studies was conducted using pre-agreed criteria.
Results:
In total, 53 studies met inclusion criteria, revealing the following: (1) antipsychotic polypharmacy was associated with increased frequency of adverse effects, and (2) a longer duration of treatment is associated with greater severity (e.g. higher BMI); (3) clozapine was more strongly associated with metabolic disturbance than other antipsychotics in three studies and olanzapine was associated with the most weight gain in three studies; (4) hyperprolactinaemia was more common in women than men, but 50% men noted sexual dysfunction versus 25–50% in women; (5) despite clinical guideline recommendations there is a low rate of baseline testing for lipids and glucose; and (6) seven studies described adverse effect management strategies, but only two examined their efficacy – one found a significant reduction in weight with non-pharmacological group therapy and the other found a significant reduction in dyslipidaemia with statins.
Conclusions:
Antipsychotic adverse effects are diverse and frequently experienced, but are not often systematically assessed. There is a need for further scientific study concerning the management of these side effects.
The characterization of gray matter morphology of individual brains is an important issue in neuroscience. Graph theory has been used to describe cortical morphology, with networks based on ...covariation of gray matter volume or thickness between cortical areas across people. Here, we extend this research by proposing a new method that describes the gray matter morphology of an individual cortex as a network. In these large-scale morphological networks, nodes represent small cortical regions, and edges connect regions that have a statistically similar structure. The method was applied to a healthy sample (n = 14, scanned at 2 different time points). For all networks, we described the spatial degree distribution, average minimum path length, average clustering coefficient, small world property, and betweenness centrality (BC). Finally, we studied the reproducibility of all these properties. The networks showed more clustering than random networks and a similar minimum path length, indicating that they were "small world." The spatial degree and BC distributions corresponded closely to those from group-derived networks. All network property values were reproducible over the 2 time points examined. Our results demonstrate that intracortical similarities can be used to provide a robust statistical description of individual gray matter morphology.
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