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Human evolution has been marked by a striking increase in total brain volume relative to body size. While a prominent and characteristic feature of this volumetric shift has been the disproportionate expansion of association cortex across our evolutionary lineage, descent with modification is apparent throughout all neural systems in both human and nonhuman primates. However, despite evidence for the ubiquitous and complex influence of evolutionary forces on brain biology, within the psychological sciences the vast majority of the literature on human brain evolution is entirely corticocentric. This selective focus has contributed to a flawed theoretical framework in which the evolution of association cortex is viewed as an isolated process, removed from the rest of the brain. Here, we review our current understanding of how evolutionary pressures have acted across anatomically and functionally coupled networks, highlighting the diverse set of rules and principles that govern human brain development. In doing so we challenge the systemic mischaracterization of human cognition and behavior as a competition that pits phylogenetically recent cortical territories against evolutionarily ancient subcortical and cerebellar systems. Rather, we propose a comprehensive view of human brain evolution with critical importance for the use of animal models, theory development, and network-focused approaches in the study of behavior across health and disease. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Individual differences in brain anatomy can be used to predict variability in cognitive function. Most studies to date have focused on broad population-level trends, but the extent to which the observed predictive features are shared across sexes and age groups remains to be established. While it is standard practice to account for intracranial volume (ICV) using proportion correction in both regional and whole-brain morphometric analyses, in the context of brain-behavior predictions the possible differential impact of ICV correction on anatomical features and subgroups within the population has yet to be systematically investigated. In this work, we evaluate the effect of proportional ICV correction on sex-independent and sex-specific predictive models of individual cognitive abilities across multiple anatomical properties (surface area, gray matter volume, and cortical thickness) in healthy young adults (Human Connectome Project; n=1013, 548 females) and typically developing children (Adolescent Brain Cognitive Development study; n=1823, 979 females). We demonstrate that ICV correction generally reduces predictive accuracies derived from surface area and gray matter volume, while increasing predictive accuracies based on cortical thickness in both adults and children. Furthermore, the extent to which predictive models generalize across sexes and age groups depends on ICV correction: models based on surface area and gray matter volume are more generalizable without ICV correction, while models based on cortical thickness are more generalizable with ICV correction. Finally, the observed neuroanatomical features predictive of cognitive abilities are unique across age groups regardless of ICV correction, but whether they are shared or unique across sexes (within age groups) depends on ICV correction. These findings highlight the importance of considering individual differences in ICV, and show that proportional ICV correction does not remove the effects of cranium volumes from anatomical measurements and can introduce ICV bias where previously there was none. ICV correction choices affect not just the strength of the relationships captured, but also the conclusions drawn regarding the neuroanatomical features that underlie those relationships.

Inhibitory interneurons orchestrate information flow across cortex and are implicated in psychiatric illness. Although classes of interneurons have unique functional properties and spatial distributions throughout the brain, the relative influence of interneuron subtypes on brain function, cortical specialization, and illness risk remains elusive. Here, we demonstrate stereotyped organizational properties of somatostatin and parvalbumin related transcripts within human and non-human primates. Interneuron spatial distributions recapitulate cortico-striato-thalamic functional networks and track regional differences in functional MRI signal amplitude. In the general population (n=9,627), parvalbumin-linked genes account for an enriched proportion of genome-wide heritable variance in in-vivo functional MRI signal amplitude. This relationship is spatially dependent, following the topographic organization of parvalbumin expression in independent post-mortem brain tissue. Finally, genetic risk for schizophrenia is enriched among interneuron-linked genes and predictive of cortical signal amplitude in parvalbumin-biased regions. These data indicate that the molecular genetic basis of resting-state brain function across cortex is shaped by the spatial distribution of interneuron-related transcripts and underlies individual differences in risk for schizophrenia.Key FindingsSpatial distributions of somatostatin (SST) and parvalbumin (PVALB) are negatively correlated in mature human and non-human primate cortex, paralleling patterns observed in utero.SST and PVALB are differentially expressed within distinct limbic and somato/motor cortico-striato-thalamic networks, respectively.In-vivo resting-state signal amplitude is heritable in the general population and tracks relative SST/PVALB expression across cortex.Single-nucleotide polymorphisms tied to PVALB-related genes account for an enriched proportion of the heritable variance in resting-state signal amplitude.PVALB-mediated heritability of resting-state signal amplitude in the general population is spatially heterogeneous, mirroring the cortical expression of PVALB in independent post-mortem brain tissue.Polygenic risk for schizophrenia is enriched among interneuron-linked genes and predicts resting-state signal amplitude in a manner that also follows the cortical expression of PVALB.

Structural brain abnormalities in schizophrenia have been well characterized with the application of univariate methods to magnetic resonance imaging (MRI) data. However, these traditional techniques lack sensitivity and predictive value at the individual level. Machine-learning approaches have emerged as potential diagnostic and prognostic tools. We used an anatomically and spatially regularized support vector machine (SVM) framework to categorize schizophrenia and healthy individuals based on whole-brain gray matter densities estimated using voxel-based morphometry from structural MRI scans. The regularized SVM model yielded recognition accuracy of 86.6% in the training set of 127 individuals and validation accuracy of 83.5% in an independent set of 85 individuals. A sequential region-of-interest (ROI) selection step was adopted for feature selection, improving recognition accuracy to 92.0% in the training set and 89.4% in the validation set. The combined model achieved 96.6% sensitivity and 74.1% specificity. Seven ROIs were identified as the optimal discriminatory subset: the occipital fusiform gyrus, middle frontal gyrus, pars opercularis of the inferior frontal gyrus, anterior superior temporal gyrus, superior frontal gyrus, left thalamus and left lateral ventricle. These findings demonstrate the utility of spatial and anatomical priors in SVM for neuroimaging analyses in conjunction with sequential ROI selection in the recognition of schizophrenia.

Aims and objective While loss of insight of cognitive deficits is a common phenomenon in patients with Alzheimer's disease (AD), there is a lack of consensus regarding the presence of impaired insight among patients with mild cognitive impairment (MCI). We aim to investigate the clinical, cognitive, and behavioral associations of anosognosia in AD and MCI subjects. Methods A consecutive series of 87 subjects (30 healthy older patients, 21 MCI, and 36 AD) each accompanied by a caregiver, underwent clinical assessment including the evaluation of insight using the Anosognosia Questionnaire for Dementia (AQD). We also separately assessed Intellectual Function (AQD-IF) and Behavior domains of the AQD scale. Regression models were subsequently used to investigate associations of AQD scores with cognitive and other neuropsychiatric symptoms, including depression and apathy. Results Both AD and MCI groups demonstrated significant anosognosia compared with the healthy control group. In the AD group, 55.6% had “Mild Anosognosia,” and 27.8% had “Severe Anosognosia.” In the MCI group, 42.9% showed “Mild Anosognosia,” and 9.5% had “Severe Anosognosia.” Greater levels of AQD-Total and AQD-IF were associated with lower Mini-mental state examination and higher apathy scores in the AD group. In the MCI group, caregiver burden was significantly associated with AQD-Total (p = 0.016) and AQD-IF (p = 0.039). Conclusion The results indicated that anosognosia is common in both AD and MCI patients and associated with cognitive dysfunction and apathy in AD. The findings of this study warrant further research to delineate the mechanisms of anosognosia as it poses a challenge to treatment outcomes. Copyright © 2015 John Wiley & Sons, Ltd.

Background Schizophrenia is associated with diverse white matter (WM) brain abnormalities. In this study, we sought to examine the WM microstructural findings which underlie clinical psychopathology clusters in schizophrenia and hypothesized that these symptom clusters are associated with common and unique WM tracts. Methods Overall, 76 healthy controls (HC), and 148 patients with schizophrenia (SZ) were recruited and severity of symptomatology in schizophrenia was assessed using the Positive and Negative Syndrome Scale. WM fractional anisotropy (FA) values were extracted from their diffusion tensor images. Psychopathology clusters were first determined using factor analysis and the relationship between these symptom factors and FA values were then assessed with structural equation modelling, which included covariates such as age, sex, duration of illness and medications prescribed. Results Patients with schizophrenia had reduced FA in the genu of corpus callosum (gCC) compared to HC. A three-factor model, namely Positive, Negative, Disorganised factors, was determined as the best fit for the data. All three psychopathology factors were associated with decreased FA in the gCC and bilateral cingulate gyrus. Higher Negative factor scores were uniquely associated with decreased FA in the right sagittal striatum and right superior longitudinal fasciculus. Conclusions This study found shared and specific WM changes and their associations with specific symptom clusters, which potentially allows for monitoring of such white matter findings associated with clinical presentations in schizophrenia over treatment and illness course.

Recent data from genetic and brain imaging studies have urged rethinking of bipolar disorder (BD) and schizophrenia (SCZ) as lying along a continuum of major endogenous psychoses rather than dichotomous disorders. We systematically reviewed extant studies (from January 2000 to July 2015) that directly compared neurocognitive impairments in adults with SCZ and BD. Within 36 included studies, comparable neurocognitive impairments were found in SCZ and BD involving executive functioning, working memory, verbal fluency and motor speed. The extent and severity of neurocognitive impairments in patients with schizoaffective disorder, and BD with psychotic features occupy positions intermediate between SCZ and BD without psychotic features, suggesting spectrum of neurocognitive impairments across psychotic spectrum conditions. Neurocognitive impairments correlated with socio-demographic (lower education), clinical (more hospitalizations, longer duration of illness, negative psychotic symptoms and non-remission status), treatment (antipsychotics, anti-cholinergics) variables and lower psychosocial functioning. The convergent neurocognitive findings in both conditions support a continuum concept of psychotic disorders and further research is needed to clarify common and dissimilar progression of specific neurocognitive impairments longitudinally.

Background: AD8 is a brief informant interview used to detect early cognitive change. This study evaluated the diagnostic performance of the participant-rated AD8 (p-AD8) in a predominantly Chinese population. Methods: Data on demographics, clinical, and cognitive features were collected from 73 participants with no cognitive impairment (NCI), 27 participants with mild cognitive impairments, and 78 participants with Alzheimer’s disease–informant dyads. Agreement and discriminative properties of p-AD8 were assessed. Results: AD8 scores were associated with dementia severity. Participant and informant AD8 scores were moderately correlated within dementia dyads. The p-AD8 showed good diagnostic performance in differentiating between participants with NCI and participants with cognitive impairment (sensitivity = 85.0%, specificity = 74.0%, and area under the curve = 0.80), with a cutoff score of ≥1. Combination of impairment in Mini-Mental State Examination and p-AD8 is more useful in detecting cognitive impairment than using the AD8 alone. Conclusion: Within a transcultural setting, the p-AD8 demonstrated good discriminative validity and can be used to gain a preliminary understanding of an individual’s cognitive status.