fMRI connectivity as a biomarker of antipsychotic treatment response: A systematic review.

BACKGROUND: Antipsychotic drugs are the first-choice therapy for psychotic episodes, but antipsychotic treatment response (AP-R) is unpredictable and only becomes clear after weeks of therapy. A biomarker for AP-R is currently unavailable. We reviewed the evidence for the hypothesis that functional magnetic resonance imaging functional connectivity (fMRI-FC) is a predictor of AP-R or could serve as a biomarker for AP-R in psychosis. METHOD: A systematic review of longitudinal fMRI studies examining the predictive performance and relationship between FC and AP-R was performed following PRISMA guidelines. Technical and clinical aspects were critically assessed for the retrieved studies. We addressed three questions: Q1) is baseline fMRI-FC related to subsequent AP-R; Q2) is AP-R related to a change in fMRI-FC; and Q3) can baseline fMRI-FC predict subsequent AP-R? RESULTS: In total, 28 articles were included. Most studies were of good quality. fMRI-FC analysis pipelines included seed-based-, independent component- / canonical correlation analysis, network-based statistics, and graph-theoretical approaches. We found high heterogeneity in methodological approaches and results. For Q1 (N = 17) and Q2 (N = 18), the most consistent evidence was found for FC between the striatum and ventral attention network as a potential biomarker of AP-R. For Q3 (N = 9) accuracy's varied form 50 till 93%, and prediction models were based on FC between various brain regions. CONCLUSION: The current fMRI-FC literature on AP-R is hampered by heterogeneity of methodological approaches. Methodological uniformity and further improvement of the reliability and validity of fMRI connectivity analysis is needed before fMRI-FC analysis can have a place in clinical applications of antipsychotic treatment.

Acoustic speech markers for schizophrenia-spectrum disorders: a diagnostic and symptom-recognition tool.

BACKGROUND: Clinicians routinely use impressions of speech as an element of mental status examination. In schizophrenia-spectrum disorders, descriptions of speech are used to assess the severity of psychotic symptoms. In the current study, we assessed the diagnostic value of acoustic speech parameters in schizophrenia-spectrum disorders, as well as its value in recognizing positive and negative symptoms. METHODS: Speech was obtained from 142 patients with a schizophrenia-spectrum disorder and 142 matched controls during a semi-structured interview on neutral topics. Patients were categorized as having predominantly positive or negative symptoms using the Positive and Negative Syndrome Scale (PANSS). Acoustic parameters were extracted with OpenSMILE, employing the extended Geneva Acoustic Minimalistic Parameter Set, which includes standardized analyses of pitch (F0), speech quality and pauses. Speech parameters were fed into a random forest algorithm with leave-ten-out cross-validation to assess their value for a schizophrenia-spectrum diagnosis, and PANSS subtype recognition. RESULTS: The machine-learning speech classifier attained an accuracy of 86.2% in classifying patients with a schizophrenia-spectrum disorder and controls on speech parameters alone. Patients with predominantly positive v. negative symptoms could be classified with an accuracy of 74.2%. CONCLUSIONS: Our results show that automatically extracted speech parameters can be used to accurately classify patients with a schizophrenia-spectrum disorder and healthy controls, as well as differentiate between patients with predominantly positive v. negatives symptoms. Thus, the field of speech technology has provided a standardized, powerful tool that has high potential for clinical applications in diagnosis and differentiation, given its ease of comparison and replication across samples.

Quantified language connectedness in schizophrenia-spectrum disorders.

Language abnormalities are a core symptom of schizophrenia-spectrum disorders and could serve as a potential diagnostic marker. Natural language processing enables quantification of language connectedness, which may be lower in schizophrenia-spectrum disorders. Here, we investigated connectedness of spontaneous speech in schizophrenia-spectrum patients and controls and determine its accuracy in classification. Using a semi-structured interview, speech of 50 patients with a schizophrenia-spectrum disorder and 50 controls was recorded. Language connectedness in a semantic word2vec model was calculated using consecutive word similarity in moving windows of increasing sizes (2-20 words). Mean, minimal and variance of similarity were calculated per window size and used in a random forest classifier to distinguish patients and healthy controls. Classification based on connectedness reached 85% cross-validated accuracy, with 84% specificity and 86% sensitivity. Features that best discriminated patients from controls were variance of similarity at window sizes between 5 and 10. We show impaired connectedness in spontaneous speech of patients with schizophrenia-spectrum disorders even in patients with low ratings of positive symptoms. Effects were most prominent at the level of sentence connectedness. The high sensitivity, specificity and tolerability of this method show that language analysis is an accurate and feasible digital assistant in diagnosing schizophrenia-spectrum disorders.

Reward-related brain structures are smaller in patients with schizophrenia and comorbid metabolic syndrome.

OBJECTIVE: Metabolic syndrome (MS) is highly prevalent in schizophrenia and often a consequence of unhealthy behaviour. Reward-related brain areas might be associated with MS, since they play a major role in regulating health behaviour. This study examined the relationship between MS and brain volumes related to the reward system in schizophrenia. METHOD: We included patients with schizophrenia, with MS (MS+; n = 23), patients with schizophrenia, without MS (MS-; n = 48), and healthy controls (n = 54). Global brain volumes and volumes of (sub)cortical areas, part of the reward circuit, were compared between patients and controls. In case of a significant brain volume difference between patients and controls, the impact of MS in schizophrenia was examined. RESULTS: Patients had smaller total brain (TB; P = 0.001), GM (P = 0.010), larger ventricles (P = 0.026), and smaller reward circuit volume (P < 0.001) than controls. MS+ had smaller TB (P = 0.017), GM (P = 0.008), larger ventricles (P = 0.015), and smaller reward circuit volume (P = 0.002) than MS-. MS+ had smaller orbitofrontal cortex (OFC; P = 0.002) and insula volumes (P = 0.005) and smaller OFC (P = 0.008) and insula cortical surface area (P = 0.025) compared to MS-. CONCLUSION: In schizophrenia, structural brain volume reductions in areas of the reward circuitry appear to be related to comorbid MS.

Clinical use of semantic space models in psychiatry and neurology: A systematic review and meta-analysis.

Verbal communication disorders are a hallmark of many neurological and psychiatric illnesses. Recent developments in computational analysis provide objective characterizations of these language abnormalities. We conducted a meta-analysis assessing semantic space models as a diagnostic or prognostic tool in psychiatric or neurological disorders. Diagnostic test accuracy analyses revealed reasonable sensitivity and specificity and high overall efficacy in differentiating between patients and controls (n=1680: Hedges' g =.73, p=.001). Analyses of full sentences (Hedges' g =.95 p <.0001) revealed a higher efficacy than single words (Hedges' g = .51, p <.0001). Specifically, models examining psychotic patients (Hedges' g =.96, p=.003) and those with autism (Hedges' g = .84, p <.0001) were highly effective. Our results show semantic space models are effective as a diagnostic tool in a variety of psychiatric and neurological disorders. The field is still exploratory in nature; techniques differ and models are only used to distinguish patients from healthy controls so far. Future research should aim to distinguish between disorders and perhaps explore newer semantic space tools like word2vec.

A study of genetic and environmental contributions to structural brain changes over time in twins concordant and discordant for bipolar disorder.

This is the first longitudinal twin study examining genetic and environmental contributions to the association between liability to bipolar disorder (BD) and changes over time in global brain volumes, and global and regional measures of cortical surface area, cortical thickness and cortical volume. A total of 50 twins from pairs discordant or concordant for BD (monozygotic: 8 discordant and 3 concordant pairs, and 1 patient and 3 co-twins from incomplete pairs; dizygotic: 6 discordant and 2 concordant pairs, and 1 patient and 7 co-twins from incomplete pairs) underwent magnetic resonance imaging twice. In addition, 57 twins from healthy twin pairs (15 monozygotic and 10 dizygotic pairs, and 4 monozygotic and 3 dizygotic subjects from incomplete pairs) were also scanned twice. Mean follow-up duration for all twins was 7.5 years (standard deviation: 1.5 years). Data were analyzed using structural equation modeling software OpenMx. The liability to BD was not associated with global or regional structural brain changes over time. Although we observed a subtle increase in cerebral white matter in BD patients, this effect disappeared after correction for multiple comparisons. Heritability of brain changes over time was generally low to moderate. Structural brain changes appear to follow similar trajectories in BD patients and healthy controls. Existing brain abnormalities in BD do not appear to progressively change over time, but this requires additional confirmation. Further study with large cohorts is recommended to assess genetic and environmental influences on structural brain abnormalities in BD, while taking into account the influence of lithium on the brain.

Genetic and environmental influences on cortical surface area and cortical thickness in bipolar disorder.

BACKGROUND: The risk of developing bipolar disorder (BD) has been linked to structural brain abnormalities. The degree to which genes and environment influence the association of BD with cortical surface area remains to be elucidated. In this twin study, genetic and environmental contributions to the association between liability to develop BD and surface area, thickness and volume of the cortex were examined. METHOD: The study cohort included 44 affected monozygotic (nine concordant, 12 discordant) and dizygotic (four concordant, 19 discordant) twin pairs, and seven twins from incomplete discordant monozygotic and dizygotic discordant twin pairs. In addition, 37 monozygotic and 24 dizygotic healthy control twin pairs, and six twins from incomplete monozygotic and dizygotic control pairs were included. RESULTS: Genetic liability to develop BD was associated with a larger cortical surface in limbic and parietal regions, and a thicker cortex in central and parietal regions. Environmental factors related to BD were associated with larger medial frontal, parietal and limbic, and smaller orbitofrontal surfaces. Furthermore, thinner frontal, limbic and occipital cortex, and larger frontal and parietal, and smaller orbitofrontal volumes were also associated with environmental factors related to BD. CONCLUSIONS: Our results suggest that unique environmental factors play a prominent role in driving the associations between liability to develop BD and cortical measures, particularly those involving cortical thickness. Further evaluation of their influence on the surface and thickness of the cortical mantle is recommended. In addition, cortical volume appeared to be primarily dependent on surface and not thickness.

Reciprocal causation models of cognitive vs volumetric cerebral intermediate phenotypes for schizophrenia in a pan-European twin cohort.

In aetiologically complex illnesses such as schizophrenia, there is no direct link between genotype and phenotype. Intermediate phenotypes could help clarify the underlying biology and assist in the hunt for genetic vulnerability variants. We have previously shown that cognition shares substantial genetic variance with schizophrenia; however, it is unknown if this reflects pleiotropic effects, direct causality or some shared third factor that links both, for example, brain volume (BV) changes. We quantified the degree of net genetic overlap and tested the direction of causation between schizophrenia liability, brain structure and cognition in a pan-European schizophrenia twin cohort consisting of 1243 members from 626 pairs. Cognitive deficits lie upstream of the liability for schizophrenia with about a quarter of the variance in liability to schizophrenia explained by variation in cognitive function. BV changes lay downstream of schizophrenia liability, with 4% of BV variation explained directly by variation in liability. However, our power to determine the nature of the relationship between BV deviation and schizophrenia liability was more limited. Thus, while there was strong evidence that cognitive impairment is causal to schizophrenia liability, we are not in a position to make a similar statement about the relationship between liability and BV. This is the first study to demonstrate that schizophrenia liability is expressed partially through cognitive deficits. One prediction of the finding that BV changes lie downstream of the disease liability is that the risk loci that influence schizophrenia liability will thereafter influence BV and to a lesser extent. By way of contrast, cognitive function lies upstream of schizophrenia, thus the relevant loci will actually have a larger effect size on cognitive function than on schizophrenia. These are testable predictions.

Symptom dimensions are associated with progressive brain volume changes in schizophrenia.

BACKGROUND: There is considerable variation in progressive brain volume changes in schizophrenia. Whether this is related to the clinical heterogeneity that characterizes the illness remains to be determined. This study examines the relationship between change in brain volume over time and individual variation in psychopathology, as measured by five continuous symptom dimensions (i.e. negative, positive, disorganization, mania and depression). METHODS: Global brain volume measurements from 105 schizophrenia patients and 100 healthy comparison subjects, obtained at inclusion and 5-year follow-up, were used in this study. Symptom dimension scores were calculated by factor analysis of clinical symptoms. Using linear regression analyses and independent-samples t-tests, the relationship between symptom dimensions and progressive brain volume changes, corrected for age, gender and intracranial volume, was examined. Antipsychotic medication, outcome and IQ were investigated as potential confounders. RESULTS: In patients, the disorganization dimension was associated with change in total brain (ß=-0.295, p=0.003) and cerebellar (ß=-0.349, p<0.001) volume. Furthermore, higher levels of disorganization were associated with lower IQ, irrespective of psychiatric status (i.e. patient or control). In healthy comparison subjects, disorganization score was not associated with progressive brain volume changes. CONCLUSION: Heterogeneity in progressive brain volume changes in schizophrenia is particularly associated with variation in disorganization. Schizophrenia patients with high levels of disorganization exhibit more progressive decrease of global brain volumes and have lower total IQ. We propose that these patients form a phenotypically and biologically homogenous subgroup that may be useful for etiological (e.g., genetic) studies.

Brain volume reductions in medication-naive patients with schizophrenia in relation to intelligence quotient.

BACKGROUND: Global brain abnormalities such as brain volume loss and grey- and white-matter deficits are consistently reported in first-episode schizophrenia patients and may already be detectable in the very early stages of the illness. Whether these changes are dependent on medication use or related to intelligence quotient (IQ) is still debated. METHOD: Magnetic resonance imaging scans were obtained for 20 medication-naive patients with first-episode schizophrenia and 26 matched healthy subjects. Volume measures of total brain grey and white matter, third and lateral ventricles and cortical thickness/surface were obtained. Differences between the groups were investigated, taking into account the effect of intelligence. RESULTS: Medication-naive patients showed statistically significant reductions in whole-brain volume and cerebral grey- and white-matter volume together with lateral ventricle enlargement compared to healthy subjects. IQ was significantly lower in patients compared to controls and was positively associated with brain and white-matter volume in the whole group. No significant differences in cortical thickness were found between the groups but medication-naive patients had a significantly smaller surface in the left superior temporal pole, Heschl's gyrus and insula compared to controls. CONCLUSIONS: Our findings suggest that brain volume loss is present at illness onset, and can be explained by the reduced surface of the temporal and insular cortex. These abnormalities are not related to medication, but IQ.

Genetic influences on thinning of the cerebral cortex during development.

During development from childhood to adulthood the human brain undergoes considerable thinning of the cerebral cortex. Whether developmental cortical thinning is influenced by genes and if independent genetic factors influence different parts of the cortex is not known. Magnetic resonance brain imaging was done in twins at age 9 (N = 190) and again at age 12 (N = 125; 113 repeated measures) to assess genetic influences on changes in cortical thinning. We find considerable thinning of the cortex between over this three year interval (on average 0.05 mm; 1.5%), particularly in the frontal poles, and orbitofrontal, paracentral, and occipital cortices. Cortical thinning was highly heritable at age 9 and age 12, and the degree of genetic influence differed for the various areas of the brain. One genetic factor affected left inferior frontal (Broca's area), and left parietal (Wernicke's area) thinning; a second factor influenced left anterior paracentral (sensory-motor) thinning. Two factors influenced cortical thinning in the frontal poles: one of decreasing influence over time, and another independent genetic factor emerging at age 12 in left and right frontal poles. Thus, thinning of the cerebral cortex is heritable in children between the ages 9 and 12. Furthermore, different genetic factors are responsible for variation in cortical thickness at ages 9 and 12, with independent genetic factors acting on cortical thickness across time and between various brain areas during childhood brain development.

Cigarette smoking and progressive brain volume loss in schizophrenia.

It is unknown whether the reported brain loss in schizophrenia can be attributed to the effects of tobacco smoking. 96 Patients (54 smokers/42 non-smokers) and 113 control subjects (35/78) were included in a 5-year longitudinal MRI study. Despite the higher prevalence of smoking behavior and the higher number of cigarettes consumed per day in the patients, cigarette smoking did not explain the excessive cerebral (gray matter) volume decreases in the patients. Moreover, smoking was not associated with brain volume change over time in the healthy subjects. However, extremely heavy smoking may contribute to excessive gray matter volume loss in schizophrenia.

Psychosis and brain volume changes during the first five years of schizophrenia.

The underlying mechanisms explaining brain volume changes in schizophrenia are not yet understood, but psychosis might be related to these changes. Forty-eight patients with first-episode schizophrenia underwent Magnetic Resonance Imaging brain scanning at inclusion and after five years. An association was found between longer duration of psychosis, larger gray matter volume decrease and larger ventricular volume increase. These findings strongly suggest that psychosis contributes to brain volume reductions found in schizophrenia.

Brain volume changes in the first year of illness and 5-year outcome of schizophrenia.

Progressive brain volume changes have been reported in first-episode schizophrenia, but their relationship to the disease process or to other factors remains unclear. We examined such changes in the first year of illness, and related them to 5-year outcome. Progressive brain volume changes, in particular of grey matter, during the first year of illness were found to be significantly associated with clinical and functional outcome 5 years after the first episode. These findings suggest that early dynamic brain volume changes are related to the disease process and predict the longer-term outcome of schizophrenia.

Differential effects of DRD4 and DAT1 genotype on fronto-striatal gray matter volumes in a sample of subjects with attention deficit hyperactivity disorder, their unaffected siblings, and controls.

Genetic influences on behavior are complex and, as such, the effect of any single gene is likely to be modest. Neuroimaging measures may serve as a biological intermediate phenotype to investigate the effect of genes on human behavior. In particular, it is possible to constrain investigations by prior knowledge of gene characteristics and by including samples of subjects where the distribution of phenotypic variance is both wide and under heritable influences. Here, we use this approach to show a dissociation between the effects of two dopamine genes that are differentially expressed in the brain. We show that the DAT1 gene, a gene expressed predominantly in the basal ganglia, preferentially influences caudate volume, whereas the DRD4 gene, a gene expressed predominantly in the prefrontal cortex, preferentially influences prefrontal gray matter volume in a sample of subjects including subjects with ADHD, their unaffected siblings, and healthy controls. This demonstrates that, by constraining our investigations by prior knowledge of gene expression, including samples in which the distribution of phenotypic variance is wide and under heritable influences, and by using intermediate phenotypes, such as neuroimaging, we may begin to map out the pathways by which genes influence behavior.

Brain morphology in antipsychotic-naïve schizophrenia: a study of multiple brain structures.

BACKGROUND: Although brain volume changes are found in schizophrenia, only a limited number of structural magnetic resonance imaging studies have exclusively examined antipsychotic-naïve patients. AIMS: To comprehensively investigate multiple brain structures in a single sample of patients who were antipsychotic-naïve. METHOD: Twenty antipsychotic-naïve patients with first-episode schizophrenia and 20 healthy comparison subjects were included. Intracranial, total brain, frontal lobe, grey and white matter, cerebellar, hippocampal, parahippocampal, thalamic, caudate nucleus and lateral and third ventricular volumes were measured. Repeated-measures analyses of (co)variance were conducted with intracranial volume as covariate. RESULTS: Third ventricle volume enlargement was found in patients compared with the healthy subjects. No differences were found in other brain regions. CONCLUSIONS: These findings suggest that some brain abnormalities are present in the early stages of schizophrenia. Moreover, it suggests that brain abnormalities reported in patients with chronic schizophrenia develop in a later stage of the disease and/or are medication induced.

Focal gray matter density changes in schizophrenia.

BACKGROUND: The view that schizophrenia is a brain disease particularly involving decrements in gray matter is supported by findings from many imaging studies. However, it is unknown whether the (progressive) loss of tissue affects the brain globally or whether tissue loss is more prominent in some areas than in others. METHODS: Magnetic resonance whole brain images were acquired from 159 patients with schizophrenia or a schizophreniform disorder and 158 healthy subjects across a 55-year age span. Gray matter density maps were made and analyzed using voxel-based morphometry. RESULTS: Compared with healthy subjects, decreases in gray matter density were found in the left amygdala; left hippocampus; right supramarginal gyrus; thalamus; (orbito) frontal, (superior) temporal, occipitotemporal, precuneate, posterior cingulate, and insular cortices bilaterally in patients with schizophrenia or schizophreniform disorder. Compared with healthy subjects, increases in gray matter density were exclusively found in the right caudate and globus pallidus in patients with schizophrenia or schizophreniform disorder. A group-by-age interaction for density was found in the left amygdala, owing to a negative regression slope of gray matter density on age in the left amygdala in patients compared with healthy subjects. CONCLUSIONS: Gray matter density is decreased in distinct focal areas in the brains of patients with schizophrenia or schizophreniform disorder. The decreased density in the left amygdala is more pronounced in older patients with schizophrenia.

Quantitative genetic modeling of variation in human brain morphology.

The degree to which individual variation in brain structure in humans is genetically or environmentally determined is as yet not well understood. We studied the brains of 54 monozygotic (33 male, 21 female) and 58 dizygotic (17 male, 20 female, 21 opposite sex) pairs of twins and 34 of their full siblings (19 male, 15 female) by means of high resolution magnetic resonance imaging scans. Structural equation modeling was used to quantify the genetic and environmental contributions to phenotypic (co)variance in whole brain, gray and white matter volume of the cerebrum, lateral ventricle volume and associated variables such as intracranial volume and height. Because the cerebral cortex makes up more that two-thirds of the brain mass and almost three-quarters of its synapses, our data predominantly concerns the telencephalon. Genetic factors accounted for most of the individual differences in whole brain (90%), gray (82%) and white (88%) matter volume. Individual differences in lateral ventricle volume were best explained by a model containing common (58%) and unique (42%) environmental factors, indicating genes to be of no or minor influence. In our sample, genetic or environmental influences were not different for males and females. The same genes influenced brain volumes and intracranial volume and almost completely explained their high phenotypic correlation. Genes influencing gray and white matter overlapped to a large extent and completely determined their phenotypic correlation. The high heritability estimates that were found indicate that brain volumes may be useful as intermediate phenotypes in behavioral genetic research.