Accelerated brain aging in schizophrenia and beyond: a neuroanatomical marker of psychiatric disorders.
ABSTRACT: Structural brain abnormalities are central to schizophrenia (SZ), but it remains unknown whether they are linked to dysmaturational processes crossing diagnostic boundaries, aggravating across disease stages, and driving the neurodiagnostic signature of the illness. Therefore, we investigated whether patients with SZ (N = 141), major depression (MD; N = 104), borderline personality disorder (BPD; N = 57), and individuals in at-risk mental states for psychosis (ARMS; N = 89) deviated from the trajectory of normal brain maturation. This deviation was measured as difference between chronological and the neuroanatomical age (brain age gap estimation [BrainAGE]). Neuroanatomical age was determined by a machine learning system trained to individually estimate age from the structural magnetic resonance imagings of 800 healthy controls. Group-level analyses showed that BrainAGE was highest in SZ (+5.5 y) group, followed by MD (+4.0), BPD (+3.1), and the ARMS (+1.7) groups. Earlier disease onset in MD and BPD groups correlated with more pronounced BrainAGE, reaching effect sizes of the SZ group. Second, BrainAGE increased across at-risk, recent onset, and recurrent states of SZ. Finally, BrainAGE predicted both patient status as well as negative and disorganized symptoms. These findings suggest that an individually quantifiable "accelerated aging" effect may particularly impact on the neuroanatomical signature of SZ but may extend also to other mental disorders.
Project description:BACKGROUND:The age of a person's brain can be estimated from structural brain images using an aggregate measure of variation in morphology across the whole brain. The brain age gap estimation (BrainAGE) score is computed as the difference between kernel-estimated brain age and chronological age. In this exploratory study, we investigated the application of the BrainAGE measure to identify potential novel effects of pharmacological agents on brain morphology. METHODS:Twenty healthy participants (23-47 years of age) completed three structural magnetic resonance imaging scans 45 minutes after administration of placebo or 200 or 600 mg of ibuprofen in a double-blind, crossover study. An externally derived BrainAGE model from a sample of 480 healthy participants was used to examine the acute effect of ibuprofen on temporary neuroanatomical changes in healthy individuals. RESULTS:The BrainAGE model produced age prediction for each participant with a mean absolute error of 6.7 years between the estimated and chronological age. The intraclass correlation coefficient for BrainAGE was 0.96. Relative to placebo, 200 and 600 mg of ibuprofen significantly decreased BrainAGE by 1.18 and 1.15 years, respectively (p < .05). The trained BrainAGE model identified the medial prefrontal cortex to be the strongest age predictor. CONCLUSIONS:BrainAGE is a potentially useful construct to examine neurological effects of therapeutic drugs. Ibuprofen temporarily reduces BrainAGE by approximately 1 year, which is likely due to its acute anti-inflammatory effects.
Project description:With the aging population, prevalence of neurodegenerative diseases is increasing, thus placing a growing burden on individuals and the whole society. However, individual rates of aging are shaped by a great variety of and the interactions between environmental, genetic, and epigenetic factors. Establishing biomarkers of the neuroanatomical aging processes exemplifies a new trend in neuroscience in order to provide risk-assessments and predictions for age-associated neurodegenerative and neuropsychiatric diseases at a single-subject level. The "Brain Age Gap Estimation (BrainAGE)" method constitutes the first and actually most widely applied concept for predicting and evaluating individual brain age based on structural MRI. This review summarizes all studies published within the last 10 years that have established and utilized the BrainAGE method to evaluate the effects of interaction of genes, environment, life burden, diseases, or life time on individual neuroanatomical aging. In future, BrainAGE and other brain age prediction approaches based on structural or functional markers may improve the assessment of individual risks for neurological, neuropsychiatric and neurodegenerative diseases as well as aid in developing personalized neuroprotective treatments and interventions.
Project description:Previous studies have shown that structural brain changes are among the best-studied candidate markers for schizophrenia (SZ) along with functional connectivity (FC) alterations of resting-state (RS) patterns. This study aimed to investigate effects of clinical and sociodemographic variables on the classification by applying multivariate pattern analysis (MVPA) to both gray matter (GM) volume and FC measures in patients with SZ and healthy controls (HC). RS and structural magnetic resonance imaging data (sMRI) from 74 HC and 71 SZ patients were obtained from a Mind Research Network COBRE dataset available via COINS (http://coins.mrn.org/dx). We used a MVPA framework using support-vector machines embedded in a repeated, nested cross-validation to generate a multi-modal diagnostic system and evaluate its generalizability. The dependence of neurodiagnostic performance on clinical and sociodemographic variables was evaluated. The RS classifier showed a slightly higher accuracy (70.5%) compared to the structural classifier (69.7%). The combination of sMRI and RS outperformed single MRI modalities classification by reaching 75% accuracy. The RS based moderator analysis revealed that the neurodiagnostic performance was driven by older SZ patients with an earlier illness onset and more pronounced negative symptoms. In contrast, there was no linear relationship between the clinical variables and neuroanatomically derived group membership measures. This study achieved higher accuracy distinguishing HC from SZ patients by fusing 2 imaging modalities. In addition the results of RS based moderator analysis showed that age of patients, as well as their age at the illness onset were the most important clinical features.
Project description:Schizophrenia and bipolar disorder together affect approximately 2.5% of the world population, and their etiologies are thought to involve multiple genetic variants and environmental influences. The analysis of gene expression patterns in brain may provide a characteristic signature for each disorder.RNA samples from the dorsolateral prefrontal cortex (Brodmann area 46) consisting of individuals with schizophrenia (SZ), bipolar disorder (BPD), and control subjects were tested on the Codelink Human 20K Bioarray platform. Selected transcripts were validated by quantitative real-time polymerase chain reaction (PCR). The strong effects of age, gender, and pH in the analysis of differential gene expression were controlled by analysis of covariance (ANCOVA). Criteria for differential gene expression were 1) a gene was significantly dysregulated in both BPD and SZ compared with control subjects and 2) significant in ANCOVA analysis with samples that have a pH above the median of the sample.A list of 78 candidate genes passed these two criteria in BPD and SZ and was overrepresented for functional categories of nervous system development, immune system development and response, and cell death. Five dysregulated genes were confirmed with quantitative Q-PCR in both BPD and SZ. Three genes were highly enriched in brain expression (AGXT2L1, SLC1A2, and TU3A). The distribution of AGXT2L1 expression in control subjects versus BPD and SZ was highly significant (Fisher's Exact Test, p < 10(-06)).These results suggest a partially shared molecular profile for both disorders and offer a window into discovery of common pathophysiology that might lead to core treatments.
Project description:We previously reported a schizophrenia associated reduction of neuronal and oligodendrocyte number in the anterior principal thalamic nucleus (APN) in a cohort of severely impaired elderly subjects with schizophrenia (SZ) relative to age matched nonpsychiatric controls (NCs). The present study was undertaken to determine 1) if those findings could be replicated in an independent sample of less chronically impaired subjects with SZ and NCs stratified across a broader age range; 2) if the findings are specific to SZ or are also seen in unipolar major depressive (MDD) or bipolar disorder (BPD); and 3) if the findings are specific to the APN or also seen in another thalamic nucleus. Computer assisted stereological methods were employed to determine the number of neurons and oligodendrocytes in the APN and centromedian nucleus (CMN) of the Nissl-stained thalamic sections maintained by the Stanley Foundation Brain Bank. This collection includes specimens from NCs and age matched subjects with diagnoses of SZ, MDD, or BPD who died between the ages of 25 and 68. Data were analyzed by mixed-effects linear regressions adjusting for demographic variables and known history of exposure to psychotropic medications. Oligodendrocyte number was decreased in both nuclei relative to NCs in subjects with SZ and in that subset of subjects with BPD who had experienced psychotic episodes. Compared to NCs both of these patient groups also exhibited an attenuation of an age-related increase in the number of oligodendrocytes. Contrary to our previous report, we did not detect a SZ-associated deficit in neuronal number in the APN. A history of exposure to neuroleptics, however, was associated with a decrease in neuronal number in both nuclei, but this decrease did not vary in relation to cumulative lifetime neuroleptic exposure in fluphenazine equivalents. Among subjects with psychiatric diagnoses, exposure to lithium was associated with an increase in the number of oligodendrocytes. No effects were detected for exposure to anticonvulsants or for abuse of alcohol or other substances.
Project description:Although Attention-Deficit/Hyperactivity Disorder (ADHD) and Bipolar Disorder (BPD) frequently co-occur and represent a particularly morbid clinical form of both disorders, neuroimaging research addressing this comorbidity is scarce. Our aim was to evaluate cortical thickness in ADHD and BPD, testing the hypothesis that comorbid subjects (ADHD+BPD) would have neuroanatomical correlates of both disorders. Magnetic Resonance Imaging (MRI) findings were compared between 31 adults with ADHD+BPD, 18 with BPD, 26 with ADHD, and 23 healthy controls. Cortical thickness analysis of regions of interest was estimated as a function of ADHD and BPD status, using linear regression models. BPD was associated with significantly thicker cortices in 13 regions, independently of ADHD status and ADHD was associated with significantly thinner neocortical gray matter in 28 regions, independent of BPD. In the comorbid state of ADHD plus BPD, the profile of cortical abnormalities consisted of structures that are altered in both disorders individually. Results support the hypothesis that ADHD and BPD independently contribute to cortical thickness alterations of selective and distinct brain structures, and that the comorbid state represents a combinatory effect of the two. Attention to comorbidity is necessary to help clarify the heterogeneous neuroanatomy of both BPD and ADHD.
Project description:Comparisons of cognitive impairments between schizophrenia (SZ) and bipolar disorder (BPD) have produced mixed results. We applied different working memory (WM) measures (Digit Span Forward and Backward, Short-delay and Long-delay CPT-AX, N-back) to patients with SZ (n = 23), psychotic BPD (n = 19) and non-psychotic BPD (n = 24), as well as to healthy controls (HC) (n = 18) in order to compare the level of WM impairments across the groups. With respect to the less demanding WM measures (Digit Span Forward and Backward, Short-delay CPT-AX), there were no between group differences in cognitive performance; however, with respect to the more demanding WM measures (Long-delay CPT-AX, N-back), we observed that the groups with psychosis (SZ, psychotic BPD) did not differ from one another, but performed poorer than the group without a history of psychosis (non-psychotic BPD). A history of psychotic symptoms may influence cognitive performance with respect to WM delay and load effects as measured by Long-delay CPT-AX and N-back tests, respectively. We observed a positive correlation of WM performance with antipsychotic treatment and a negative correlation with depressive symptoms in BPD and with negative symptoms in SZ subgroup. Our study suggests that WM dysfunctions are more closely related to a history of psychosis than to the diagnostic categories of SZ and BPD described by psychiatric classification systems.
Project description:Several imaging modalities, including T1-weighted structural imaging, diffusion tensor imaging, and functional MRI can show chronological age related changes. Employing machine learning algorithms, an individual's imaging data can predict their age with reasonable accuracy. While details vary according to modality, the general strategy is to: (1) extract image-related features, (2) build a model on a training set that uses those features to predict an individual's age, (3) validate the model on a test dataset, producing a predicted age for each individual, (4) define the "Brain Age Gap Estimate" (BrainAGE) as the difference between an individual's predicted age and his/her chronological age, (5) estimate the relationship between BrainAGE and other variables of interest, and (6) make inferences about those variables and accelerated or delayed brain aging. For example, a group of individuals with overall positive BrainAGE may show signs of accelerated aging in other variables as well. There is inevitably an overestimation of the age of younger individuals and an underestimation of the age of older individuals due to "regression to the mean." The correlation between chronological age and BrainAGE may significantly impact the relationship between BrainAGE and other variables of interest when they are also related to age. In this study, we examine the detectability of variable effects under different assumptions. We use empirical results from two separate datasets [training = 475 healthy volunteers, aged 18-60 years (259 female); testing = 489 participants including people with mood/anxiety, substance use, eating disorders and healthy controls, aged 18-56 years (312 female)] to inform simulation parameter selection. Outcomes in simulated and empirical data strongly support the proposal that models incorporating BrainAGE should include chronological age as a covariate. We propose either including age as a covariate in step 5 of the above framework, or employing a multistep procedure where age is regressed on BrainAGE prior to step 5, producing BrainAGE Residualized (BrainAGER) scores.
Project description:Objectives: This is the first study to explore cognitive, emotional, and behavioral responses to voices in youth with borderline personality disorder (BPD) compared with those with schizophrenia spectrum disorder (SZ), and to examine if negative appraisals of voices predict depression and anxiety across the groups. Methods: The sample comprised 43 outpatients, aged 15-25 years, who reported auditory verbal hallucinations (AVH) and were diagnosed with either Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) BPD or SZ. Data were collected using the Psychotic Symptom Rating Scales, the revised Beliefs About Voices Questionnaire, the Voice Rank Scale, and the Depression Anxiety Stress Scale. Results: Youth with BPD did not differ from youth with SZ in beliefs about the benevolence or malevolence of voices. Youth with BPD appraised their voices as more omnipotent and of higher social rank in relation to themselves, compared with youth with SZ. In both diagnostic groups, beliefs about malevolence and omnipotence of voices were correlated with more resistance toward voices, and beliefs about benevolence with more engagement with voices. In addition, perceiving the voices as being of higher social rank than oneself and negative voice content were both independent predictors of depression, irrespective of diagnostic group. In contrast, negative appraisals of voices did not predict anxiety after adjusting for negative voice content. Conclusions: This study replicated the link between negative appraisals of voices and depression that has been found in adults with SZ in a mixed diagnostic youth sample. It, thus, provides preliminary evidence that the cognitive model of AVH can be applied to understanding and treating voices in youth with BPD.
Project description:The vesicular monoamine transporter 1 gene (VMAT1/SLC18A1) maps to the shared bipolar disorder (BPD)/schizophrenia (SZ) susceptibility locus on chromosome 8p21. Vesicular monoamine transporters are involved in transport of monoamine neurotransmitters which have been postulated to play a relevant role in the etiology of BPD and/or SZ. Variations in the VMAT1 gene might affect transporter function and/or expression and might be involved in the etiology of BPD and/or SZ. Genotypes of 585 patients with BPD type I and 563 control subjects were obtained for three missense single nucleotide polymorphisms (SNPs) (Thr4Pro, Thr98Ser, Thr136Ile) and four non-coding SNPs (rs988713, rs2279709, rs3735835, rs1497020). All cases and controls were of European descent. Allele frequencies differed significantly for the potential functional polymorphism Thr136Ser between BPD patients and controls (p=0.003; df=1; OR=1.34; 95% CI: 1.11-1.62). Polymorphisms in the promoter region (rs988713: p=0.005, df=1; OR=1.31; 95% CI: 1.09-1.59) and intron 8 (rs2279709: p=0.039, df=1; OR=0.84; 95% CI: 0.71-0.99) were also associated with disease. Expression analysis confirmed that VMAT1 is expressed in human brain at the mRNA and protein level. Results suggest that variations in the VMAT1 gene may confer susceptibility to BPD in patients of European descent. Additional studies are necessary to confirm this effect and to elucidate the role of VMAT1 in central nervous system physiology.