Complexity Of Schizophrenia Research Articles

Neuroimaging markers of aberrant brain activity and treatment response in schizophrenia patients based on brain complexity

The complexity of brain activity reflects its ability to process information, adapt to environmental changes, and transition between states. However, it remains unclear how schizophrenia (SZ) affects brain activity complexity, particularly its dynamic changes. This study aimed to investigate the abnormal patterns of brain activity complexity in SZ, their relationship with cognitive deficits, and the impact of antipsychotic medication. Forty-four drug-naive first-episode (DNFE) SZ patients and thirty demographically matched healthy controls (HC) were included. Functional MRI-based sliding window analysis was utilized for the first time to calculate weighted permutation entropy to characterize complex patterns of brain activity in SZ patients before and after 12 weeks of risperidone treatment. Results revealed reduced complexity in the caudate, putamen, and pallidum at baseline in SZ patients compared to HC, with reduced complexity in the left caudate positively correlated with Continuous Performance Test (CPT) and Category Fluency Test scores. After treatment, the complexity of the left caudate increased. Regions with abnormal complexity showed decreased functional connectivity, with complexity positively correlated with connectivity strength. We observed that the dynamic complexity of the brain exhibited the characteristic of spontaneous, recurring “complexity drop”, potentially reflecting transient state transitions in the resting brain. Compared to HC, patients exhibited reduced scope, intensity, and duration of complexity drop, all of which improved after treatment. Reduced duration was negatively correlated with CPT scores and positively with clinical symptoms. The results suggest that abnormalities in brain activity complexity and its dynamic changes may underlie cognitive deficits and clinical symptoms in SZ patients. Antipsychotic treatment partially restores these abnormalities, highlighting their potential as indicators of treatment efficacy and biomarkers for personalized therapy.

Unraveling the Boundaries, Overlaps, and Connections between Schizophrenia and Obsessive-Compulsive Disorder (OCD).

Schizophrenia (SCZ) and obsessive-compulsive disorder (OCD) typically have distinct diagnostic criteria and treatment approaches. SCZ is characterized by delusions, hallucinations, disorganized speech, and cognitive impairments, while OCD involves persistent, intrusive thoughts (obsessions) and repetitive behaviors (compulsions). The co-occurrence of these disorders increases clinical complexity and poses significant challenges for diagnosis and treatment. Epidemiological studies indicate a significant overlap, with prevalence rates of comorbid OCD in SCZ patients ranging from 12% to 25%, which is higher than in the general population. Etiological hypotheses suggest shared genetic, neurobiological, and environmental factors, with genetic studies identifying common loci and pathways, such as glutamatergic and dopaminergic systems. Neuroimaging studies reveal both overlapping and distinct neural abnormalities, indicating shared and unique neurobiological substrates. Environmental factors, like early life stressors and urbanicity, also contribute to the comorbidity. The overlapping clinical features of both disorders complicate diagnosis. Treatment approaches include combining SSRIs with antipsychotics and cognitive behavioral therapy (CBT). The complexity of SCZ and OCD comorbidity underscores the need for a dimensional, spectrum-based perspective on psychiatric disorders, alongside traditional categorical approaches, to improve diagnosis and treatment outcomes.

A Dynamic Entropy Approach Reveals Reduced Functional Network Connectivity Trajectory Complexity in Schizophrenia.

Over the past decade and a half, dynamic functional imaging has revealed low-dimensional brain connectivity measures, identified potential common human spatial connectivity states, tracked the transition patterns of these states, and demonstrated meaningful transition alterations in disorders and over the course of development. Recently, researchers have begun to analyze these data from the perspective of dynamic systems and information theory in the hopes of understanding how these dynamics support less easily quantified processes, such as information processing, cortical hierarchy, and consciousness. Little attention has been paid to the effects of psychiatric disease on these measures, however. We begin to rectify this by examining the complexity of subject trajectories in state space through the lens of information theory. Specifically, we identify a basis for the dynamic functional connectivity state space and track subject trajectories through this space over the course of the scan. The dynamic complexity of these trajectories is assessed along each dimension of the proposed basis space. Using these estimates, we demonstrate that schizophrenia patients display substantially simpler trajectories than demographically matched healthy controls and that this drop in complexity concentrates along specific dimensions. We also demonstrate that entropy generation in at least one of these dimensions is linked to cognitive performance. Overall, the results suggest great value in applying dynamic systems theory to problems of neuroimaging and reveal a substantial drop in the complexity of schizophrenia patients' brain function.

Clinical and Sociodemographic Correlations with Neurological Soft Signs in Hospitalized Patients with Schizophrenia: A Preliminary Longitudinal Study.

Schizophrenia is a severe, chronic neuropsychiatric disorder characterized by symptoms that profoundly impact behavior, cognition, perception, and emotions, leading to a reduced quality of life and physical impairment. Given the complexity of schizophrenia, there is a pressing need for clinical markers and tools to predict its course, enhance disease staging, facilitate early intervention, improve differential diagnosis, and tailor individualized treatment approaches. Previous studies focused on the relationship between neurological soft signs (NSS) and factors such as age, illness duration, and symptomatology, indicating NSS as state markers improving in parallel with psychotic symptom remission or predicting treatment resistance. However, there is a lack of consensus on NSS assessment tools, hindering routine clinical monitoring despite diagnostic and prognostic potential. The present longitudinal study involved 81 psychiatric inpatients diagnosed with schizophrenia. Patients were assessed at three time points: baseline, 1 month, and 6 months. The examination included the use of scales to evaluate psychotic and neurological symptoms, as well as the identification of adverse extrapyramidal reactions caused by neuroleptic treatment. The progression of NSS was correlated to both the symptomatology and the sociodemographic data of the patients. The main findings from the present investigation revealed a statistical correlation between NSS and psychopathological symptoms, especially with negative symptoms of schizophrenia. However, it is important to note that neuroleptic side effects only had a limited impact on NSS. Therefore, instead of being linked to extrapyramidal symptoms caused by neuroleptics, NSS appears to be more frequently related with symptoms of schizophrenia. Our findings provide further support for their strong association with the course of schizophrenia, independent of treatment side effects, thus emphasizing their potential as reliable assessment tools in both research and clinical settings.

Evaluation of the association COMT Val158Met variant and childhood trauma on aggression in Turkish SCZ patients

Childhood trauma is a serious form of stress that makes individuals more vulnerable to developing Schizophrenia (SCZ). Many studies have predicted the association between the catechol-O-methyltransferase (COMT) gene Val158Met variant and aggressive attack. We aimed to investigate the association the COMT variant and childhood trauma on aggression in Turkish SCZ patients This study included 89 patients diagnosed with SCZ. Childhood Trauma Questionnaire (CTS) and Overt Aggression Scale (OAS) were used to assess childhood trauma and aggression. COMT Val158Met variant was analyzed by PCR-RFLP method from isolated DNAs. There was no statistically significant difference in comparing the COMT genotype distribution and clinical characteristics including suicide attempts, self-destructive behavior, crime history, substance, alcohol and tobacco use. When we evaluate Spearman’s rank correlation coefficients between CTQ and OAS, the correlation between the OAS and CTQ scores of the patients was statistically significant except for the sexual abuse subgroup of the CTQ. In the univariate logistic regression analysis, in which the dichotomized OAS score was accepted as the dependent variable, it was found that age, suicide attempt, substance abuse, and CTQ total score significantly predicted the higher OAS scores. In the multivariate logistic regression analysis, which included the variables that predicted OAS significantly, age, suicide attempt, and total CTQ score were determined as independent variables predicting OAS. Because of the phenotypic complexity in SCZ, it is difficult to draw strong conclusions about COMT and to highlight a definitive relationship. Larger-scale studies are needed to examine the multifactorial inheritance pattern of schizophrenia in different dimensions.

Displaced Metaphor as Madness? A Critical-Clinical Study of Schizophrenia in Joe Penhall's Blue/Orange

ABSTRACT No other trope captures the perceived complexity of schizophrenia and the epistemological, clinical, ethical and phenomenological aporias it poses than the phrase “the sublime object of psychiatry” (Angela Woods). “Sublimity”, however, instigates further complications and problems by mystifying the nature and experience of schizophrenia rather than contributing to the development of a clearer account of it. A conspicuous case of the critique of this treatment of schizophrenia in terms of the “sublime” can be found in Joe Penhall’s two dramatic works: Some Voices (1993) and Blue/Orange (2000). This essay will argue that Penhall's treatment of schizophrenia is distinguished by two salient features: its method (the critical-clinical) and its simultaneously symptomatic and symptomatological treatment of schizophrenia. What further distinguishes Penhall’s critical depiction of schizophrenia is his insistence that the cultural is as crucial as the clinical in the experience, representation, and perception of schizophrenia. This is attested by his acute inclusion of such crucial issues as race, class, discourse, and gender as highly determining elements in the experience, perceptions and diagnosis of schizophrenia. This essay, accordingly, will demonstrate how Blue/Orange presents a critical-clinical account of the limits of various discursively-determined treatments of schizophrenia (including both psychiatry and anti-psychiatry) thereby accentuating the necessity of developing a more holistic method in the cultural and clinical treatments of schizophrenia.

Going deep into schizophrenia with artificial intelligence

Despite years of research, the mechanisms governing the onset, relapse, symptomatology, and treatment of schizophrenia (SZ) remain elusive. The lack of appropriate analytic tools to deal with the heterogeneity and complexity of SZ may be one of the reasons behind this situation. Deep learning, a subfield of artificial intelligence (AI) inspired by the nervous system, has recently provided an accessible way of modeling and analyzing complex, high-dimensional, nonlinear systems. The unprecedented accuracy of deep learning algorithms in classification and prediction tasks has revolutionized a wide range of scientific fields and is rapidly permeating SZ research. Deep learning has the potential of becoming a valuable aid for clinicians in the prediction, diagnosis, and treatment of SZ, especially in combination with principles from Bayesian statistics. Furthermore, deep learning could become a powerful tool for uncovering the mechanisms underlying SZ thanks to a growing number of techniques designed for improving model interpretability and causal reasoning. The purpose of this article is to introduce SZ researchers to the field of deep learning and review its latest applications in SZ research. In general, existing studies have yielded impressive results in classification and outcome prediction tasks. However, methodological concerns related to the assessment of model performance in several studies, the widespread use of small training datasets, and the little clinical value of some models suggest that some of these results should be taken with caution.

A multimodal attempt to follow-up linkage regions using RNA expression, SNPs and CpG methylation in schizophrenia and bipolar disorder kindreds.

The complexity of schizophrenia (SZ) and bipolar disorder (BD) has slowed down progress in understanding their genetic roots. Alternative genomic approaches are needed to bypass these difficulties. We attempted a multimodal approach to follow-up on reported linkage findings in SZ and BD from the Eastern Quebec kindreds in chromosomes 3q21, 4p34, 6p22, 8p21, 8p11, 13q11-q14, 15q13, 16p12, and 18q21. First, in 498 subjects, we measured RNA expression (47 K Illumina chips) in SZ and BD patients that we compared with their non-affected relatives (NARs) to identify, for each chromosomal region, genes showing the most significant differences in expression. Second, we performed SNP genotyping (700 K Illumina chips) and cis-eQTN analysis. Third, we measured DNA methylation on genes with RNA expression differences or eQTNs. We found a significant overexpression of the gene ITGB5 at 3q25 in SZ and BD after multiple testing p value adjustment. SPCS3 gene at 4q34, and FZD3 gene at 8p21, contained significant eQTNs after multiple testing corrections, while ITGB5 provided suggestive results. Methylation in associated genes did not explain the expression differences between patients and NARs. Our multimodal approach involving RNA expression, dense SNP genotyping and eQTN analyses, restricted to chromosomal regions having shown linkage, lowered the multiple testing burden and allowed for a deeper examination of candidate genes in SZ or BD.

EEG Multiscale Complexity in Schizophrenia During Picture Naming.

Introduction: Patients with schizophrenia show cognitive deficits that are evident both behaviourally and with EEG recordings. Recent studies have suggested that non-linear analyses of EEG might more adequately reflect the complex, irregular, non-stationary behavior of neural processes than more traditional ERP measures. Non-linear analyses have been mainly applied to EEGs from patients at rest, whereas differences in complexity might be more evident during task performance.Objective: We aimed to investigate changes in non-linear brain dynamics of patients with schizophrenia during cognitive processing.Method: 18 patients and 17 matched healthy controls were asked to name pictures. EEG data were collected at rest and while they were performing a naming task. EEGs were analyzed with the classical Lempel-Ziv Complexity (LZC) and with the Multiscale LZC. Electrodes were grouped in seven regions of interest (ROI).Results: As expected, controls had fewer naming errors than patients. Regarding EEG complexity, the interaction between Group, Task and ROI indicated that patients showed higher complexity values in right frontal regions only at rest, where no differences in complexity between patients and controls were found during the naming task. EEG complexity increased from rest to task in controls in left temporal-parietal regions, while no changes from rest to task were observed in patients. Finally, differences in complexity between patients and controls depended on the frequency bands: higher values of complexity in patients at rest were only observed in fast bands, indicating greater heterogeneity in patients in local dynamics of neuronal assemblies.Conclusion: Consistent with previous studies, schizophrenic patients showed higher complexity than controls in frontal regions at rest. Interestingly, we found different modulations of brain complexity during a simple cognitive task between patients and controls. These data can be interpreted as indicating schizophrenia-related failures to adapt brain functioning to the task, which is reflected in poorer behavioral performance.Highlights: - We measured classical and multiscale Lempel-Ziv Complexity (LZCN and MLZC) of the EEG signal of patients with schizophrenia and controls at rest and while performing a cognitive task. - We found that patients and controls showed a different pattern of brain complexity depending on their cognitive state (at rest or under cognitive challenge). - Our results illustrate the value of the MLZC in the characterization of the pattern of brain complexity in schizophrenia on function of frequency bands. - Nonlinear methodologies of EEG analysis can help to characterize brain dysfunction in schizophrenia.

P383 Multiscale lempel-ZIV complexity in schizophrenia at rest and while performing a naming task

Schizophrenic patients show cognitive deficits which are evident in EEG measures. Recent studies have suggested that non-linear EEG indexes might more adequately reflect the complex, irregular, nonstationary behavior of neural processes in schizophrenia than more traditional ERP measures (Fernandez et al., 2011). So far, non-linear analyses have mainly been performed to EEGs from patients at rest, whereas differences in complexity might be more evident during task performance. In addition, most studies have explored complexity at a single time scale. Objective We aimed to investigate changes in nonlinear multiscale brain dynamics in schizophrenic patients during cognitive processing. Method 18 patients and 17 matched healthy controls were asked to name pictures. EEG data were collected at rest and while they were performing the task. EEGs were analyzed with the classical and the multiscale Lempel-Ziv Complexity. Electrodes were grouped in seven regions of interest (ROI). Results Controls had fewer naming errors than patients. More importantly, we found greater complexity values for patients than for controls in right frontal regions, at rest. The interaction between Group, Task and ROI indicated that differences between patients and controls were more evident when performing the task, with increments in brain complexity in left temporal parietal regions, only in the control group. Conclusions We found different modulations of brain complexity during a simple cognitive task between patients and controls. These data can be interpreted as a failure of patients to adapt their brain functioning to the task, which reflect in poorer behavioral performance.

Ethical Application of Precision Medicine to Schizophrenia Management

A recent effort has been made to better characterize the genetic architecture of schizophrenia, and apply a precision medicine model to its treatment. In pursuing this approach, it is likely that ethical concerns regarding cost-benefit uncertainties, the duty to inform and confidentiality will arise. Due to the complexity of schizophrenia’s genetic profile, research efforts must be weighed against the risk to human subjects and the required consumption of valuable healthcare resources. Genetic risk profiles must be judged in conjunction with actionability and must be fully explained to patients and others potentially affected. However, confidentiality should be of paramount importance due to the presence of a risk-state phenotype, meaning the associated presence of clinically detectable neurocognitive deficiencies in those with elevated schizophrenia risk profiles.

Are There Schizophrenia Genetic Markers and Mutations? A Systematic Review and Meta-Analyses

Background: Schizophrenia is a severe psychiatric disorder with a complex genetic factor determining its disease onset. Nevertheless, it is not clear in this mental disorder. Objective: To conduct a systematic review of articles regarding the genetic markers and mutations in schizophrenia. Methods: A systematic review of articles on genetic markers and mutations in schizophrenia, published from January 1, 2011, to September 7, 2015, on SCOPUS database was carried out. Search terms were “Genetic markers”, “Mutation”, and “Schizophrenia”. Results: Of the 527 retrieved studies, 31 met the eligibility criteria. Genetic polymorphism, Immune-associated genes, TCF4 and ZNF804A association with microRNA, Neuregulin gene, Chromosome 13q32 and 11p15.4, genes involved in glutamatergic via schizophrenia and brain structure, appeared to be associated with the origin of schizophrenia. Conclusion: Some studies show genes involved in several pathways leading to the disease pathogenesis such as that one related with the dopaminergic and immune system, or rare alleles. Some genes present no involvement in the etiology of this mental disorder. These findings clarify the genetic complexity of schizophrenia and affirm that together, the genes have an overall effect greater than the sum of the individual effect of each gene.

Polygenic Risk for Schizophrenia Influences Cortical Gyrification in 2 Independent General Populations.

Schizophrenia is highly heritable, whereas the effect of each genetic variant is very weak. Since clinical heterogeneity and complexity of schizophrenia is high, considerable effort has been made to relate genetic variants to underlying neurobiological aspects of schizophrenia (endophenotypes). Given the polygenic nature of schizophrenia, our goal was to form a measure of additive genetic risk and explore its relationship to cortical morphology. Utilizing the data from a recent genome-wide association study that included nearly 37 000 cases of schizophrenia, we computed a polygenic risk score (PGRS) for each subject in 2 independent and healthy general populations. We then investigated the effect of polygenic risk for schizophrenia on cortical gyrification calculated from 3.0T structural imaging data in the discovery dataset (N = 315) and replication dataset (N = 357). We found a consistent effect of the polygenic risk for schizophrenia on cortical gyrification in the inferior parietal lobules in 2 independent general-population samples. A higher PGRS was significantly associated with a lower local gyrification index in the bilateral inferior parietal lobles, where case-control differences have been reported in previous studies on schizophrenia. Our findings strongly support the effectiveness of both PGRSs and endophenotypes in establishing the genetic architecture of psychiatry. Our findings may provide some implications regarding individual differences in the genetic risk for schizophrenia to cortical morphology and brain development.

Decreased resting-state brain activity complexity in schizophrenia characterized by both increased regularity and randomness.

Schizophrenia is characterized by heterogeneous pathophysiology. Using multiscale entropy (MSE) analysis, which enables capturing complex dynamics of time series, we characterized MSE patterns of blood-oxygen-level-dependent (BOLD) signals across different time scales and determined whether BOLD activity in patients with schizophrenia exhibits increased complexity (increased entropy in all time scales), decreased complexity toward regularity (decreased entropy in all time scales), or decreased complexity toward uncorrelated randomness (high entropy in short time scales followed by decayed entropy as the time scale increases). We recruited 105 patients with schizophrenia with an age of onset between 18 and 35 years and 210 age- and sex-matched healthy volunteers. Results showed that MSE of BOLD signals in patients with schizophrenia exhibited two routes of decreased BOLD complexity toward either regular or random patterns. Reduced BOLD complexity toward regular patterns was observed in the cerebellum and temporal, middle, and superior frontal regions, and reduced BOLD complexity toward randomness was observed extensively in the inferior frontal, occipital, and postcentral cortices as well as in the insula and middle cingulum. Furthermore, we determined that the two types of complexity change were associated differently with psychopathology; specifically, the regular type of BOLD complexity change was associated with positive symptoms of schizophrenia, whereas the randomness type of BOLD complexity was associated with negative symptoms of the illness. These results collectively suggested that resting-state dynamics in schizophrenia exhibit two routes of pathologic change toward regular or random patterns, which contribute to the differences in syndrome domains of psychosis in patients with schizophrenia.

Allosteric Modulators for the Treatment of Schizophrenia: Targeting Glutamatergic Networks

Schizophrenia is a highly debilitating mental disorder which afflicts approximately 1% of the global population. Cognitive and negative deficits account for the lifelong disability associated with schizophrenia, whose symptoms are not effectively addressed by current treatments. New medicines are needed to treat these aspects of the disease. Neurodevelopmental, neuropathological, genetic, and behavioral pharmacological data indicate that schizophrenia stems from a dysfunction of glutamate synaptic transmission, particularly in frontal cortical networks. A number of novel pre- and postsynaptic mechanisms affecting glutamatergic synaptic transmission have emerged as viable targets for schizophrenia. While developing orthosteric glutamatergic agents for these targets has proven extremely difficult, targeting allosteric sites of these targets has emerged as a promising alternative. From a medicinal chemistry perspective, allosteric sites provide an opportunity of finding agents with better drug-like properties and greater target specificity. Furthermore, allosteric modulators are better suited to maintaining the highly precise temporal and spatial aspects of glutamatergic synaptic transmission. Herein, we review neuropathological and genomic/genetic evidence underscoring the importance of glutamate synaptic dysfunction in the etiology of schizophrenia and make a case for allosteric targets for therapeutic intervention. We review progress in identifying allosteric modulators of AMPA receptors, NMDA receptors, and metabotropic glutamate receptors, all with the aim of restoring physiological glutamatergic synaptic transmission. Challenges remain given the complexity of schizophrenia and the difficulty in studying cognition in animals and humans. Nonetheless, important compounds have emerged from these efforts and promising preclinical and variable clinical validation has been achieved.

Whole-Genome Sequencing in Pharmacogenetics

PharmacogenomicsVol. 14, No. 4 EditorialWhole-genome sequencing in pharmacogeneticsThomas J UrbanThomas J UrbanCenter for Human Genome Variation, Duke University Medical Center, 308 Research Drive, LSRC Wing B, Room 331A, Durham, NC 27708-1009, USA. Search for more papers by this authorEmail the corresponding author at thomas.urban@duke.eduPublished Online:25 Feb 2013https://doi.org/10.2217/pgs.12.211AboutSectionsView ArticleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInRedditEmail View articleKeywords: clinical sequencingdrug developmentnext-generation sequencingNGSpharmacogenomicsWESWGSwhole-exome sequencingReferences1 Maher B. Personal genomes: the case of the missing heritability. Nature456(7218),18–21 (2008).Crossref, Medline, CAS, Google Scholar2 Goldstein DB. Common genetic variation and human traits. N. Engl. J. 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De novo gene mutations highlight patterns of genetic and neural complexity in schizophrenia

To evaluate evidence for de novo etiologies in schizophrenia, we sequenced at high coverage the exomes of families recruited from two populations with distinct demographic structure and history. We sequenced a total of 795 exomes from 231 parent-proband trios enriched for sporadic schizophrenia cases, as well as 34 unaffected trios. We observed in cases an excess of non-synonymous single nucleotide variants as well as a higher prevalence of gene-disruptive de novo mutations. We found four genes (LAMA2, DPYD, TRRAP and VPS39) affected by recurrent de novo events within or across the two populations, a finding unlikely to have occurred by chance. We show that de novo mutations affect genes with diverse functions and developmental profiles but we also find a substantial contribution of mutations in genes with higher expression in early fetal life. Our results help define the pattern of genomic and neural architecture of schizophrenia.

Proteomic approaches to unravel the complexity of schizophrenia

Schizophrenia is a debilitating mental disorder that affects approximately 30 million people worldwide. The development and progression of this disease is now thought to be precipitated through a complex interaction between altered gene function and environmental factors. Proteomic analyses have been applied extensively over the past 10 years in studies of several tissues from schizophrenic patients, resulting in increased insight into the affected molecular pathways. In addition, these proteomic approaches have led to the identification of a set of molecular biomarker assays as the first blood-based test to aid in the diagnosis of schizophrenia. Here, we discuss the main outcome of these investigations and suggest a practical means of integrating and translating the findings between the brain and peripheral blood to increase our understanding of schizophrenia pathophysiology.

The Texture of the Real: Experimentation and Experience in Schizophrenia

The dominance of the neurosciences in psychiatric research raises questions about the relationship between research practices and the lived experience of mental illness. Here, I use data from a group of researchers focusing on neurocognition in schizophrenia to explore the problem of representation in psychiatric research and the forms that neuroscientific evidence assumes for those who produce it. These researchers grappled with the complexity of schizophrenia not by narrowing disease concepts to biological facts but by referencing measurement techniques to generate new versions of schizophrenia. By linking experimental findings to inchoate concepts of personhood and social experience, I found that they reframed and reinforced cultural values, including that those with schizophrenia are destined to a debased and deficient existence. I argue that cognition has emerged as an essential feature of schizophrenia not only because of its representational utility but also because of the ontological work the concept performs. In closing, I present some implications for the neurobiological and social sciences.

What Are We Looking for in New Antipsychotics?

Antipsychotics are the cornerstone of treatment for psychotic and some nonpsychotic disorders. However, despite pharmacologic advances, considerable areas of need remain. This article reviews desirable properties for future antipsychotics and considers how far current agents have come in achieving those objectives. Preferably, new antipsychotics should have a "balanced" pharmacodynamic profile that addresses the need for efficacy without compromising psychiatric or physical well-being; a safe, fast, and convenient pharmacokinetic profile; a definable therapeutic window, and availability in multiple formulations. Compared with available agents, new antipsychotics should ideally have at least similar efficacy for positive symptoms, agitation, and aggression and better efficacy for negative or cognitive symptoms, relapse prevention, treatment-resistant illness, and associated problems such as depression, anxiety, and substance abuse. Improved tolerability and subjective acceptability to patients are also important in promoting adherence and continued treatment. Finally, they should have improved effectiveness in facilitating functioning, subjective well-being, quality of life, and, ultimately, recovery. Given the complexity of schizophrenia, its unknown etiology and pathophysiology, and challenges in clinical trial design and conduct, it is not surprising that it has remained difficult to develop antipsychotics with novel mechanisms. To achieve true breakthroughs, we need greater insight into the pathophysiology underlying specific disease processes and therapeutic and adverse responses. It is hoped that research on drug-specific biomarkers that can predict response in specific patient groups will advance personalized psychiatric care and improve patient outcomes.