Metadata in Smartphone-Based Cognitive Assessments: Current State and Emerging Evidence in Psychiatric Disorders.

A number of recent studies have questioned whether, despite modern treatment, the natural course of bipolar illness today still involves multiple relapses and impaired psychosocial functioning. This prospective follow-up study examined longitudinal outcome in a large group of inpatients with affective disorders. Fifty-one bipolar manic patients and 49 unipolar depressed patients were interviewed three times: 1) during hospitalization, 2) approximately 2 years after discharge, and 3) approximately 4.5 years after discharge. Subjects were treated under routine conditions and assessed for global adjustment, rehospitalization, and work and social functioning. Only 41% of the bipolar group had a good overall outcome by the time of the 4.5-year follow-up. The bipolar patients had more severe work impairment than the unipolar group. More than one-half of the bipolar patients were rehospitalized at least once during the 4.5-year follow-up period. Outcome for both diagnostic groups improved significantly over time. Many contemporary bipolar patients demonstrate gradual improvement in the first several years after hospitalization. However, a subgroup approaching 60% still experience poor posthospital adjustment in one or more areas of functioning.

Optimal Ascertainment Strategies to Detect Linkage to Common Disease Alleles

Bipolar disorder is a common condition diagnosed by the occurrence of pathological mood elevation but most often dominated by dysphoria states. Over the past 10 years, understanding of bipolar disorder and the number of evidence-based treatments have increased dramatically. This article offers strategies for improving diagnostic confidence and simple benchmarks that facilitate integrating principles of evidence-based medicine into the management of patients with bipolar disorder. Simple systematic assessment techniques such as focusing the evaluation to assess the most extreme episode of mood elevation and longitudinal factors such as age of onset and course of illness can avoid errors of omission and raise diagnostic confidence. An iterative measurement-based treatment model that aims to bring patients and their supports into the collaborative care process for progressively better outcomes is recommended.

OPINION article Front. Aging Neurosci., 26 August 2015Sec. Alzheimer's Disease and Related Dementias Volume 7 - 2015 | https://doi.org/10.3389/fnagi.2015.00164

The Kraepelinian Dichotomy: Evidence From Developmental and Neuroimaging Studies

Demystifying borderline personality: critique of the concept and unorthodox reflections on its natural kinship with the bipolar spectrum

Understanding Associations Between Serious Mental Illness and HIV Among Patients in the VA Health System

Schizophrenia and velocardio-facial syndrome

Neuropsychiatric Significance of Subcortical Hyperintensity

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Despite a marked increase in treatment for bipolar disorder among youths, little is known about their pattern of service use. This article describes mental health service use in the year before and after a new clinical diagnosis of bipolar disorder. Claims were reviewed between April 1, 2004, and March 31, 2005, for 1,274,726 privately insured youths (17 years and younger) who were eligible for services at least one year before and after a service claim; 2,907 youths had new diagnosis of bipolar disorder during this period. Diagnoses of other mental disorders and prescriptions filled for psychotropic drugs were assessed in the year before and after the initial diagnosis of bipolar disorder. The one-year rate of a new diagnosis of bipolar disorder was .23%. During the year before the new diagnosis of bipolar disorder, youths were commonly diagnosed as having depressive disorder (46.5%) or disruptive behavior disorder (36.7%) and had often filled a prescription for an antidepressant (48.5%), stimulant (33.0%), mood stabilizer (31.8%), or antipsychotic (29.1%). Most youths with a new diagnosis of bipolar disorder had only one (28.8%) or two to four (28.7%) insurance claims for bipolar disorder in the year starting with the index diagnosis. The proportion starting mood stabilizers after the index diagnosis was highest for youths with five or more insurance claims for bipolar disorder (42.1%), intermediate for those with two to four claims (24.2%), and lowest for those with one claim (13.8%). Most youths with a new diagnosis of bipolar disorder had recently received treatment for depressive or disruptive behavior disorders, and many had no claims listing a diagnosis of bipolar disorder after the initial diagnosis. The service pattern suggests that a diagnosis of bipolar disorder is often given tentatively to youths treated for mental disorders with overlapping symptom profiles and is subsequently reconsidered.

More than 100 years ago Kraepelin proposed a very practical and persuasive solution to a long-standing problem in clinical psychiatry. He proposed to reduce heterogeneity by splitting the perplexing variety of psychopathological signs and symptoms, of patterns of deviant behavior and experiences, of short- and long-term course and outcome of functional disturbances into two major groups: schizophrenia (dementia praecox) and affective disorders (manic-depressive illness) (1). In this way, he created the so-called ‘Kraepelinian dichotomy’, which turned out to be clinically useful for subsequent decades. However, he himself got skeptical subsequently (2) if this simplistic solution really worked in practice as the number of ‘cases in-between’ were too numerous. About 70 years ago, the concept of schizoaffective disorders emerged from difficulties in practicing Kraepelin's dichotomy by separating schizophrenia and affective disorders. In 1933, Kasanin first coined this term (3). Although originally related to ‘reactive psychoses’ in the Scandinavian tradition (4), the term became transformed to indicate the intraindividual co-occurrence of both severe affective as well as severe psychotic syndromes, which did not fit in either of Kraepelin's categories. The widespread use of this term reflected the clinical need to consider border-cases separately. Many clinicians are probably motivated to use this category because of implications on the course of illness. However, qualitative inter-class differences cannot be detected: the most recent outcome study (5) saw a less poor outcome in schizoaffective disorders compared with schizophrenia, but it was difficult to distinguish schizoaffective and mood disorders with psychotic symptoms; a progressively worsening intermediate course was reported for both diagnostic groups. Thus, a dimensional view of schizoaffective outcome is recommended. In contrast to its clinical popularity, research investigations in this diagnostic category – although operational definitions became available – remained relatively rare as it becomes evident from a PubMed search (search terms in titles: schizoaffective disorder = 230 citations; schizophrenia = 13.297; bipolar disorder = 2.355; during a 10-year period 1995–2005). If this category became a research topic at all, it was a border-category of schizophrenia and/or affective disorders. Thus, the biological basis and the nature of this category ‘in-between’ remained obscure. Several reasons might account for this fact. An unequivocal definition of schizoaffective disorder was never attained. For example, the concepts of ICD-10 and DSM-IV strongly differ by the criterion of simultaneity or temporal contiguity. The available diagnostic definitions include so complex criteria that the reliability is relatively low (6). Thus, it does not come as a surprise that most cases with a schizoaffective episode change this diagnosis in subsequent episodes (7). Furthermore, both most widely used diagnostic manuals propose criteria which are fully different from the clinical conventions. This is now demonstrated by a careful diagnostic re-evaluation of a representative Danish in-patient sample (n = 59) with the diagnosis of schizoaffective disorder by Vollmer-Larsen et al. (8): not a single patient fulfilled either the DSM-IV or the ICD-10 criteria (full criteria) for schizoaffective disorder. The vast majority of cases were allocated either to schizophrenia or to affective disorders (ICD-10), both by the rater of the clinical records or by an automatic OPCRIT algorithm. This observation is important because the basic assumption for proposing the diagnostic entity of schizoaffective disorders is losing its validity. At the starting point for this diagnostic category ‘in-between’ schizophrenia and bipolar affective disorders were assumed to be due to two distinct disease processes. Doubts in this unproven hypothesis emerged already in the 1970s. Thus, the relationship between depression and schizophrenia has been studied in a variety of contexts in the past. For example, it was recognized that (i) postpsychotic depression was a common phenomenon, and (ii) postpsychotic depression was often preceded by depression, already at the beginning of the psychotic episode but overseen by the clinician [e.g. (9)]. Very recently, a most carefully conducted retrospective epidemiological study reported depressive symptoms and syndromes to be very common precursors of the first negative and psychotic symptoms in subjects developing schizophrenia later-on (10); it was convincingly concluded that depression presents an integral part or even the basic fundament of schizophrenia (10). In this perspective, schizoaffective disorders cannot be considered as a distinct disease entity between the two extremes of Kraepelin's dichotomy. This conclusion also goes together with recent family studies. In a huge case-register in Denmark schizoaffective disorders did not ‘breed true’, and a family history of schizoaffective disorders did not only increase the risk for schizoaffective disorder, but also for schizophrenia and affective disorder by a similar magnitude (11). An increasing number of family and twin studies report that intrafamilial cosegregation and concordance of schizophrenia and affective disorders are more common than expected by chance and point at shared genetic basis (12). Thus, on a clinical level the overlap between the syndromes of schizophrenia and affective disorders are too broad to be captured by the intermediate diagnosis of schizoaffective disorders. It might be argued that clinical phenomenology is too unspecific to clearly differentiate disease processes on a pathophysiological or molecular level. Can the prototypes schizophrenia and affective disorders be clearly distinguished on a neurobiological basis? Yet, they cannot! (13). Multiple neuropathological, biochemical and genetic communalities between schizophrenia and affective disorders (especially bipolar) were also recently detected, which add to the symptomatic overlap. Recently, common susceptibility genes such as NRG1, G72/G30 or DISC1 were detected to impact on schizophrenia, affective disorders as well as schizoaffective disorders (14). In addition to these communalities, both disorders also reveal diagnosis-specific etiological factors (as the susceptibility gene DTNBP1 for schizophrenia). Taken together, there is growing evidence that a substantial proportion of etiological factors is shared between schizophrenia and bipolar disorder; the contribution of these common determinants is particularly strong in the symptomatic interface, especially in schizoaffective disorders. In this perspective, schizoaffective disorders reflect a quantitative variation in the common etiological and pathophysiological underground of schizophrenia and affective disorders. In summary, the historical starting point of the concept of schizoaffective disorders is not valid any more. The diagnosis ‘schizoaffective disorder’ has not yet been unequivocally defined after more than 70 years, the available concurrent diagnostic definitions are not reliable. The most recommended diagnostic definitions in ICD-10 and DSM-IV even lack face validity because they do not fit with clinical conventions. Do we need this category any more? Is it more appropriate to broaden the concepts of schizophrenia and bipolar disorder even more and concede that etiological communalities might come up as symptomatic overlaps? Or is it even more appropriate to discard categorical diagnostic concepts and substitute them by dimensions: a psychotic, a manic and a depressive one, which allow graduations and overlaps? Currently, these questions are open to discussion. The task forces for new versions of the DSM- and ICD-diagnostic systems and manuals have to consider these recent insights and developments. They would be badly advised if they would just continue the historical and current concepts of schizoaffective disorders into the future.

Resolving the Discrepancy in Childhood Bipolar High-Risk Study Findings

BackgroundRecent advances in cognitive digital assessment methodology, including high-frequency, ambulatory assessments, promise to improve the detection of subtle cognitive changes. Computational modeling approaches may further improve the sensitivity of digital cognitive assessments to detect subtle cognitive changes by capturing features that map onto core cognitive processes.ObjectiveWe explored the validity of a brief smartphone-based adaptation of a visual working memory task that has shown sensitivity for detecting preclinical Alzheimer disease risk. We aimed to optimize properties of the task for computational cognitive feature extraction with drift diffusion modeling.MethodsWe analyzed data from 68 participants (n=47, 69% women; n=55, 81% White; mean age 49, SD 14; range 24-80 years) who completed 60 trials for each of 16 variations of a visual working memory binding task (the Color Shapes task) on smartphones, over an 8-day period. A drift diffusion model was fit to the response time and accuracy data from the task. We experimentally manipulated 3 properties of the Color Shapes task (study time, probability of change, and choice urgency) to test how they yielded differences in key drift diffusion model parameters (drift rate, initial bias toward a response option, and caution in decision-making). We also evaluated how an additional task property, the test array size, impacted responses across all conditions. For array size, we tested a whole display of 3 shapes against a single probe of 1 shape only.ResultsThe 3 task property manipulations yielded the following results: (1) increasing the ratio of different responses was credibly associated with higher initial bias toward the different response (mean 0.06, SD 0.02 for the whole display; mean 0.15, SD 0.02, for the single probe condition); (2) increasing the choice urgency during the test phase was credibly associated with decreased caution in decision-making in the single probe condition (mean −0.04, SD 0.02) but not in the whole display (mean −0.01, SD 0.02); and (3) contrary to expectation, longer study times did not yield a credibly faster drift rate but produced credibly slower ones for the whole display condition (mean −0.28, SD 0.05) and a null effect for the single probe condition (mean 0.01, SD 0.05). In addition, as expected, we found that individual differences in drift rate were associated with age in both array sizes (r=−0.45 with Bayes factor=191), with older participants having a slower drift rate. Older participants also showed higher caution (r=0.42 with Bayes factor=80.76) in the single probe condition.ConclusionsWe identified a version of the Color Shapes task optimized for smartphone-based cognitive assessments in real-world settings, with data designed for analysis through computational cognitive modeling. Our proposed approach can advance the development of tools for efficient and effective early detection and monitoring of risk for Alzheimer disease.