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Abstract Cognitive dysfunction is a core feature of schizophrenia. The subtyping of cognitive performance in schizophrenia may aid the refinement of disease heterogeneity. The literature on cognitive subtyping in schizophrenia, however, is limited by variable methodologies and neuropsychological tasks, lack of validation, and paucity of studies examining longitudinal stability of profiles. It is also unclear if cognitive profiles represent a single linear severity continuum or unique cognitive subtypes. Cognitive performance measured with the Brief Assessment of Cognition in Schizophrenia was analyzed in schizophrenia patients (n = 767). Healthy controls (n = 1012) were included as reference group. Latent profile analysis was performed in a schizophrenia discovery cohort (n = 659) and replicated in an independent cohort (n = 108). Longitudinal stability of cognitive profiles was evaluated with latent transition analysis in a 10-week follow-up cohort. Confirmatory factor analysis (CFA) was carried out to investigate if cognitive profiles represent a unidimensional structure. A 4-profile solution was obtained from the discovery cohort and replicated in an independent cohort. It comprised of a “less-impaired” cognitive subtype, 2 subtypes with “intermediate cognitive impairment” differentiated by executive function performance, and a “globally impaired” cognitive subtype. This solution showed relative stability across time. CFA revealed that cognitive profiles are better explained by distinct meaningful profiles than a severity linear continuum. Associations between profiles and negative symptoms were observed. The subtyping of schizophrenia patients based on cognitive performance and its associations with symptomatology may aid phenotype refinement, mapping of specific biological mechanisms, and tailored clinical treatments.

Rates of mild cognitive impairment (MCI) have varied substantially, depending on the criteria used and the samples surveyed. The present investigation used a psychometric algorithm for identifying MCI and its stability to determine if low cognitive functioning was related to poorer longitudinal outcomes. The Advanced Cognitive Training of Independent and Vital Elders (ACTIVE) study is a multi-site longitudinal investigation of long-term effects of cognitive training with older adults. ACTIVE exclusion criteria eliminated participants at highest risk for dementia (i.e., Mini-Mental State Examination < 23). Using composite normative for sample- and training-corrected psychometric data, 8.07% of the sample had amnestic impairment, while 25.09% had a non-amnestic impairment at baseline. Poorer baseline functional scores were observed in those with impairment at the first visit, including a higher rate of attrition, depressive symptoms, and self-reported physical functioning. Participants were then classified based upon the stability of their classification. Those who were stably impaired over the 5-year interval had the worst functional outcomes (e.g., Instrumental Activities of Daily Living performance), and inconsistency in classification over time also appeared to be associated increased risk. These findings suggest that there is prognostic value in assessing and tracking cognition to assist in identifying the critical baseline features associated with poorer outcomes. (JINS, 2012, 18, 1–15)

Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been increasingly integrated into imaging signatures of AD by means of classification frameworks, offering promising tools for individualized diagnosis and prognosis. We reviewed neuroimaging-based studies for AD and mild cognitive impairment classification, selected after online database searches in Google Scholar and PubMed (January, 1985–June, 2016). We categorized these studies based on the following neuroimaging modalities (and sub-categorized based on features extracted as a post-processing step from these modalities): i) structural magnetic resonance imaging [MRI] (tissue density, cortical surface, and hippocampal measurements), ii) functional MRI (functional coherence of different brain regions, and the strength of the functional connectivity), iii) diffusion tensor imaging (patterns along the white matter fibers), iv) fluorodeoxyglucose positron emission tomography (FDG-PET) (metabolic rate of cerebral glucose), and v) amyloid-PET (amyloid burden). The studies reviewed indicate that the classification frameworks formulated on the basis of these features show promise for individualized diagnosis and prediction of clinical progression. Finally, we provided a detailed account of AD classification challenges and addressed some future research directions. •We reviewed Alzheimer’s disease neuroimaging-based classification studies.•We covered structural MRI, fMRI, DTI, amyloid-PET, FDG-PET, and multimodalities.•The reported studies were validated through appropriate cross-validation.•We categorized the studies based on feature extraction methods.•We discussed challenges, disparities in experimental conditions and future directions.

Key Points The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) provides a framework for the diagnosis of neurocognitive disorders based on three syndromes: delirium, mild neurocognitive disorder and major neurocognitive disorder Major neurocognitive disorder is mostly synonymous with dementia, although the criteria have been modified so that impairments in learning and memory are not necessary for diagnosis DSM-5 describes criteria to delineate specific aetiological subtypes of mild and major neurocognitive disorder The diagnostic certainty of an aetiological diagnosis is based on clinical features and biomarkers, and can be qualified as probable or possible The DSM-5 criteria are consistent with those developed by various expert groups for the different aetiological subtypes of neurocognitive disorders Further validation in clinical practice is necessary, but we expect these criteria will have high reliability and validity, and widespread adoption will bring consistency to the diagnosis of diverse neurocognitive disorders The fifth edition of the American Psyciatric Association's Diagnostic and Statistical Manual for Mental Disorders (DSM-5) was published in 2013, and with it came new diagnostic criteria for mild cognitive impairment and dementia. In this Review, members of the working group tasked with writing the DSM-5 criteria for neurocognitive disorders present the new approach to categorization and diagnosis. Three key syndromes are recognized—delirium, mild neurocognitive disorder and major neurocognitive disorder—and each can have distinct aetiological subtypes. Neurocognitive disorders—including delirium, mild cognitive impairment and dementia—are characterized by decline from a previously attained level of cognitive functioning. These disorders have diverse clinical characteristics and aetiologies, with Alzheimer disease, cerebrovascular disease, Lewy body disease, frontotemporal degeneration, traumatic brain injury, infections, and alcohol abuse representing common causes. This diversity is reflected by the variety of approaches to classifying these disorders, with separate groups determining criteria for each disorder on the basis of aetiology. As a result, there is now an array of terms to describe cognitive syndromes, various definitions for the same syndrome, and often multiple criteria to determine a specific aetiology. The fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) provides a common framework for the diagnosis of neurocognitive disorders, first by describing the main cognitive syndromes, and then defining criteria to delineate specific aetiological subtypes of mild and major neurocognitive disorders. The DSM-5 approach builds on the expectation that clinicians and research groups will welcome a common language to deal with the neurocognitive disorders. As the use of these criteria becomes more widespread, a common international classification for these disorders could emerge for the first time, thus promoting efficient communication among clinicians and researchers.

Current group-average analysis suggests quantitative but not qualitative cognitive differences between schizophrenia (SZ) and bipolar disorder (BD). There is increasing recognition that cognitive within-group heterogeneity exists in both disorders, but it remains unclear as to whether between-group comparisons of performance in cognitive subgroups emerging from within each of these nosological categories uphold group-average findings. We addressed this by identifying cognitive subgroups in large samples of SZ and BD patients independently, and comparing their cognitive profiles. The utility of a cross-diagnostic clustering approach to understanding cognitive heterogeneity in these patients was also explored. Hierarchical clustering analyses were conducted using cognitive data from 1541 participants (SZ n = 564, BD n = 402, healthy control n = 575). Three qualitatively and quantitatively similar clusters emerged within each clinical group: a severely impaired cluster, a mild-moderately impaired cluster and a relatively intact cognitive cluster. A cross-diagnostic clustering solution also resulted in three subgroups and was superior in reducing cognitive heterogeneity compared with disorder clustering independently. Quantitative SZ-BD cognitive differences commonly seen using group averages did not hold when cognitive heterogeneity was factored into our sample. Members of each corresponding subgroup, irrespective of diagnosis, might be manifesting the outcome of differences in shared cognitive risk factors.

Subjective cognitive decline (SCD) is recognized as a potential early risk factor for Alzheimer's disease (AD) and other dementias, but standardized approaches to assess and address the concerns of individuals with SCD remain limited. Moreover, its prevalence is increasing in memory clinics, highlighting an unmet clinical need for tailored protocols and evidence-based guidance. A recent work proposed a taxonomy for SCD, categorizing individuals into three subgroups based on psychological factors (e.g., anxiety, depression, neuroticism), comorbidities (e.g., vascular risk factors, neurological or somatic comorbidities), and no apparent cause. This taxonomy provides a structured framework for interpreting SCD and guiding management strategies. However, its clinical applicability requires validation in independent cohorts. The SCD taxonomy was initially tested on a small subset of individuals from the Geneva Memory Center, classifying individuals into the three previously introduced subgroups using detailed clinical reports (Ribaldi et al., 2024). Currently, the taxonomy is being applied to a larger retrospective cohort. All participants underwent comprehensive neuropsychological assessments and clinical evaluations, with a subset also featuring biomarker data. To streamline its application, the taxonomy has been converted into an automated algorithm that assigns individuals to subgroups based on available clinical data. The ongoing effort focuses on validating the accuracy of this automated classification by comparing it with expert clinical judgments. Once this step is complete, baseline differences among the subgroups will be analyzed, followed by an assessment of longitudinal cognitive trajectories. We will present the taxonomy structure and preliminary findings from the validation cohort. Initial findings from the original cohort demonstrated the taxonomy's ability to stratify individuals based on psychological factors and somatic comorbidities, revealing significant differences in demographics, clinical features, and cognitive trajectories across subgroups. Specifically, the SCD group with no apparent cause exhibited faster cognitive decline and an AD-like biomarker profile. Ongoing analyses aim to confirm these findings in the larger cohort. Preliminary findings support the applicability of the SCD taxonomy, underscoring its potential as a clinical tool for risk stratification and personalized intervention. This validation represents a critical step toward integrating the taxonomy into routine clinical workflows, addressing the growing needs of individuals with SCD.

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n=30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org. [Display omitted] •We objectively compared 29 algorithms for computer-aided diagnosis of dementia.•15 international teams tested their algorithms on a blinded multicenter dataset.•Algorithms combining types of features performed best: the highest AUC was 78.8%.

Objectives: To refine mild cognitive impairment (MCI) diagnostic criteria, we examined progression to dementia using two approaches to identifying MCI. Methods: A total of 1203 Framingham Heart Study participants were classified at baseline as cognitively normal or MCI (overall and four MCI subtypes) via conventional Petersen/Winblad criteria (single cognitive test impaired per domain, >1.5 SD below expectations) or Jak/Bondi criteria (two tests impaired per domain, >1 SD below norms). Cox proportional hazards models were constructed to examine the association between each MCI definition and incident dementia. Results: The Petersen/Winblad criteria classified 34% of participants as having MCI while the Jak/Bondi criteria classified 24% as MCI. Over a mean follow-up of 9.7 years, 58 participants (5%) developed incident dementia. Both MCI criteria were associated with incident dementia [Petersen/Winblad: hazards ratio (HR) = 2.64; p-value=.0002; Jak/Bondi: HR=3.30; p-value <.0001]. When both MCI definitions were included in the same model, only the Jak/Bondi definition remained statistically significantly associated with incident dementia (HR=2.47; p-value=.008). Multi-domain amnestic and single domain non-amnestic MCI subtypes were significantly associated with incident dementia for both diagnostic approaches (all p-values <.01). Conclusions: The Jak/Bondi MCI criteria had a similar association with dementia as the conventional Petersen/Winblad MCI criteria, despite classifying ~30% fewer participants as having MCI. Further exploration of alternative methods to conventional MCI diagnostic criteria is warranted. (JINS, 2016, 22, 937–943)

Little is known about ageing with autism spectrum disorder (ASD). We examined the characteristics of adults referred to a specialist diagnostic centre for assessment of possible ASD, 100 of whom received an ASD diagnosis and 46 did not. Few demographic differences were noted between the groups. Comorbid psychiatric disorders were high in individuals with ASD (58 %) and non-ASD (59 %). Individuals who received an ASD diagnosis had higher self-rated severity of ASD traits than non-ASD individuals. Within the ASD group, older age was associated with higher ratings of ASD traits and better cognitive performance. One interpretation is that general cognitive ability and the development of coping strategies across the lifespan, do not necessarily reduce ASD traits but may mitigate their effects.

ObjectiveInconsistent results exist regarding the cognitive profile in patients with Parkinson's disease with mild cognitive impairment (PD-MCI). We aimed at providing data on this topic from a large cohort of patients with PD-MCI.MethodsSociodemographic, clinical and neuropsychological baseline data from patients with PD-MCI recruited in the multicentre, prospective, observational DEMPARK/LANDSCAPE study were analysed.Results269 patients with PD-MCI (age 67.8±7.4, Unified Parkinson's Disease Rating Scale (UPDRS-III) scores 23.2±11.6) were included. PD-MCI subtypes were 39.4% non-amnestic single domain, 30.5% amnestic multiple domain, 23.4% non-amnestic multiple domain and 6.7% amnestic single domain. Executive functions were most frequently impaired. The most sensitive tests to detect cognitive dysfunctions were the Modified Card Sorting Test, digit span backwards and word list learning direct recall. Multiple stepwise regression analyses showed that global cognition, gender and age, but not education or disease-related parameters predicted PD-MCI subtypes.ConclusionsThis study with the so far largest number of prospectively recruited patients with PD-MCI indicates that non-amnestic PD-MCI is more frequent than amnestic PD-MCI; executive dysfunctions are the most typical cognitive symptom in PD-MCI; and age, gender and global cognition predict the PD-MCI subtype. Longitudinal data are needed to test the hypothesis that patients with PD-MCI with specific cognitive profiles have different risks to develop dementia.