BrainBase: a curated knowledgebase for brain diseases.

Brain is the central organ of the nervous system and any brain disease can seriously affect human health. Here we present BrainBase (https://ngdc.cncb.ac.cn/brainbase), a curated knowledgebase for brain diseases that aims to provide a whole picture of brain diseases and associated genes. Specifically, based on manual curation of 2768 published articles along with information retrieval from several public databases, BrainBase features comprehensive collection of 7175 disease-gene associations spanning a total of 123 brain diseases and linking with 5662 genes, 16 591 drug-target interactions covering 2118 drugs/chemicals and 623 genes, and five types of specific genes in light of expression specificity in brain tissue/regions/cerebrospinal fluid/cells. In addition, considering the severity of glioma among brain tumors, the current version of BrainBase incorporates 21 multi-omics datasets, presents molecular profiles across various samples/conditions and identifies four groups of glioma featured genes with potential clinical significance. Collectively, BrainBase integrates not only valuable curated disease-gene associations and drug-target interactions but also molecular profiles through multi-omics data analysis, accordingly bearing great promise to serve as a valuable knowledgebase for brain diseases.

Deep learning for brain disorders: from data processing to disease treatment.

In order to reach precision medicine and improve patients' quality of life, machine learning is increasingly used in medicine. Brain disorders are often complex and heterogeneous, and several modalities such as demographic, clinical, imaging, genetics and environmental data have been studied to improve their understanding. Deep learning, a subpart of machine learning, provides complex algorithms that can learn from such various data. It has become state of the art in numerous fields, including computer vision and natural language processing, and is also growingly applied in medicine. In this article, we review the use of deep learning for brain disorders. More specifically, we identify the main applications, the concerned disorders and the types of architectures and data used. Finally, we provide guidelines to bridge the gap between research studies and clinical routine.

Types of Cerebral Herniation and Their Imaging Features.

Cerebral herniation, defined as a shift of cerebral tissue from its normal location into an adjacent space, is a life-threatening condition that requires prompt diagnosis. The imaging spectrum can range from subtle changes to clear displacement of brain structures. For radiologists, it is fundamental to be familiar with the different imaging findings of the various subtypes of brain herniation. Brain herniation syndromes are commonly classified on the basis of their location as intracranial and extracranial hernias. Intracranial hernias can be further divided into three types: (a) subfalcine hernia; (b) transtentorial hernia, which can be ascending or descending (lateral and central); and (c) tonsillar hernia. Brain herniation may produce brain damage, compress cranial nerves and vessels causing hemorrhage or ischemia, or obstruct the normal circulation of cerebrospinal fluid, producing hydrocephalus. Owing to its location, each type of hernia may be associated with a specific neurologic syndrome. Knowledge of the clinical manifestations ensures a focused imaging analysis. To make an accurate diagnosis, the authors suggest a six-key-point approach: comprehensive analysis of a detailed history of the patient and results of clinical examination, knowledge of anatomic landmarks, direction of mass effect, recognition of displaced structures, presence of indirect radiologic findings, and possible complications. CT and MRI are the imaging modalities of choice used for establishing a correct diagnosis and guiding therapeutic decisions. They also have important prognostic implications. The preferred imaging modality is CT: the acquisition time is shorter and it is less expensive and more widely available. Patients with brain herniation are generally in critical clinical condition. Making a prompt diagnosis is fundamental for the patient's safety.©RSNA, 2019.

Whither Research Domain Criteria?

In 2010, the National Institute of Mental Health launched the Research Diagnostic Criteria (RDoC) as a research framework aimed at advancing research into the etiology of mental disorders, the development of clinically actionable biomarkers, and the eventual development of precision medications. The foundation of RDoC in that first phase rested in the assumption that mental disorders are brain disorders that originate in aberrant neural circuitry, and that therapeutic advances could flow from alterations in that circuitry. RDoC proposed a matrix of psychological constructs with seven levels of analysis ranging from the cell to self-report, but with neural circuitry at the center. In 2016, another model was proposed in which neural circuitry became equivalent to other units of analyses. With the advent of a new Director of the NIMH, the emphasis returned to neural circuitry as a priority, along with computational psychiatry. Have these shifts undermined the RDoC project?

Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis.

A psychological disorder is a mutilation state of the body that intervenes the imperative functioning of the mind or brain. In the last few years, the number of psychological disorders patients has been significantly raised. This paper presents a comprehensive review of some of the major human psychological disorders (stress, depression, autism, anxiety, Attention-deficit hyperactivity disorder (ADHD), Alzheimer, Parkinson, insomnia, schizophrenia and mood disorder) mined using different supervised and nature-inspired computing techniques. A systematic review methodology based on three-dimensional search space i.e. disease diagnosis, psychological disorders and classification techniques has been employed. This study reviews the discipline, models, and methodologies used to diagnose different psychological disorders. Initially, different types of human psychological disorders along with their biological and behavioural symptoms have been presented. The racial effects on these human disorders have been briefly explored. The morbidity rate of psychological disordered Indian patients has also been depicted. The significance of using different supervised learning and nature-inspired computing techniques in the diagnosis of different psychological disorders has been extensively examined and the publication trend of the related articles has also been comprehensively accessed. The brief details of the datasets used in mining these human disorders have also been shown. In addition, the effect of using feature selection on the predictive rate of accuracy of these human disorders is also presented in this study. Finally, the research gaps have been identified that witnessed that there is a full scope for diagnosis of mania, insomnia, mood disorder using emerging nature-inspired computing techniques. Moreover, there is a need to explore the use of a binary or chaotic variant of different nature-inspired computing techniques in the diagnosis of different human psychological disorders. This study will serve as a roadmap to guide the researchers who want to pursue their research work in the mining of different psychological disorders.

The epileptic encephalopathy jungle - from Dr West to the concepts of aetiology-related and developmental encephalopathies.

PURPOSE OF REVIEW: We aim to further disentangle the jungle of terminology of epileptic encephalopathy and provide some insights into the current understanding about the aetiology and pathophysiology of this process. We cover also the key features of epilepsy syndromes of infancy and childhood which are considered at high risk of developing an epileptic encephalopathy. RECENT FINDINGS: The concept of 'epileptic encephalopathy' has progressively been elaborated by the International League Against Epilepsy according to growing clinical and laboratory evidence. It defines a process of neurological impairment caused by the epileptic activity itself and, therefore, potentially reversible with successful treatment, although to a variable extent. Epileptic activity interfering with neurogenesis, synaptogenesis, and normal network organization as well as triggering neuroinflammation are among the possible pathophysiological mechanisms leading to the neurological compromise. This differs from the newly introduced concept of 'developmental encephalopathy' which applies to where the epilepsy and developmental delay are both because of the underlying aetiology and aggressive antiepileptic treatment may not be helpful. SUMMARY: The understanding and use of correct terminology is crucial in clinical practice enabling appropriate expectations of antiepileptic treatment. Further research is needed to elucidate underlying pathophysiological mechanisms, define clear outcome predictors, and find new treatment targets.

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.

Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in recent years. Using a variety of neuroimaging modalities such as structural, functional and diffusion MRI, along with machine learning techniques, hundreds of studies have been carried out for accurate classification of patients with heterogeneous mental and neurodegenerative disorders such as schizophrenia and Alzheimer's disease. More than 500 studies have been published during the past quarter century on single subject prediction focused on a multiple brain disorders. In the first part of this study, we provide a survey of more than 200 reports in this field with a focus on schizophrenia, mild cognitive impairment (MCI), Alzheimer's disease (AD), depressive disorders, autism spectrum disease (ASD) and attention-deficit hyperactivity disorder (ADHD). Detailed information about those studies such as sample size, type and number of extracted features and reported accuracy are summarized and discussed. To our knowledge, this is by far the most comprehensive review of neuroimaging-based single subject prediction of brain disorders. In the second part, we present our opinion on major pitfalls of those studies from a machine learning point of view. Common biases are discussed and suggestions are provided. Moreover, emerging trends such as decentralized data sharing, multimodal brain imaging, differential diagnosis, disease subtype classification and deep learning are also discussed. Based on this survey, there is extensive evidence showing the great potential of neuroimaging data for single subject prediction of various disorders. However, the main bottleneck of this exciting field is still the limited sample size, which could be potentially addressed by modern data sharing models such as the ones discussed in this paper. Emerging big data technologies and advanced data-intensive machine learning methodologies such as deep learning have coincided with an increasing need for accurate, robust and generalizable single subject prediction of brain disorders during an exciting time. In this report, we survey the past and offer some opinions regarding the road ahead.

Association between Various Brain Pathologies and Gait Disturbance.

BACKGROUND: Approximately 30% of older adults have disrupted gait. It is associated with increased risk of cognitive decline, disability, dementia, and death. Additionally, most older adults present with 1 or more neuropathologies at autopsy. Recently, there has been an effort to investigate the association between subclinical neuropathology and gait. SUMMARY: We reviewed studies that investigated the association between gait and neuropathologies. Although all pathologies reviewed were associated with gait, grey matter atrophy was most consistently linked with poorer gait performance. Studies investigating the association between white matter and gait focused primarily on total white matter. Future research using more parsed regional analysis will provide more insight into this relationship. Evidence from studies investigating neuronal activity and gait suggests that gait disruption is associated with both under- and overactivation. Additional research is needed to delineate these conflicting results. Lastly, early evidence suggests that both amyloid and tau aggregation negatively impact multiple gait parameters, but additional studies are warranted. Overall, there was substantial methodological heterogeneity and a paucity of longitudinal studies. Key Messages: Longitudinal studies mapping changes in different types of neuropathology as they relate to changes in multiple gait parameters are needed to better understand trajectories of pathology and gait.

Framing Tobacco Dependence as a "Brain Disease": Implications for Policy and Practice.

INTRODUCTION: Like other forms of drug dependence, tobacco dependence is increasingly being described as a "chronic brain disease." The potential consequences of this medical labelling have been examined in relation to other addictions, but the implications for tobacco control have been neglected. Some have posited that biomedical conceptions of addiction will reduce stigma and increase uptake of efficacious treatments. Others have countered that it could increase stigma, reduce treatment seeking, and deter unassisted quitting. We explored how smokers respond to the labelling of smoking as a brain disease. METHODS: Semi-structured interviews with 29 Australian smokers recruited using purposive sampling. Thematic analysis was used to analyze the results. RESULTS: Most participants questioned the accuracy of the brain disease label as applied to smoking. They believed that smoking was not a chronic disease because they perceived smoking to be an individual's choice. In addition, many believed that this label would increase the stigma that they already felt and, did not want to adopt a "sick role" in relation to their smoking. CONCLUSIONS: Describing smoking as a brain disease is more likely to alienate smokers than to engage them in quitting. The application of overly medical labels of smoking are inconsistent with smokers own conceptualizations of their smoking, and may have unintended consequences if they are widely disseminated in healthcare settings or antismoking campaigns. IMPLICATIONS: The participants in this project believed that biomedical labels of smoking as a "brain disease" or a "chronic disease" were discordant their existing understandings of their smoking. Explanations of addiction that downplay or ignore the role of choice and autonomy risk being perceived as irrelevant by smokers, and could lead to suspicion of health professionals or an unwillingness to seek treatment.

Improving the Identification of Neonatal Encephalopathy: Utility of a Web-Based Video Tool.

Objective This study tested the effectiveness of a video teaching tool in improving identification and classification of encephalopathy in infants. Study Design We developed an innovative video teaching tool to help clinicians improve their skills in interpreting the neonatal neurological examination for grading encephalopathy. Pediatric residents were shown 1-minute video clips demonstrating exam findings in normal neonates and neonates with various degrees of encephalopathy. Findings from five domains were demonstrated: spontaneous activity, level of alertness, posture/tone, reflexes, and autonomic responses. After each clip, subjects were asked to identify whether the exam finding was normal or consistent with mild, moderate, or severe abnormality. Subjects were then directed to a web-based teaching toolkit, containing a compilation of videos demonstrating normal and abnormal findings on the neonatal neurological examination. Immediately after training, subjects underwent posttesting, again identifying exam findings as normal, mild, moderate, or severe abnormality. Results Residents improved in their overall ability to identify and classify neonatal encephalopathy after viewing the teaching tool. In particular, the identification of abnormal spontaneous activity, reflexes, and autonomic responses were most improved. Conclusion This pretest/posttest evaluation of an educational tool demonstrates that after viewing our toolkit, pediatric residents were able to improve their overall ability to detect neonatal encephalopathy.

Consensus classification of posterior cortical atrophy.

INTRODUCTION: A classification framework for posterior cortical atrophy (PCA) is proposed to improve the uniformity of definition of the syndrome in a variety of research settings. METHODS: Consensus statements about PCA were developed through a detailed literature review, the formation of an international multidisciplinary working party which convened on four occasions, and a Web-based quantitative survey regarding symptom frequency and the conceptualization of PCA. RESULTS: A three-level classification framework for PCA is described comprising both syndrome- and disease-level descriptions. Classification level 1 (PCA) defines the core clinical, cognitive, and neuroimaging features and exclusion criteria of the clinico-radiological syndrome. Classification level 2 (PCA-pure, PCA-plus) establishes whether, in addition to the core PCA syndrome, the core features of any other neurodegenerative syndromes are present. Classification level 3 (PCA attributable to AD [PCA-AD], Lewy body disease [PCA-LBD], corticobasal degeneration [PCA-CBD], prion disease [PCA-prion]) provides a more formal determination of the underlying cause of the PCA syndrome, based on available pathophysiological biomarker evidence. The issue of additional syndrome-level descriptors is discussed in relation to the challenges of defining stages of syndrome severity and characterizing phenotypic heterogeneity within the PCA spectrum. DISCUSSION: There was strong agreement regarding the definition of the core clinico-radiological syndrome, meaning that the current consensus statement should be regarded as a refinement, development, and extension of previous single-center PCA criteria rather than any wholesale alteration or redescription of the syndrome. The framework and terminology may facilitate the interpretation of research data across studies, be applicable across a broad range of research scenarios (e.g., behavioral interventions, pharmacological trials), and provide a foundation for future collaborative work.

Descriptive study of symptomatic epilepsy by age of onset in patients with a 3-year follow-up at the Neuropaediatric Department of a reference centre. / Estudio descriptivo de las epilepsias sintomáticas según edad de inicio controladas durante 3 años en una Unidad de Neuropediatría de referencia regional.

OBJECTIVE: We conducted a descriptive study of symptomatic epilepsy by age at onset in a cohort of patients who were followed up at a neuropaediatric department of a reference hospital over a 3-year period PATIENTS AND METHODS: We included all children with epilepsy who were followed up from January 1, 2008 to December 31, 2010 RESULTS: Of the 4595 children seen during the study period, 605 (13.17%) were diagnosed with epilepsy; 277 (45.79%) of these had symptomatic epilepsy. Symptomatic epilepsy accounted for 67.72% and 61.39% of all epilepsies starting before one year of age, or between the ages of one and 3, respectively. The aetiologies of symptomatic epilepsy in our sample were: prenatal encephalopathies (24.46% of all epileptic patients), perinatal encephalopathies (9.26%), post-natal encephalopathies (3.14%), metabolic and degenerative encephalopathies (1.98%), mesial temporal sclerosis (1.32%), neurocutaneous syndromes (2.64%), vascular malformations (0.17%), cavernomas (0.17%), and intracranial tumours (2.48%). In some aetiologies, seizures begin before the age of one; these include Down syndrome, genetic lissencephaly, congenital cytomegalovirus infection, hypoxic-ischaemic encephalopathy, metabolic encephalopathies, and tuberous sclerosis. CONCLUSIONS: The lack of a universally accepted classification of epileptic syndromes makes it difficult to compare series from different studies. We suggest that all epilepsies are symptomatic because they have a cause, whether genetic or acquired. The age of onset may point to specific aetiologies. Classifying epilepsy by aetiology might be a useful approach. We could establish 2 groups: a large group including epileptic syndromes with known aetiologies or associated with genetic syndromes which are very likely to cause epilepsy, and another group including epileptic syndromes with no known cause. Thanks to the advances in neuroimaging and genetics, the latter group is expected to become increasingly smaller.

New classification efforts in epilepsy: Opportunities for clinical neurosciences.

Classification is truly a scientific endeavor. In recent years, the use of the term in clinical epilepsy has diverged from the purpose of the intellectual process of classification in the sciences. As genetics and molecular biology come into their own, scientific classification may finally come to epilepsy and begin to shed light on the many aspects of brain disorders that are characterized by seizures but which are truly multifaceted disorders and deserve more comprehensive, multidisciplinary approaches than they have received previously. This article is part of a Special Issue entitled "The new approach to classification: Rethinking cognition and behavior in epilepsy".

Case definition and classification of leukodystrophies and leukoencephalopathies.

OBJECTIVE: An approved definition of the term leukodystrophy does not currently exist. The lack of a precise case definition hampers efforts to study the epidemiology and the relevance of genetic white matter disorders to public health. METHOD: Thirteen experts at multiple institutions participated in iterative consensus building surveys to achieve definition and classification of disorders as leukodystrophies using a modified Delphi approach. RESULTS: A case definition for the leukodystrophies was achieved, and a total of 30 disorders were classified under this definition. In addition, a separate set of disorders with heritable white matter abnormalities but not meeting criteria for leukodystrophy, due to presumed primary neuronal involvement and prominent systemic manifestations, was classified as genetic leukoencephalopathies (gLE). INTERPRETATION: A case definition of leukodystrophies and classification of heritable white matter disorders will permit more detailed epidemiologic studies of these disorders.

Discovering transnosological molecular basis of human brain diseases using biclustering analysis of integrated gene expression data.

BACKGROUND: It has been reported that several brain diseases can be treated as transnosological manner implicating possible common molecular basis under those diseases. However, molecular level commonality among those brain diseases has been largely unexplored. Gene expression analyses of human brain have been used to find genes associated with brain diseases but most of those studies were restricted either to an individual disease or to a couple of diseases. In addition, identifying significant genes in such brain diseases mostly failed when it used typical methods depending on differentially expressed genes. RESULTS: In this study, we used a correlation-based biclustering approach to find coexpressed gene sets in five neurodegenerative diseases and three psychiatric disorders. By using biclustering analysis, we could efficiently and fairly identified various gene sets expressed specifically in both single and multiple brain diseases. We could find 4,307 gene sets correlatively expressed in multiple brain diseases and 3,409 gene sets exclusively specified in individual brain diseases. The function enrichment analysis of those gene sets showed many new possible functional bases as well as neurological processes that are common or specific for those eight diseases. CONCLUSIONS: This study introduces possible common molecular bases for several brain diseases, which open the opportunity to clarify the transnosological perspective assumed in brain diseases. It also showed the advantages of correlation-based biclustering analysis and accompanying function enrichment analysis for gene expression data in this type of investigation.

Cognitive and affective dysfunctions in autoimmune thyroiditis.

Hashimoto's thyroiditis (HT) is the most frequent cause of hypothyroidism in areas with sufficient iodine intake. While the impact of thyroid function on mood and cognition is well known, only in the recent years, an increasing number of studies report on the association of HT with cognitive and affective disturbances also in the euthyroid state. Recent imaging studies have shown that these impairments are accompanied by altered brain perfusion, in particular, in the frontal lobe and a reduced gray matter density in the left inferior gyrus frontalis. Brain function abnormalities in euthyroid patients with HT may be subtle and only detected by specific testing or even severe as it is the case in the rare neuropsychiatric disorder Hashimoto's encephalopathy (HE). The good response to glucocorticoids in patients with HE indicates an autoimmune origin. In line with this, the cognitive deficits and the high psycho-social burden in euthyroid HT patients without apparent signs of encephalopathy appear to be associated with anti-thyroid peroxidase auto-antibody (TPO Abs) levels. Though in vitro studies showing binding of TPO Abs to human cerebellar astrocytes point to a potential direct role of TPO Abs in the pathogenesis of brain abnormalities in HT patients, TPO Abs may function only as a marker of an autoimmune disorder of the central nervous system. In line with this, anti-central nervous system auto-antibodies (CNS Abs) which are markedly increased in patients with HT disturb myelinogenesis in vitro and, therefore, may impair myelin sheath integrity. In addition, in HT patients, production of monocyte- and T-lymphocyte-derived cytokines is also markedly increased which may negatively affect multiple neurotransmitters and, consequently, diverse brain neurocircuits.

Validity and reliability of a novel immunosuppressive adverse effects scoring system in renal transplant recipients.

BACKGROUND: After renal transplantation, many patients experience adverse effects from maintenance immunosuppressive drugs. When these adverse effects occur, patient adherence with immunosuppression may be reduced and impact allograft survival. If these adverse effects could be prospectively monitored in an objective manner and possibly prevented, adherence to immunosuppressive regimens could be optimized and allograft survival improved. Prospective, standardized clinical approaches to assess immunosuppressive adverse effects by health care providers are limited. Therefore, we developed and evaluated the application, reliability and validity of a novel adverse effects scoring system in renal transplant recipients receiving calcineurin inhibitor (cyclosporine or tacrolimus) and mycophenolic acid based immunosuppressive therapy. METHODS: The scoring system included 18 non-renal adverse effects organized into gastrointestinal, central nervous system and aesthetic domains developed by a multidisciplinary physician group. Nephrologists employed this standardized adverse effect evaluation in stable renal transplant patients using physical exam, review of systems, recent laboratory results, and medication adherence assessment during a clinic visit. Stable renal transplant recipients in two clinical studies were evaluated and received immunosuppressive regimens comprised of either cyclosporine or tacrolimus with mycophenolic acid. Face, content, and construct validity were assessed to document these adverse effect evaluations. Inter-rater reliability was determined using the Kappa statistic and intra-class correlation. RESULTS: A total of 58 renal transplant recipients were assessed using the adverse effects scoring system confirming face validity. Nephrologists (subject matter experts) rated the 18 adverse effects as: 3.1 ± 0.75 out of 4 (maximum) regarding clinical importance to verify content validity. The adverse effects scoring system distinguished 1.75-fold increased gastrointestinal adverse effects (p=0.008) in renal transplant recipients receiving tacrolimus and mycophenolic acid compared to the cyclosporine regimen. This finding demonstrated construct validity. Intra-class correlation was 0.81 (95% confidence interval: 0.65-0.90) and Kappa statistic of 0.68 ± 0.25 for all 18 adverse effects and verified substantial inter-rater reliability. CONCLUSIONS: This immunosuppressive adverse effects scoring system in stable renal transplant recipients was evaluated and substantiated face, content and construct validity with inter-rater reliability. The scoring system may facilitate prospective, standardized clinical monitoring of immunosuppressive adverse drug effects in stable renal transplant recipients and improve medication adherence.

Neurology is psychiatry--and vice versa.

This paper explores the relationship between neurology and psychiatry. It marshals evidence that disorders of the brain typically have neurological and psychological-cognitive, affective, behavioural-manifestations, while disorders of the psyche are based in the brain. Given the inseparability of neurological and psychiatric disorders, their disease classifications should eventually fuse, and joint initiatives in training, service and research should be strongly encouraged.

Mental Illness And Brain Disease.

It has become common to say psychiatric illnesses are brain diseases. This reflects a conception of the mental as being biologically based, though it is also thought that thinking of psychiatric illness this way will reduce the stigma attached to psychiatric illness. If psychiatric illnesses are brain diseases, however, it is not clear why psychiatry should not collapse into neurology, and some argue for this course. Others try to maintain a distinction by saying that neurology deals with abnormalities of neural structure while psychiatry deals with specific abnormalities of neural functioning. It is not clear that neurologists would accept this division, nor that they should. I argue that if we take seriously the notion that psychiatric illnesses are mental illnesses we can draw a more defensible boundary between psychiatry and neurology. As mental illnesses, psychiatric illnesses must have symptoms that affect our mental capacities and that the sufferer is capable of being aware of, even if they are not always self-consciously aware of them. Neurological illnesses, such as stroke or multiple sclerosis, may be diagnosed even if they are silent, just as the person may not be aware of having high blood pressure or may suffer a silent myocardial infarction. It does not make sense to speak of panic disorder if the person has never had a panic attack, however, or of bipolar disorder in the absence of mood swings. This does not mean psychiatric illnesses are not biologically based. Mental illnesses are illnesses of persons, whereas other illnesses are illnesses of biological individuals.

[Neurocognitive disorders in DSM-5: a critical review]. / Neurocognitieve stoornissen in de DSM-5: een kritische bespreking.

BACKGROUND: The APA published the DSM-5 in May, 2013. When compared to dsm-iv, the latest edition incorporates many changes, some relating to neurocognitive disorders. AIM: To review critically the new DSM-5 alterations and adjustments relating to neurocognitive disorders. METHOD: We compared the relevant chapters in DSM-IV-TR and DSM-5 and we searched the literature for articles involving discussions about cognitive disorders in DSM-5. RESULTS: With regard to differential diagnosis of neurocognitive disorders, DSM-5 has more in common with current clinical practice than does the DSM-IV. DSM-5 names ten etiological subtypes for which the diagnostic criteria are based on recent scientific research. However, some researchers and clinicians have reservations about using the term 'major neurocognitive disorder' instead of 'dementia', and are reluctant to make a distinction between 'mild' and 'major' cognitive disorders. CONCLUSION: The alterations and adjustments that appear in DSM-5 in relation to neurocognitive disorders may well mean progress for clinicians and researchers but they will inevitably require greater investment.