Schizophrenia Imaging Signatures and Their Associations With Cognition, Psychopathology, and Genetics in the General Population

The challenges of using machine learning models in psychiatric research and clinical practice

To understand the complex nature of heterogeneous psychiatric disorders, scientists and clinicians are required to employ a wide range of clinical, endophenotypic, neuroimaging, genomic, and environmental data to understand the biological mechanisms of psychiatric illness before this knowledge is applied into clinical setting. Machine learning (ML) is an automated process that can detect patterns from large multidimensional datasets and can supersede conventional statistical methods as it can detect both linear and non-linear relationships. Due to this advantage, ML has potential to enhance our understanding, improve diagnosis, prognosis and treatment of psychiatric disorders. The current review provides an in-depth examination of, and offers practical guidance for, the challenges encountered in the application of ML models in psychiatric research and clinical practice. These challenges include the curse of dimensionality, data quality, the 'black box' problem, hyperparameter tuning, external validation, class imbalance, and data representativeness. These challenges are particularly critical in the context of psychiatry as it is expected that researchers will encounter them during the stages of ML model development and deployment. We detail practical solutions and best practices to effectively mitigate the outlined challenges. These recommendations have the potential to improve reliability and interpretability of ML models in psychiatry.

Abnormal postcentral gyrus voxel-mirrored homotopic connectivity as a biomarker of mild cognitive impairment: A resting-state fMRI and support vector machine analysis

BACKGROUND

While patients affected by mild cognitive impairment (MCI) exhibit characteristic voxel-mirrored homotopic connectivity (VMHC) alterations, the ability of such VMHC abnormalities to predict the diagnosis of MCI in these patients remains uncertain. As such, this study was performed to evaluate the potential role of VMHC abnormalities in the diagnosis of MCI.

METHODS

MCI patients and healthy controls (HCs) were enrolled and subjected to resting-state functional magnetic resonance imaging (rs-fMRI) and neuropsychological testing. VMHC and support vector machine (SVM) techniques were then used to examine the collected imaging data.

RESULTS

Totally, 53 MCI patients and 68 healthy controls were recruited. Compared to HCs, MCI patients presented with an increase in postcentral gyrus VMHC. SVM classification demonstrated the ability of postcentral gyrus VMHC values to classify HCs and MCI patients with accuracy, sensitivity, and specificity values of 63.64 %, 71.69 %, and 89.71 %, respectively.

CONCLUSION

VMHC abnormalities in the postcentral gyrus may be mechanistically involved in the pathophysiological progression of MCI patients, and these abnormal VMHC patterns may also offer utility as a neuroimaging biomarker for MCI patient diagnosis.

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning

Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes with different brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal magnetic resonance imaging to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, and multiple sclerosis, as well as their potential in a transdiagnostic framework, where neuroanatomical and neurobiological commonalities were assessed across diagnostic boundaries. Subsequently, we summarize relevant machine learning methodologies and their clinical interpretability. We discuss the potential clinical implications of the current findings and envision future research avenues. Finally, we discuss an emerging paradigm called dimensional neuroimaging endophenotypes. Dimensional neuroimaging endophenotypes dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into low-dimensional yet informative, quantitative brain phenotypic representations, serving as robust intermediate phenotypes (i.e., endophenotypes), presumably reflecting the interplay of underlying genetic, lifestyle, and environmental processes associated with disease etiology.

The genetic architecture of biological age in nine human organ systems

Investigating the genetic underpinnings of human aging is essential for unraveling the etiology of and developing actionable therapies for chronic diseases. Here, we characterize the genetic architecture of the biological age gap (BAG; the difference between machine learning-predicted age and chronological age) across nine human organ systems in 377,028 participants of European ancestry from the UK Biobank. The BAGs were computed using cross-validated support vector machines, incorporating imaging, physical traits and physiological measures. We identify 393 genomic loci-BAG pairs (P < 5 × 10-8) linked to the brain, eye, cardiovascular, hepatic, immune, metabolic, musculoskeletal, pulmonary and renal systems. Genetic variants associated with the nine BAGs are predominantly specific to the respective organ system (organ specificity) while exerting pleiotropic links with other organ systems (interorgan cross-talk). We find that genetic correlation between the nine BAGs mirrors their phenotypic correlation. Further, a multiorgan causal network established from two-sample Mendelian randomization and latent causal variance models revealed potential causality between chronic diseases (for example, Alzheimer's disease and diabetes), modifiable lifestyle factors (for example, sleep duration and body weight) and multiple BAGs. Our results illustrate the potential for improving human organ health via a multiorgan network, including lifestyle interventions and drug repurposing strategies.

Brain Network Localization of Gray Matter Atrophy and Neurocognitive and Social Cognitive Dysfunction in Schizophrenia

BACKGROUND

Numerous studies have established the presence of gray matter atrophy and brain activation abnormalities during neurocognitive and social cognitive tasks in schizophrenia. Despite a growing consensus that diseases localize better to distributed brain networks than individual anatomical regions, relatively few studies have examined brain network localization of gray matter atrophy and neurocognitive and social cognitive dysfunction in schizophrenia.

METHODS

To address this gap, we initially identified brain locations of structural and functional abnormalities in schizophrenia from 301 published neuroimaging studies with 8712 individuals with schizophrenia and 9275 healthy control participants. By applying novel functional connectivity network mapping to large-scale resting-state functional magnetic resonance imaging datasets, we mapped these affected brain locations to 3 brain abnormality networks of schizophrenia.

RESULTS

The gray matter atrophy network of schizophrenia comprised a broadly distributed set of brain areas predominantly implicating the ventral attention, somatomotor, and default networks. The neurocognitive dysfunction network was also composed of widespread brain areas primarily involving the frontoparietal and default networks. By contrast, the social cognitive dysfunction network consisted of circumscribed brain regions mainly implicating the default, subcortical, and visual networks.

CONCLUSIONS

Our findings suggest shared and unique brain network substrates of gray matter atrophy and neurocognitive and social cognitive dysfunction in schizophrenia, which may not only refine the understanding of disease neuropathology from a network perspective but may also contribute to more targeted and effective treatments for impairments in different cognitive domains in schizophrenia.

Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm

Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal 'trajectory' of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.

The Genetic Architecture of Biological Age in Nine Human Organ Systems

Understanding the genetic basis of biological aging in multi-organ systems is vital for elucidating age-related disease mechanisms and identifying therapeutic interventions. This study characterized the genetic architecture of the biological age gap (BAG) across nine human organ systems in 377,028 individuals of European ancestry from the UK Biobank. We discovered 393 genomic loci-BAG pairs (P-value<5×10-8) linked to the brain, eye, cardiovascular, hepatic, immune, metabolic, musculoskeletal, pulmonary, and renal systems. We observed BAG-organ specificity and inter-organ connections. Genetic variants associated with the nine BAGs are predominantly specific to the respective organ system while exerting pleiotropic effects on traits linked to multiple organ systems. A gene-drug-disease network confirmed the involvement of the metabolic BAG-associated genes in drugs targeting various metabolic disorders. Genetic correlation analyses supported Cheverud's Conjecture1 - the genetic correlation between BAGs mirrors their phenotypic correlation. A causal network revealed potential causal effects linking chronic diseases (e.g., Alzheimer's disease), body weight, and sleep duration to the BAG of multiple organ systems. Our findings shed light on promising therapeutic interventions to enhance human organ health within a complex multi-organ network, including lifestyle modifications and potential drug repositioning strategies for treating chronic diseases. All results are publicly available at https://labs-laboratory.com/medicine.

Neurobiology-based Cognitive Biotypes Using Multi-scale Intrinsic Connectivity Networks in Psychotic Disorders

Objective

Understanding the neurobiology of cognitive dysfunction in psychotic disorders remains elusive, as does developing effective interventions. Limited knowledge about the biological heterogeneity of cognitive dysfunction hinders progress. This study aimed to identify subgroups of patients with psychosis with distinct patterns of functional brain alterations related to cognition (cognitive biotypes).

Methods

B-SNIP consortium data (2,270 participants including participants with psychotic disorders, relatives, and controls) was analyzed. Researchers used reference-informed independent component analysis and the NeuroMark 100k multi-scale intrinsic connectivity networks (ICN) template to obtain subject-specific ICNs and whole-brain functional network connectivity (FNC). FNC features associated with cognitive performance were identified through multivariate joint analysis. K-means clustering identified subgroups of patients based on these features in a discovery set. Subgroups were further evaluated in a replication set and in relatives.

Results

Two biotypes with different functional brain alteration patterns were identified. Biotype 1 exhibited brain-wide alterations, involving hypoconnectivity in cerebellar-subcortical and somatomotor-visual networks and worse cognitive performance. Biotype 2 exhibited hyperconnectivity in somatomotor-subcortical networks and hypoconnectivity in somatomotor-high cognitive processing networks, and better preserved cognitive performance. Demographic, clinical, cognitive, and FNC characteristics of biotypes were consistent in discovery and replication sets, and in relatives. 70.12% of relatives belonged to the same biotype as their affected family members.

Conclusions

These findings suggest two distinctive psychosis-related cognitive biotypes with differing functional brain patterns shared with their relatives. Patient stratification based on these biotypes instead of traditional diagnosis may help to optimize future research and clinical trials addressing cognitive dysfunction in psychotic disorders.

Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions

Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning

Machine learning has been increasingly used to obtain individualized neuroimaging signatures for disease diagnosis, prognosis, and response to treatment in neuropsychiatric and neurodegenerative disorders. Therefore, it has contributed to a better understanding of disease heterogeneity by identifying disease subtypes that present significant differences in various brain phenotypic measures. In this review, we first present a systematic literature overview of studies using machine learning and multimodal MRI to unravel disease heterogeneity in various neuropsychiatric and neurodegenerative disorders, including Alzheimer's disease, schizophrenia, major depressive disorder, autism spectrum disorder, multiple sclerosis, as well as their potential in transdiagnostic settings. Subsequently, we summarize relevant machine learning methodologies and discuss an emerging paradigm which we call dimensional neuroimaging endophenotype (DNE). DNE dissects the neurobiological heterogeneity of neuropsychiatric and neurodegenerative disorders into a low-dimensional yet informative, quantitative brain phenotypic representation, serving as a robust intermediate phenotype (i.e., endophenotype) largely reflecting underlying genetics and etiology. Finally, we discuss the potential clinical implications of the current findings and envision future research avenues.

Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo

Major depressive disorder (MDD) is a heterogeneous clinical syndrome with widespread subtle neuroanatomical correlates. Our objective was to identify the neuroanatomical dimensions that characterize MDD and predict treatment response to selective serotonin reuptake inhibitor (SSRI) antidepressants or placebo. In the COORDINATE-MDD consortium, raw MRI data were shared from international samples (N = 1,384) of medication-free individuals with first-episode and recurrent MDD (N = 685) in a current depressive episode of at least moderate severity, but not treatment-resistant depression, as well as healthy controls (N = 699). Prospective longitudinal data on treatment response were available for a subset of MDD individuals (N = 359). Treatments were either SSRI antidepressant medication (escitalopram, citalopram, sertraline) or placebo. Multi-center MRI data were harmonized, and HYDRA, a semi-supervised machine-learning clustering algorithm, was utilized to identify patterns in regional brain volumes that are associated with disease. MDD was optimally characterized by two neuroanatomical dimensions that exhibited distinct treatment responses to placebo and SSRI antidepressant medications. Dimension 1 was characterized by preserved gray and white matter (N = 290 MDD), whereas Dimension 2 was characterized by widespread subtle reductions in gray and white matter (N = 395 MDD) relative to healthy controls. Although there were no significant differences in age of onset, years of illness, number of episodes, or duration of current episode between dimensions, there was a significant interaction effect between dimensions and treatment response. Dimension 1 showed a significant improvement in depressive symptoms following treatment with SSRI medication (51.1%) but limited changes following placebo (28.6%). By contrast, Dimension 2 showed comparable improvements to either SSRI (46.9%) or placebo (42.2%) (β = -18.3, 95% CI (-34.3 to -2.3), P = 0.03). Findings from this case-control study indicate that neuroimaging-based markers can help identify the disease-based dimensions that constitute MDD and predict treatment response.

Predicting cognitive dysfunction and regional hubs using Braak staging amyloid-beta biomarkers and machine learning

Mild cognitive impairment (MCI) is a transitional stage between normal aging and early Alzheimer's disease (AD). The presence of extracellular amyloid-beta (Aβ) in Braak regions suggests a connection with cognitive dysfunction in MCI/AD. Investigating the multivariate predictive relationships between regional Aβ biomarkers and cognitive function can aid in the early detection and prevention of AD. We introduced machine learning approaches to estimate cognitive dysfunction from regional Aβ biomarkers and identify the Aβ-related dominant brain regions involved with cognitive impairment. We employed Aβ biomarkers and cognitive measurements from the same individuals to train support vector regression (SVR) and artificial neural network (ANN) models and predict cognitive performance solely based on Aβ biomarkers on the test set. To identify Aβ-related dominant brain regions involved in cognitive prediction, we built the local interpretable model-agnostic explanations (LIME) model. We found elevated Aβ in MCI compared to controls and a stronger correlation between Aβ and cognition, particularly in Braak stages III-IV and V-VII (p < 0.05) biomarkers. Both SVR and ANN, especially ANN, showed strong predictive relationships between regional Aβ biomarkers and cognitive impairment (p < 0.05). LIME integrated with ANN showed that the parahippocampal gyrus, inferior temporal gyrus, and hippocampus were the most decisive Braak regions for predicting cognitive decline. Consistent with previous findings, this new approach suggests relationships between Aβ biomarkers and cognitive impairment. The proposed analytical framework can estimate cognitive impairment from Braak staging Aβ biomarkers and delineate the dominant brain regions collectively involved in AD pathophysiology.

Antipsychotics and structural brain changes: could treatment adherence explain the discrepant findings?

Progressive structural brain changes are well documented in schizophrenia and have been linked to both illness progression and the extent of antipsychotic treatment exposure. Literature reporting longitudinal changes in brain structure in individuals with schizophrenia is selectively reviewed to assess the roles of illness, antipsychotic treatment, adherence and other factors in the genesis of these changes. This narrative review considers literature investigating longitudinal changes in brain structure in individuals with schizophrenia. The review focusses on structural changes in the cortex, basal ganglia and white matter. It also examines effects of medication non-adherence and relapse on the clinical course of the illness and on structural brain changes. Studies investigating structural magnetic resonance imaging changes in patients treated with long-acting injectable antipsychotics are reviewed. Temporal changes in brain structure in schizophrenia can be divided into those that are associated with antipsychotic treatment and those that are not. Changes associated with treatment include increases in basal ganglia and white matter volumes. Relapse episodes may be a critical factor in illness progression and brain volume reductions. Medication adherence may be an important factor that could explain the findings that brain volume reductions are associated with poor treatment response, higher intensity of antipsychotic treatment exposure and more time spent in relapse. Improved adherence via long-acting injectable antipsychotics and adherence focussed psychosocial interventions could maximize protective effects of antipsychotics against illness progression.

Gene Interactions in Human Disease Studies-Evidence Is Mounting

Despite monumental advances in molecular technology to generate genome sequence data at scale, there is still a considerable proportion of heritability in most complex diseases that remains unexplained. Because many of the discoveries have been single-nucleotide variants with small to moderate effects on disease, the functional implication of many of the variants is still unknown and, thus, we have limited new drug targets and therapeutics. We, and many others, posit that one primary factor that has limited our ability to identify novel drug targets from genome-wide association studies may be due to gene interactions (epistasis), gene-environment interactions, network/pathway effects, or multiomic relationships. We propose that many of these complex models explain much of the underlying genetic architecture of complex disease. In this review, we discuss the evidence from multiple research avenues, ranging from pairs of alleles to multiomic integration studies and pharmacogenomics, that supports the need for further investigation of gene interactions (or epistasis) in genetic and genomic studies of human disease. Our goal is to catalog the mounting evidence for epistasis in genetic studies and the connections between genetic interactions and human health and disease that could enable precision medicine of the future.

Two Neuroanatomical Signatures in Schizophrenia: Expression Strengths Over the First 2 Years of Treatment and Their Relationships to Neurodevelopmental Compromise and Antipsychotic Treatment

BACKGROUND AND HYPOTHESIS

Two machine learning derived neuroanatomical signatures were recently described. Signature 1 is associated with widespread grey matter volume reductions and signature 2 with larger basal ganglia and internal capsule volumes. We hypothesized that they represent the neurodevelopmental and treatment-responsive components of schizophrenia respectively.

STUDY DESIGN

We assessed the expression strength trajectories of these signatures and evaluated their relationships with indicators of neurodevelopmental compromise and with antipsychotic treatment effects in 83 previously minimally treated individuals with a first episode of a schizophrenia spectrum disorder who received standardized treatment and underwent comprehensive clinical, cognitive and neuroimaging assessments over 24 months. Ninety-six matched healthy case-controls were included.

STUDY RESULTS

Linear mixed effect repeated measures models indicated that the patients had stronger expression of signature 1 than controls that remained stable over time and was not related to treatment. Stronger signature 1 expression showed trend associations with lower educational attainment, poorer sensory integration, and worse cognitive performance for working memory, verbal learning and reasoning and problem solving. The most striking finding was that signature 2 expression was similar for patients and controls at baseline but increased significantly with treatment in the patients. Greater increase in signature 2 expression was associated with larger reductions in PANSS total score and increases in BMI and not associated with neurodevelopmental indices.

CONCLUSIONS

These findings provide supporting evidence for two distinct neuroanatomical signatures representing the neurodevelopmental and treatment-responsive components of schizophrenia.

Psychosis brain subtypes validated in first-episode cohorts and related to illness remission: results from the PHENOM consortium

Using machine learning, we recently decomposed the neuroanatomical heterogeneity of established schizophrenia to discover two volumetric subgroups-a 'lower brain volume' subgroup (SG1) and an 'higher striatal volume' subgroup (SG2) with otherwise normal brain structure. In this study, we investigated whether the MRI signatures of these subgroups were also already present at the time of the first-episode of psychosis (FEP) and whether they were related to clinical presentation and clinical remission over 1-, 3-, and 5-years. We included 572 FEP and 424 healthy controls (HC) from 4 sites (Sao Paulo, Santander, London, Melbourne) of the PHENOM consortium. Our prior MRI subgrouping models (671 participants; USA, Germany, and China) were applied to both FEP and HC. Participants were assigned into 1 of 4 categories: subgroup 1 (SG1), subgroup 2 (SG2), no subgroup membership ('None'), and mixed SG1 + SG2 subgroups ('Mixed'). Voxel-wise analyses characterized SG1 and SG2 subgroups. Supervised machine learning analyses characterized baseline and remission signatures related to SG1 and SG2 membership. The two dominant patterns of 'lower brain volume' in SG1 and 'higher striatal volume' (with otherwise normal neuromorphology) in SG2 were identified already at the first episode of psychosis. SG1 had a significantly higher proportion of FEP (32%) vs. HC (19%) than SG2 (FEP, 21%; HC, 23%). Clinical multivariate signatures separated the SG1 and SG2 subgroups (balanced accuracy = 64%; p < 0.0001), with SG2 showing higher education but also greater positive psychosis symptoms at first presentation, and an association with symptom remission at 1-year, 5-year, and when timepoints were combined. Neuromorphological subtypes of schizophrenia are already evident at illness onset, separated by distinct clinical presentations, and differentially associated with subsequent remission. These results suggest that the subgroups may be underlying risk phenotypes that could be targeted in future treatment trials and are critical to consider when interpreting neuroimaging literature.

Cognitive impairment in schizophrenia: aetiology, pathophysiology, and treatment

Cognitive deficits are a core feature of schizophrenia, account for much of the impaired functioning associated with the disorder and are not responsive to existing treatments. In this review, we first describe the clinical presentation and natural history of these deficits. We then consider aetiological factors, highlighting how a range of similar genetic and environmental factors are associated with both cognitive function and schizophrenia. We then review the pathophysiological mechanisms thought to underlie cognitive symptoms, including the role of dopamine, cholinergic signalling and the balance between GABAergic interneurons and glutamatergic pyramidal cells. Finally, we review the clinical management of cognitive impairments and candidate novel treatments.

Schizophrenie: Neuroanatomische Anomalien auch in Normalbevölkerung zu finden

Translationale Neurowissenschaften haben das Ziel, Mechanismen neurologischer und psychiatrischer Erkrankungen zu identifizieren, um dieses Wissen in therapeutische Maßnahmen zu übersetzen. In vielen Domänen gibt es dabei noch viel zu lernen. Die folgende Studie unternimmt den Versuch, anhand eines großen Datensatzes Unterschiede zwischen schizophren erkrankten Personen und Nicht-Erkrankten zu identifizieren.

Better characterization of attention and hyperactivity/impulsivity in children with ADHD: The key to understanding the underlying white matter microstructure

The apparent increase in the prevalence of the attention deficit hyperactivity disorder (ADHD) diagnosis raises many questions regarding the variability of the subjective diagnostic method. This comprehensive review reports findings in studies assessing white matter (WM) bundles in diffusion MRI and symptom severity in children with ADHD. These studies suggested the involvement of the connections between the frontal, parietal, and basal ganglia regions. This review discusses the limitations surrounding diffusion tensor imaging (DTI) and suggests novel imaging techniques allowing for a more reliable representation of the underlying biology. We propose a more inclusive approach to studying ADHD that includes known endophenotypes within the ADHD diagnosis. Aligned with the Research Domain Criteria Initiative, we also propose to investigate attentional capabilities and impulsive behaviours outside of the borders of the diagnosis. We support the existing hypothesis that ADHD originates from a developmental error and propose that it could lead to an accumulation in time of abnormalities in WM microstructure and pathways. Finally, state-of-the-art diffusion processing and novel artificial intelligence approaches would be beneficial to fully understand the pathophysiology of ADHD.

Abnormal regional homogeneity in right caudate as a potential neuroimaging biomarker for mild cognitive impairment: A resting-state fMRI study and support vector machine analysis

Objective

Mild cognitive impairment (MCI) is a heterogeneous syndrome characterized by cognitive impairment on neurocognitive tests but accompanied by relatively intact daily activities. Due to high variation and no objective methods for diagnosing and treating MCI, guidance on neuroimaging is needed. The study has explored the neuroimaging biomarkers using the support vector machine (SVM) method to predict MCI.

Methods

In total, 53 patients with MCI and 68 healthy controls were involved in scanning resting-state functional magnetic resonance imaging (rs-fMRI). Neurocognitive testing and Structured Clinical Interview, such as Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) test, Activity of Daily Living (ADL) Scale, Hachinski Ischemic Score (HIS), Clinical Dementia Rating (CDR), Montreal Cognitive Assessment (MoCA), and Hamilton Rating Scale for Depression (HRSD), were utilized to assess participants' cognitive state. Neuroimaging data were analyzed with the regional homogeneity (ReHo) and SVM methods.

Results

Compared with healthy comparisons (HCs), ReHo of patients with MCI was decreased in the right caudate. In addition, the SVM classification achieved an overall accuracy of 68.6%, sensitivity of 62.26%, and specificity of 58.82%.

Conclusion

The results suggest that abnormal neural activity in the right cerebrum may play a vital role in the pathophysiological process of MCI. Moreover, the ReHo in the right caudate may serve as a neuroimaging biomarker for MCI, which can provide objective guidance on diagnosing and managing MCI in the future.