Defining and predicting transdiagnostic categories of neurodegenerative disease

Beyond the usual suspects: multi-factorial computational models in the search for neurodegenerative disease mechanisms

From Alzheimer's disease to amyotrophic lateral sclerosis, the molecular cascades underlying neurodegenerative disorders remain poorly understood. The clinical view of neurodegeneration is confounded by symptomatic heterogeneity and mixed pathology in almost every patient. While the underlying physiological alterations originate, proliferate, and propagate potentially decades before symptomatic onset, the complexity and inaccessibility of the living brain limit direct observation over a patient's lifespan. Consequently, there is a critical need for robust computational methods to support the search for causal mechanisms of neurodegeneration by distinguishing pathogenic processes from consequential alterations, and inter-individual variability from intra-individual progression. Recently, promising advances have been made by data-driven spatiotemporal modeling of the brain, based on in vivo neuroimaging and biospecimen markers. These methods include disease progression models comparing the temporal evolution of various biomarkers, causal models linking interacting biological processes, network propagation models reproducing the spatial spreading of pathology, and biophysical models spanning cellular- to network-scale phenomena. In this review, we discuss various computational approaches for integrating cross-sectional, longitudinal, and multi-modal data, primarily from large observational neuroimaging studies, to understand (i) the temporal ordering of physiological alterations, i(i) their spatial relationships to the brain's molecular and cellular architecture, (iii) mechanistic interactions between biological processes, and (iv) the macroscopic effects of microscopic factors. We consider the extents to which computational models can evaluate mechanistic hypotheses, explore applications such as improving treatment selection, and discuss how model-informed insights can lay the groundwork for a pathobiological redefinition of neurodegenerative disorders.

Cryo-EM structures of pathogenic fibrils and their impact on neurodegenerative disease research

Neurodegenerative diseases are commonly associated with the formation of aberrant protein aggregates within the brain, and ultrastructural analyses have revealed that the proteins within these inclusions often assemble into amyloid filaments. Cryoelectron microscopy (cryo-EM) has emerged as an effective method for determining the near-atomic structure of these disease-associated filamentous proteins, and the resulting structures have revolutionized the way we think about aberrant protein aggregation and propagation during disease progression. These structures have also revealed that individual fibril conformations may dictate different disease conditions, and this newfound knowledge has improved disease modeling in the lab and advanced the ongoing pursuit of clinical tools capable of distinguishing and targeting different pathogenic entities within living patients. In this review, we summarize some of the recently developed cryo-EM structures of ex vivo α-synuclein, tau, β-amyloid (Aβ), TAR DNA-binding protein 43 (TDP-43), and transmembrane protein 106B (TMEM106B) fibrils and discuss how these structures are being leveraged toward mechanistic research and therapeutic development.

N-Salicyloyl Tryptamine Derivatives as Potent Neuroinflammation Inhibitors by Constraining Microglia Activation via a STAT3 Pathway

Neuroinflammation is an important factor that exacerbates neuronal death and abnormal synaptic function in neurodegenerative diseases (NDDs). Due to the complex pathogenesis and the presence of blood-brain barrier (BBB), no effective clinical drugs are currently available. Previous results showed that N-salicyloyl tryptamine derivatives had the potential to constrain the neuroinflammatory process. In this study, 30 new N-salicyloyl tryptamine derivatives were designed and synthesized to investigate a structure-activity relationship (SAR) for the indole ring of tryptamine in order to enhance their antineuroinflammatory effects. Among them, both in vitro and in vivo compound 18 exerted the best antineuroinflammatory effects by suppressing the activation of microglia, which is the culprit of neuroinflammation. The underlying mechanism of its antineuroinflammatory effect may be related to the inhibition of transcription, expression and phosphorylation of signal transducer and activator of transcription 3 (STAT3) that subsequently regulated downstream cyclooxygenase-2 (COX-2) expression and activity. With its excellent BBB permeability and pharmacokinetic properties, compound 18 exhibited significant neuroprotective effects in the hippocampal region of lipopolysaccharides (LPS)-induced mice than former N-salicyloyl tryptamine derivative L7. In conclusion, compound 18 has provided a new approach for the development of highly effective antineuroinflammatory therapeutic drugs targeting microglia activation.

The neural substrates of transdiagnostic cognitive-linguistic heterogeneity in primary progressive aphasia

BACKGROUND

Clinical variants of primary progressive aphasia (PPA) are diagnosed based on characteristic patterns of language deficits, supported by corresponding neural changes on brain imaging. However, there is (i) considerable phenotypic variability within and between each diagnostic category with partially overlapping profiles of language performance between variants and (ii) accompanying non-linguistic cognitive impairments that may be independent of aphasia magnitude and disease severity. The neurobiological basis of this cognitive-linguistic heterogeneity remains unclear. Understanding the relationship between these variables would improve PPA clinical/research characterisation and strengthen clinical trial and symptomatic treatment design. We address these knowledge gaps using a data-driven transdiagnostic approach to chart cognitive-linguistic differences and their associations with grey/white matter degeneration across multiple PPA variants.

METHODS

Forty-seven patients (13 semantic, 15 non-fluent, and 19 logopenic variant PPA) underwent assessment of general cognition, errors on language performance, and structural and diffusion magnetic resonance imaging to index whole-brain grey and white matter changes. Behavioural data were entered into varimax-rotated principal component analyses to derive orthogonal dimensions explaining the majority of cognitive variance. To uncover neural correlates of cognitive heterogeneity, derived components were used as covariates in neuroimaging analyses of grey matter (voxel-based morphometry) and white matter (network-based statistics of structural connectomes).

RESULTS

Four behavioural components emerged: general cognition, semantic memory, working memory, and motor speech/phonology. Performance patterns on the latter three principal components were in keeping with each variant's characteristic profile, but with a spectrum rather than categorical distribution across the cohort. General cognitive changes were most marked in logopenic variant PPA. Regardless of clinical diagnosis, general cognitive impairment was associated with inferior/posterior parietal grey/white matter involvement, semantic memory deficits with bilateral anterior temporal grey/white matter changes, working memory impairment with temporoparietal and frontostriatal grey/white matter involvement, and motor speech/phonology deficits with inferior/middle frontal grey matter alterations.

CONCLUSIONS

Cognitive-linguistic heterogeneity in PPA closely relates to individual-level variations on multiple behavioural dimensions and grey/white matter degeneration of regions within and beyond the language network. We further show that employment of transdiagnostic approaches may help to understand clinical symptom boundaries and reveal clinical and neural profiles that are shared across categorically defined variants of PPA.

Artificial intelligence for dementia genetics and omics

Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine. HIGHLIGHTS: We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research.

The Role of Atypical Chemokine Receptors in Neuroinflammation and Neurodegenerative Disorders

Neuroinflammation is associated with several neurodegenerative disorders, including Alzheimer's disease (AD), Parkinson's disease (PD), and multiple sclerosis (MS). Neuroinflammation provides protection in acute situations but results in significant damage to the nervous system if chronic. Overexpression of chemokines within the brain results in the recruitment and activation of glial and peripheral immune cells which can propagate a cascading inflammatory response, resulting in neurodegeneration and the onset of neurodegenerative disorders. Recent work has identified the role of atypical chemokine receptors (ACKRs) in neurodegenerative conditions. ACKRs are seven-transmembrane domain receptors that do not follow canonical G protein signaling, but regulate inflammatory responses by modulating chemokine abundance, location, and availability. This review summarizes what is known about the four ACKRs and three putative ACKRs within the brain, highlighting their known expression and discussing the current understanding of each ACKR in the context of neurodegeneration. The ability of ACKRs to alter levels of chemokines makes them an appealing therapeutic target for neurodegenerative conditions. However, further work is necessary to understand the expression of several ACKRs within the neuroimmune system and the effectiveness of targeted drug therapies in the prevention and treatment of neurodegenerative conditions.

Shared genetic risk loci between Alzheimer's disease and related dementias, Parkinson's disease, and amyotrophic lateral sclerosis

BACKGROUND

Genome-wide association studies (GWAS) have indicated moderate genetic overlap between Alzheimer's disease (AD) and related dementias (ADRD), Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS), neurodegenerative disorders traditionally considered etiologically distinct. However, the specific genetic variants and loci underlying this overlap remain almost entirely unknown.

METHODS

We leveraged state-of-the-art GWAS for ADRD, PD, and ALS. For each pair of disorders, we examined each of the GWAS hits for one disorder and tested whether they were also significant for the other disorder, applying Bonferroni correction for the number of variants tested. This approach rigorously controls the family-wise error rate for both disorders, analogously to genome-wide significance.

RESULTS

Eleven loci with GWAS hits for one disorder were also associated with one or both of the other disorders: one with all three disorders (the MAPT/KANSL1 locus), five with ADRD and PD (near LCORL, CLU, SETD1A/KAT8, WWOX, and GRN), three with ADRD and ALS (near GPX3, HS3ST5/HDAC2/MARCKS, and TSPOAP1), and two with PD and ALS (near GAK/TMEM175 and NEK1). Two of these loci (LCORL and NEK1) were associated with an increased risk of one disorder but decreased risk of another. Colocalization analysis supported a shared causal variant between ADRD and PD at the CLU, WWOX, and LCORL loci, between ADRD and ALS at the TSPOAP1 locus, and between PD and ALS at the NEK1 and GAK/TMEM175 loci. To address the concern that ADRD is an imperfect proxy for AD and that the ADRD and PD GWAS have overlapping participants (nearly all of which are from the UK Biobank), we confirmed that all our ADRD associations had nearly identical odds ratios in an AD GWAS that excluded the UK Biobank, and all but one remained nominally significant (p < 0.05) for AD.

CONCLUSIONS

In one of the most comprehensive investigations to date of pleiotropy between neurodegenerative disorders, we identify eleven genetic risk loci shared among ADRD, PD, and ALS. These loci support lysosomal/autophagic dysfunction (GAK/TMEM175, GRN, KANSL1), neuroinflammation/immunity (TSPOAP1), oxidative stress (GPX3, KANSL1), and the DNA damage response (NEK1) as transdiagnostic processes underlying multiple neurodegenerative disorders.

A scoping review of neurodegenerative manifestations in explainable digital phenotyping

Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson's and Alzheimer's disease, as single entities, but rather as a spectrum of multifaceted symptoms with heterogeneous progression courses and treatment responses. The definition of the naturalistic behavioral repertoire of early neurodegenerative manifestations is still elusive, impeding early diagnosis and intervention. Central to this view is the role of artificial intelligence (AI) in reinforcing the depth of phenotypic information, thereby supporting the paradigm shift to precision medicine and personalized healthcare. This suggestion advocates the definition of disease subtypes in a new biomarker-supported nosology framework, yet without empirical consensus on standardization, reliability and interpretability. Although the well-defined neurodegenerative processes, linked to a triad of motor and non-motor preclinical symptoms, are detected by clinical intuition, we undertake an unbiased data-driven approach to identify different patterns of neuropathology distribution based on the naturalistic behavior data inherent to populations in-the-wild. We appraise the role of remote technologies in the definition of digital phenotyping specific to brain-, body- and social-level neurodegenerative subtle symptoms, emphasizing inter- and intra-patient variability powered by deep learning. As such, the present review endeavors to exploit digital technologies and AI to create disease-specific phenotypic explanations, facilitating the understanding of neurodegenerative diseases as "bio-psycho-social" conditions. Not only does this translational effort within explainable digital phenotyping foster the understanding of disease-induced traits, but it also enhances diagnostic and, eventually, treatment personalization.

Recent insights into the roles of circular RNAs in human brain development and neurologic diseases

Circular RNAs (circRNAs) are a novel class of non-coding RNAs. They are single-stranded RNA transcripts characterized with a closed loop structure making them resistant to degrading enzymes. Recently, circRNAs have been suggested with regulatory roles in gene expression involved in controlling various biological processes. Notably, they have demonstrated abundance, dynamic expression, back-splicing events, and spatiotemporally regulation in the human brain. Accordingly, they are expected to be involved in brain functions and related diseases. Studies in animals and human brain have revealed differential expression of circRNAs in brain compartments. Interestingly, contributing roles of circRNAs in the regulation of central nervous system (CNS) development have been demonstrated in a number of studies. It has been proposed that circRNAs play role in substantial neurological functions like neurotransmitter-associated tasks, neural cells maturation, and functions of synapses. Furthermore, 3 main pathways have been identified in association with circRNAs's host genes including axon guidance, Wnt signaling, and transforming growth factor beta (TGF-β) signaling pathways, which are known to be involved in substantial functions like migration and differentiation of neurons and specification of axons, and thus play role in brain development. In this review, we have an overview to the biogenesis, biological functions of circRNAs, and particularly their roles in human brain development and the pathogenesis of neurodegenerative diseases including Alzheimer's diseases, multiple sclerosis, Parkinson's disease and brain tumors.

Mapping behavioural, cognitive and affective transdiagnostic dimensions in frontotemporal dementia

Two common clinical variants of frontotemporal dementia are the behavioural variant frontotemporal dementia, presenting with behavioural and personality changes attributable to prefrontal atrophy, and semantic dementia, displaying early semantic dysfunction primarily due to anterior temporal degeneration. Despite representing independent diagnostic entities, mounting evidence indicates overlapping cognitive-behavioural profiles in these syndromes, particularly with disease progression. Why such overlap occurs remains unclear. Understanding the nature of this overlap, however, is essential to improve early diagnosis, characterization and management of those affected. Here, we explored common cognitive-behavioural and neural mechanisms contributing to heterogeneous frontotemporal dementia presentations, irrespective of clinical diagnosis. This transdiagnostic approach allowed us to ascertain whether symptoms not currently considered core to these two syndromes are present in a significant proportion of cases and to explore the neural basis of clinical heterogeneity. Sixty-two frontotemporal dementia patients (31 behavioural variant frontotemporal dementia and 31 semantic dementia) underwent comprehensive neuropsychological, behavioural and structural neuroimaging assessments. Orthogonally rotated principal component analysis of neuropsychological and behavioural data uncovered eight statistically independent factors explaining the majority of cognitive-behavioural performance variation in behavioural variant frontotemporal dementia and semantic dementia. These factors included Behavioural changes, Semantic dysfunction, General Cognition, Executive function, Initiation, Disinhibition, Visuospatial function and Affective changes. Marked individual-level overlap between behavioural variant frontotemporal dementia and semantic dementia was evident on the Behavioural changes, General Cognition, Initiation, Disinhibition and Affective changes factors. Compared to behavioural variant frontotemporal dementia, semantic dementia patients displayed disproportionate impairment on the Semantic dysfunction factor, whereas greater impairment on Executive and Visuospatial function factors was noted in behavioural variant frontotemporal dementia. Both patient groups showed comparable magnitude of atrophy to frontal regions, whereas severe temporal lobe atrophy was characteristic of semantic dementia. Whole-brain voxel-based morphometry correlations with emergent factors revealed associations between fronto-insular and striatal grey matter changes with Behavioural, Executive and Initiation factor performance, bilateral temporal atrophy with Semantic dysfunction factor scores, parietal-subcortical regions with General Cognitive performance and ventral temporal atrophy associated with Visuospatial factor scores. Together, these findings indicate that cognitive-behavioural overlap (i) occurs systematically in frontotemporal dementia; (ii) varies in a graded manner between individuals and (iii) is associated with degeneration of different neural systems. Our findings suggest that phenotypic heterogeneity in frontotemporal dementia syndromes can be captured along continuous, multidimensional spectra of cognitive-behavioural changes. This has implications for the diagnosis of both syndromes amidst overlapping features as well as the design of symptomatic treatments applicable to multiple syndromes.

CRISPR and iPSCs: Recent Developments and Future Perspectives in Neurodegenerative Disease Modelling, Research, and Therapeutics

Neurodegenerative diseases are prominent causes of pain, suffering, and death worldwide. Traditional approaches modelling neurodegenerative diseases are deficient, and therefore, improved strategies that effectively recapitulate the pathophysiological conditions of neurodegenerative diseases are the need of the hour. The generation of human-induced pluripotent stem cells (iPSCs) has transformed our ability to model neurodegenerative diseases in vitro and provide an unlimited source of cells (including desired neuronal cell types) for cell replacement therapy. Recently, CRISPR/Cas9-based genome editing has also been gaining popularity because of the flexibility they provide to generate and ablate disease phenotypes. In addition, the recent advancements in CRISPR/Cas9 technology enables researchers to seamlessly target and introduce precise modifications in the genomic DNA of different human cell lines, including iPSCs. CRISPR-iPSC-based disease modelling, therefore, allows scientists to recapitulate the pathological aspects of most neurodegenerative processes and investigate the role of pathological gene variants in healthy non-patient cell lines. This review outlines how iPSCs, CRISPR/Cas9, and CRISPR-iPSC-based approaches accelerate research on neurodegenerative diseases and take us closer to a cure for neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, Amyotrophic Lateral Sclerosis, and so forth.

Neuropathology and emerging biomarkers in corticobasal syndrome

Corticobasal syndrome (CBS) is a clinical syndrome characterised by progressive asymmetric limb rigidity and apraxia with dystonia, myoclonus, cortical sensory loss and alien limb phenomenon. Corticobasal degeneration (CBD) is one of the most common underlying pathologies of CBS, but other disorders, such as progressive supranuclear palsy (PSP), Alzheimer's disease (AD) and frontotemporal lobar degeneration with TDP-43 inclusions, are also associated with this syndrome.In this review, we describe common and rare neuropathological findings in CBS, including tauopathies, synucleinopathies, TDP-43 proteinopathies, fused in sarcoma proteinopathy, prion disease (Creutzfeldt-Jakob disease) and cerebrovascular disease, based on a narrative review of the literature and clinicopathological studies from two brain banks. Genetic mutations associated with CBS, including GRN and MAPT, are also reviewed. Clinicopathological studies on neurodegenerative disorders associated with CBS have shown that regardless of the underlying pathology, frontoparietal, as well as motor and premotor pathology is associated with CBS. Clinical features that can predict the underlying pathology of CBS remain unclear. Using AD-related biomarkers (ie, amyloid and tau positron emission tomography (PET) and fluid biomarkers), CBS caused by AD often can be differentiated from other causes of CBS. Tau PET may help distinguish AD from other tauopathies and non-tauopathies, but it remains challenging to differentiate non-AD tauopathies, especially PSP and CBD. Although the current clinical diagnostic criteria for CBS have suboptimal sensitivity and specificity, emerging biomarkers hold promise for future improvements in the diagnosis of underlying pathology in patients with CBS.

Beyond the average patient: how neuroimaging models can address heterogeneity in dementia

Dementia is a highly heterogeneous condition, with pronounced individual differences in age of onset, clinical presentation, progression rates and neuropathological hallmarks, even within a specific diagnostic group. However, the most common statistical designs used in dementia research studies and clinical trials overlook this heterogeneity, instead relying on comparisons of group average differences (e.g. patient versus control or treatment versus placebo), implicitly assuming within-group homogeneity. This one-size-fits-all approach potentially limits our understanding of dementia aetiology, hindering the identification of effective treatments. Neuroimaging has enabled the characterization of the average neuroanatomical substrates of dementias; however, the increasing availability of large open neuroimaging datasets provides the opportunity to examine patterns of neuroanatomical variability in individual patients. In this update, we outline the causes and consequences of heterogeneity in dementia and discuss recent research that aims to tackle heterogeneity directly, rather than assuming that dementia affects everyone in the same way. We introduce spatial normative modelling as an emerging data-driven technique, which can be applied to dementia data to model neuroanatomical variation, capturing individualized neurobiological 'fingerprints'. Such methods have the potential to detect clinically relevant subtypes, track an individual's disease progression or evaluate treatment responses, with the goal of moving towards precision medicine for dementia.

Concurrent tau pathologies in frontotemporal lobar degeneration with TDP-43 pathology

Aims

Accumulating evidence suggests that patients with frontotemporal lobar degeneration (FTLD) can have pathologic accumulation of multiple proteins, including tau and TDP‐43. This study aimed to determine the frequency and characteristics of concurrent tau pathology in FTLD with TDP‐43 pathology (FTLD‐TDP).

Methods

The study included 146 autopsy‐confirmed cases of FTLD‐TDP and 55 cases of FTLD‐TDP with motor neuron disease (FTLD‐MND). Sections from the basal forebrain were screened for tau pathology with phosphorylated‐tau immunohistochemistry. For cases with tau pathology on the screening section, additional brain sections were studied to establish a diagnosis. Genetic analysis of C9orf72, GRN and MAPT was performed on select cases.

Results

We found 72 cases (36%) with primary age‐related tauopathy (PART), 85 (42%) with ageing‐related tau astrogliopathy (ARTAG), 45 (22%) with argyrophilic grain disease (AGD) and 2 cases (1%) with corticobasal degeneration (CBD). Patients with ARTAG or AGD were significantly older than those without these comorbidities. One of the patients with FTLD‐TDP and CBD had C9orf72 mutation and relatively mild tau pathology, consistent with incidental CBD.

Conclusion

The coexistence of TDP‐43 and tau pathologies was relatively common, particularly PART and ARTAG. Although rare, patients with FTLD can have multiple neurodegenerative proteinopathies. The absence of TDP‐43‐positive astrocytic plaques may suggest that CBD and FTLD‐TDP were independent disease processes in the two patients with both tau and TDP‐43 pathologies. It remains to be determined if mixed cases represent a unique disease process or two concurrent disease processes in an individual.

Deep learning reveals disease-specific signatures of white matter pathology in tauopathies

Although pathology of tauopathies is characterized by abnormal tau protein aggregation in both gray and white matter regions of the brain, neuropathological investigations have generally focused on abnormalities in the cerebral cortex because the canonical aggregates that form the diagnostic criteria for these disorders predominate there. This corticocentric focus tends to deemphasize the relevance of the more complex white matter pathologies, which remain less well characterized and understood. We took a data-driven machine-learning approach to identify novel disease-specific morphologic signatures of white matter aggregates in three tauopathies: Alzheimer disease (AD), progressive supranuclear palsy (PSP), and corticobasal degeneration (CBD). We developed automated approaches using whole slide images of tau immunostained sections from 49 human autopsy brains (16 AD,13 CBD, 20 PSP) to identify cortex/white matter regions and individual tau aggregates, and compared tau-aggregate morphology across these diseases. Tau burden in the gray and white matter for individual subjects strongly correlated in a highly disease-specific fashion. We discovered previously unrecognized tau morphologies for AD, CBD and PSP that may be of importance in disease classification. Intriguingly, our models classified diseases equally well based on either white or gray matter tau staining. Our results suggest that tau pathology in white matter is informative, disease-specific, and linked to gray matter pathology. Machine learning has the potential to reveal latent information in histologic images that may represent previously unrecognized patterns of neuropathology, and additional studies of tau pathology in white matter could improve diagnostic accuracy.

Deep learning-based model for diagnosing Alzheimer's disease and tauopathies

AIMS

This study aimed to develop a deep learning-based model for differentiating tauopathies, including Alzheimer's disease (AD), progressive supranuclear palsy (PSP), corticobasal degeneration (CBD) and Pick's disease (PiD), based on tau-immunostained digital slide images.

METHODS

We trained the YOLOv3 object detection algorithm to detect five tau lesion types: neuronal inclusions, neuritic plaques, tufted astrocytes, astrocytic plaques and coiled bodies. We used 2522 digital slide images of CP13-immunostained slides of the motor cortex from 10 cases each of AD, PSP and CBD for training. Data augmentation was performed to increase the size of the training dataset. We next constructed random forest classifiers using the quantitative burdens of each tau lesion from motor cortex, caudate nucleus and superior frontal gyrus, ascertained from the object detection model. We split 120 cases (32 AD, 36 PSP, 31 CBD and 21 PiD) into training (90 cases) and test (30 cases) sets to train random forest classifiers.

RESULTS

The resultant random forest classifier achieved an average test score of 0.97, indicating that 29 out of 30 cases were correctly diagnosed. A validation study using hold-out datasets of CP13- and AT8-stained slides from 50 cases (10 AD, 17 PSP, 13 CBD and 10 PiD) showed >92% (without data augmentation) and >95% (with data augmentation) diagnostic accuracy in both CP13- and AT8-stained slides.

CONCLUSION

Our diagnostic model trained with CP13 also works for AT8; therefore, our diagnostic tool can be potentially used by other investigators and may assist medical decision-making in neuropathological diagnoses of tauopathies.

Artificial intelligence extension of the OSCAR-IB criteria

Artificial intelligence (AI)-based diagnostic algorithms have achieved ambitious aims through automated image pattern recognition. For neurological disorders, this includes neurodegeneration and inflammation. Scalable imaging technology for big data in neurology is optical coherence tomography (OCT). We highlight that OCT changes observed in the retina, as a window to the brain, are small, requiring rigorous quality control pipelines. There are existing tools for this purpose. Firstly, there are human-led validated consensus quality control criteria (OSCAR-IB) for OCT. Secondly, these criteria are embedded into OCT reporting guidelines (APOSTEL). The use of the described annotation of failed OCT scans advances machine learning. This is illustrated through the present review of the advantages and disadvantages of AI-based applications to OCT data. The neurological conditions reviewed here for the use of big data include Alzheimer disease, stroke, multiple sclerosis (MS), Parkinson disease, and epilepsy. It is noted that while big data is relevant for AI, ownership is complex. For this reason, we also reached out to involve representatives from patient organizations and the public domain in addition to clinical and research centers. The evidence reviewed can be grouped in a five-point expansion of the OSCAR-IB criteria to embrace AI (OSCAR-AI). The review concludes by specific recommendations on how this can be achieved practically and in compliance with existing guidelines.

Brain pathology recapitulates physiology: A network meta-analysis

Network architecture is a brain-organizational motif present across spatial scales from cell assemblies to distributed systems. Structural pathology in some neurodegenerative disorders selectively afflicts a subset of functional networks, motivating the network degeneration hypothesis (NDH). Recent evidence suggests that structural pathology recapitulating physiology may be a general property of neuropsychiatric disorders. To test this possibility, we compared functional and structural network meta-analyses drawing upon the BrainMap database. The functional meta-analysis included results from >7,000 experiments of subjects performing >100 task paradigms; the structural meta-analysis included >2,000 experiments of patients with >40 brain disorders. Structure-function network concordance was high: 68% of networks matched (pFWE < 0.01), confirming the broader scope of NDH. This correspondence persisted across higher model orders. A positive linear association between disease and behavioral entropy (p = 0.0006;R2 = 0.53) suggests nodal stress as a common mechanism. Corroborating this interpretation with independent data, we show that metabolic 'cost' significantly differs along this transdiagnostic/multimodal gradient.