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Background: In Huntington's disease clinical trials, recruitment and stratification approaches primarily rely on genetic load, cognitive and motor assessment scores. They focus less on in vivo brain imaging markers, which reflect neuropathology well before clinical diagnosis. Machine learning methods offer a degree of sophistication which could significantly improve prognosis and stratification by leveraging multimodal biomarkers from large datasets. Such models specifically tailored to HD gene expansion carriers could further enhance the efficacy of the stratification process., Objectives: To improve stratification of Huntington's disease individuals for clinical trials., Methods: We used data from 451 gene positive individuals with Huntington's disease (both premanifest and diagnosed) from previously published cohorts (PREDICT, TRACK, TrackON, and IMAGE). We applied whole-brain parcellation to longitudinal brain scans and measured the rate of lateral ventricular enlargement, over 3 years, which was used as the target variable for our prognostic random forest regression models. The models were trained on various combinations of features at baseline, including genetic load, cognitive and motor assessment score biomarkers, as well as brain imaging-derived features. Furthermore, a simplified stratification model was developed to classify individuals into two homogenous groups (low risk and high risk) based on their anticipated rate of ventricular enlargement., Results: The predictive accuracy of the prognostic models substantially improved by integrating brain imaging features alongside genetic load, cognitive and motor biomarkers: a 24 % reduction in the cross-validated mean absolute error, yielding an error of 530 mm 3 /year. The stratification model had a cross-validated accuracy of 81 % in differentiating between moderate and fast progressors (precision = 83 %, recall = 80 %)., Conclusions: This study validated the effectiveness of machine learning in differentiating between low- and high-risk individuals based on the rate of ventricular enlargement. The models were exclusively trained using features from HD individuals, which offers a more disease-specific, simplified, and accurate approach for prognostic enrichment compared to relying on features extracted from healthy control groups, as done in previous studies. The proposed method has the potential to enhance clinical utility by: i) enabling more targeted recruitment of individuals for clinical trials, ii) improving post-hoc evaluation of individuals, and iii) ultimately leading to better outcomes for individuals through personalized treatment selection., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Alzheimer's disease (AD) is the most common cause of dementia and is characterized by progressive neurodegeneration and cognitive decline. Understanding the pathophysiology underlying AD is paramount for the management of individuals at risk of and suffering from AD. The vascular hypothesis stipulates a relationship between cardiovascular disease and AD-related changes although the nature of this relationship remains unknown. In this review, we discuss several potential pathological pathways of vascular involvement in AD that have been described including dysregulation of neurovascular coupling, disruption of the blood brain barrier, and reduced clearance of metabolite waste such as beta-amyloid, a toxic peptide considered the hallmark of AD. We will also discuss the two-hit hypothesis which proposes a 2-step positive feedback loop in which microvascular insults precede the accumulation of Aß and are thought to be at the origin of the disease development. At neuroimaging, signs of vascular dysfunction such as chronic cerebral hypoperfusion have been demonstrated, appearing early in AD, even before cognitive decline and alteration of traditional biomarkers. Cerebral small vessel disease such as cerebral amyloid angiopathy, characterized by the aggregation of Aß in the vessel wall, is highly prevalent in vascular dementia and AD patients. Current data is unclear whether cardiovascular disease causes, precipitates, amplifies, precedes, or simply coincides with AD. Targeted imaging tools to quantitatively evaluate the intracranial vasculature and longitudinal studies in individuals at risk for or in the early stages of the AD continuum could be critical in disentangling this complex relationship between vascular disease and AD., (Copyright © 2022. Published by Elsevier Inc.)

Background: Huntington's disease (HD) is an inherited neurodegenerative disorder caused by an expanded CAG repeat in the HTT gene. It is diagnosed following a standardized examination of motor control and often presents with cognitive decline and psychiatric symptoms. Recent studies have detected genetic loci modifying the age at onset of motor symptoms in HD, but genetic factors influencing cognitive and psychiatric presentations are unknown., Methods: We tested the hypothesis that psychiatric and cognitive symptoms in HD are influenced by the same common genetic variation as in the general population by 1) constructing polygenic risk scores from large genome-wide association studies of psychiatric and neurodegenerative disorders and of intelligence and 2) testing for correlation with the presence of psychiatric and cognitive symptoms in a large sample (n = 5160) of patients with HD., Results: Polygenic risk score for major depression was associated specifically with increased risk of depression in HD, as was schizophrenia risk score with psychosis and irritability. Cognitive impairment and apathy were associated with reduced polygenic risk score for intelligence., Conclusions: Polygenic risk scores for psychiatric disorders, particularly depression and schizophrenia, are associated with increased risk of the corresponding psychiatric symptoms in HD, suggesting a common genetic liability. However, the genetic liability to cognitive impairment and apathy appears to be distinct from other psychiatric symptoms in HD. No associations were observed between HD symptoms and risk scores for other neurodegenerative disorders. These data provide a rationale for treatments effective in depression and schizophrenia to be used to treat depression and psychotic symptoms in HD., (Copyright © 2020 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Phenotypic manifestations of Huntington disease (HD) can be detected at least 15 years prior to the time when a motor diagnosis is given. Advances in clinical care and future research will require consistent use of HD definitions and HD premanifest (prodromal) stages being used across clinics, sites, and countries. Cognitive and behavioral (psychiatric) changes in HD are summarized and implications for ongoing advancement in our knowledge of prodromal HD are suggested. The earliest detected cognitive changes are observed in the Symbol Digit Modalities Test, Stroop Interference, Stroop Color and Word Test-interference condition, and Trail Making Test. Cognitive changes in the middle and near motor diagnostic stages of prodromal HD involve nearly every cognitive test administered and the greatest changes over time (i.e., slopes) are found in those prodromal HD participants who are nearest to motor diagnosis. Psychiatric changes demonstrate significant worsening over time and remain elevated compared with healthy controls throughout the prodromal disease course. Psychiatric and behavior changes in prodromal HD are much lower than that obtained using cognitive assessment, although the psychiatric and behavioral changes represent symptoms most debilitating to independent capacity and wellness., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Huntington disease (HD) is a fatal progressive neurodegenerative disorder for which only symptomatic treatment is available. A better understanding of the pathology, and identification of biomarkers will facilitate the development of disease-modifying treatments. HD is potentially a good model of a neurodegenerative disease for development of biomarkers because it is an autosomal-dominant disease with complete penetrance, caused by a single gene mutation, in which the neurodegenerative process can be assessed many years before onset of signs and symptoms of manifest disease. Previous MRI studies have detected abnormalities in gray and white matter starting in premanifest stages. However, the understanding of how these abnormalities are related, both in time and space, is still incomplete. In this study, we combined deep gray matter shape diffeomorphometry and white matter DTI analysis in order to provide a better mapping of pathology in the deep gray matter and subcortical white matter in premanifest HD. We used 296 MRI scans from the PREDICT-HD database. Atrophy in the deep gray matter, thalamus, hippocampus, and nucleus accumbens was analyzed by surface based morphometry, and while white matter abnormalities were analyzed in (i) regions of interest surrounding these structures, using (ii) tractography-based analysis, and using (iii) whole brain atlas-based analysis. We detected atrophy in the deep gray matter, particularly in putamen, from early premanifest stages. The atrophy was greater both in extent and effect size in cases with longer exposure to the effects of the CAG expansion mutation (as assessed by greater CAP-scores), and preceded detectible abnormalities in the white matter. Near the predicted onset of manifest HD, the MD increase was widespread, with highest indices in the deep and posterior white matter. This type of in-vivo macroscopic mapping of HD brain abnormalities can potentially indicate when and where therapeutics could be targeted to delay the onset or slow the disease progression.

Background: Previous neuroimaging research indicates that brain atrophy in Huntington disease (HD) begins many years before movement abnormalities become severe enough to warrant diagnosis. Most clinical trials being planned for individuals in the prediagnostic stage of HD propose to use delay of disease onset as the primary outcome measure. Although formulas have been developed based on age and CAG repeat length, to predict when HD motor onset will occur, it would be useful to have additional measures that can improve the accuracy of prediction of disease onset., Methods: The current study examined magnetic resonance imaging (MRI) measures of striatum and white matter volume in 85 individuals prospectively followed from pre-HD stage through diagnosable motor onset (incident cases) and 85 individuals individually matched with incident cases on CAG repeat length, sex, and age, who were not diagnosed with HD during the course of the study., Results: Volumes of striatum and white matter were significantly smaller in individuals who would be diagnosed 1 to 4 years following the initial MRI scan, compared with those who would remain in the pre-HD stage. Putamen volume was the measure that best distinguished between the two groups., Conclusions: Results suggest that MRI volumetric measures may be helpful in selecting individuals for future clinical trials in pre-HD where HD motor onset is the primary outcome measure. In planning for multisite clinical trials in pre-HD, investigators may also want to consider using more objective measures, such as MRI volumes, in addition to onset of diagnosable movement disorder, as major outcome measures., (Copyright © 2012 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Objective: Practice effects on cognitive tests have been shown to further characterize patients with amnestic mild cognitive impairment (aMCI) and may provide predictive information about cognitive change across time. We tested the hypothesis that a loss of practice effects would portend a worse prognosis in aMCI., Design: Longitudinal, observational design following participants across 1 year., Setting: Community-based cohort., Participants: Three groups of older adults: 1) cognitively intact (n = 57), 2) aMCI with large practice effects across 1 week (MCI + PE, n = 25), and 3) aMCI with minimal practice effects across 1 week (MCI - PE, n = 26)., Measurements: Neuropsychological tests., Results: After controlling for age and baseline cognitive differences, the MCI - PE group performed significantly worse than the other groups after 1 year on measures of immediate memory, delayed memory, language, and overall cognition., Conclusions: Although these results need to be replicated in larger samples, the loss of short-term practice effects portends a worse prognosis in patients with aMCI.

Background: Psychiatric disturbances are relatively common in manifest Huntington's disease (HD), but less is known about these symptoms in the earliest phase of the illness., Methods: This study examined self-reported psychiatric symptoms in a large sample (N = 681) of prediagnosed individuals who show the gene expansion for HD ("expansion-positive") compared with a sample of individuals who do not show the gene expansion but are at risk for HD ("expansion-negative")., Results: Using baseline Symptom Checklist 90-Revised (SCL-90-R) data from the Predict-HD study, expansion-positive individuals reported significantly more psychiatric symptoms (e.g., depression, anxiety, obsessive-compulsiveness) than expansion-negative individuals. Within the expansion-positive group, individuals with more motor signs had higher levels of psychiatric symptoms. The SCL-90-R scores had stronger relationships with reported abilities to perform activities of daily living than other markers of HD. Finally, when companions of the expansion-positive individuals also completed the SCL-90-R on the participants, there was considerable consistency in the ratings of psychiatric symptoms., Conclusions: Subtle, subclinical psychiatric symptoms are present in this prediagnosed HD sample, even though most are estimated to be more than 10 years from HD diagnosis. As suggested by other research, these subtle symptoms might be the earliest markers of the disease; however, longitudinal data are needed.

Background: The purpose of this study was to examine the probability of obsessive and compulsive (OC) symptoms across stages of Huntington's disease (HD) with both cross sectional and longitudinal data., Methods: We present the largest sample to date of individuals at risk for HD (N = 3964). Obsessive and compulsive symptoms were assessed with the Unified Huntington's Disease Rating Scale OC items., Results: The probability of meeting the threshold for obsessions and compulsions increased with greater disease severity. Those with no motor abnormalities ("at risk") had a 7% probability of obsessions and a 3.5% probability of compulsions; the peak probability for obsessions (24%) and compulsions (12%) occurred in patients with advanced disease with significant functional disability., Conclusions: The probability of OC symptoms is more than three times greater by stages 3 and 4 (clearly manifest disease) than in our at-risk group with no apparent motor abnormalities.

Background: Huntington's disease (HD) is traditionally conceptualized as a degenerative disease of the striatum. Recent scientific advances, however, have suggested neurodevelopmental contributions and extrastriatal brain abnormalities. This study was designed to assess the morphology of the brain in participants who had previously undergone elective DNA analyses for the HD mutation who did not currently have a clinical diagnosis of HD (preclinical HD subjects)., Methods: Twenty-four preclinical participants with the gene expansion for HD underwent brain magnetic resonance imaging and were compared with a group of 24 healthy control subjects, matched by gender and age., Results: Huntington's disease preclinical participants had substantial morphologic differences from controls throughout the cerebrum. Volume of the cerebral cortex was significantly increased in preclinical HD, whereas the basal ganglia and cerebral white matter volume were substantially decreased., Conclusions: In individuals with the HD gene mutation who are considered healthy (preclinical for manifest disease), the morphology of the brain is substantially altered compared with matched control subjects. Although decreased volumes of the striatum and cerebral white matter could represent early degenerative changes, the novel finding of enlarged cortex suggests that developmental pathology occurs in HD.

Background: This study sought to determine the relative importance of cognitive measures in predicting various domains of everyday functional capacity in older outpatients with schizophrenia., Methods: Ninety-three psychiatry outpatients with diagnoses of schizophrenia and schizoaffective disorders underwent a comprehensive neuropsychiatric evaluation, including neuropsychological testing and clinical ratings of psychopathology. Functional capacity was assessed with the Direct Assessment of Functional Status, a performance measure of basic and instrumental activities of daily living (ADLs/IADLs)., Results: Neuropsychological performance significantly predicted most ADLs/IADLs measured, except simple eating behaviors, time orientation, and grooming. Lower educational level and negative symptoms also were associated with worse functional capacity, whereas positive symptoms and depressed mood were not. Measures of cognitive functioning accounted for more variance in functional capacity than did psychiatric ratings of symptoms, and multiple regression analyses demonstrated that neuropsychological performance was predictive of functional capacity, over and above clinical symptoms. No specific cognitive domains were differentially predictive of specific domains of functional capacity., Conclusions: Neurocognitive abilities were more predictive of functional capacity than level of clinical symptoms; however, these abilities were not specific predictors of functioning. This is consistent with findings of relatively generalized, intercorrelated cognitive impairment in schizophrenia and multiply determined domains of everyday functioning.

Schizophrenia has traditionally been viewed as a psychotic disorder with onset in adolescence or early adulthood and a deteriorating course. Over the past decade, the authors have been studying patients meeting DSM-III-R as well as specified research criteria for late-onset schizophrenia (onset after age 45) and several comparison groups with psychiatric, neurologic, neuropsychologic, brain-imaging, psychophysiological, and psychosocial assessments. Results to date suggest a number of similarities and differences between late-onset schizophrenia and comparison groups of other older patients with psychoses (including earlier-onset schizophrenia). Later-onset schizophrenia is probably a neurobiologically distinct subtype of schizophrenia. Differential involvement of cortico-striato-pallido-thalamic circuitry may explain differences in age at onset. The authors propose a new conceptual model for level of functioning at different stages of life in late-onset schizophrenia.