Plasma ß-Amyloid in Mild Behavioural Impairment - Neuropsychiatric Symptoms on the Alzheimer's Continuum.

INTRODUCTION: Simple markers are required to recognize older adults at higher risk for neurodegenerative disease. Mild behavioural impairment (MBI) and plasma ß-amyloid (Aß) have been independently implicated in the development of incident cognitive decline and dementia. Here we studied the associations between MBI and plasma Aß42/Aß40. METHODS: Participants with normal cognition (n = 86) or mild cognitive impairment (n = 53) were selected from the Alzheimer's Disease Neuroimaging Initiative. MBI scores were derived from Neuropsychiatric Inventory items. Plasma Aß42/Aß40 ratios were assayed using mass spectrometry. Linear regressions were fitted to assess the association between MBI total score as well as MBI domain scores with plasma Aß42/Aß40. RESULTS: Lower plasma Aß42/Aß40 was associated with higher MBI total score (p = 0.04) and greater affective dysregulation (p = 0.04), but not with impaired drive/motivation (p = 0.095) or impulse dyscontrol (p = 0.29) MBI domains. CONCLUSION: In persons with normal cognition or mild cognitive impairment, MBI was associated with low plasma Aß42/Aß40. Incorporating MBI into case detection may help capture preclinical and prodromal Alzheimer's disease.

Neural correlates of the impulse dyscontrol domain of mild behavioral impairment.

OBJECTIVES: Agitation and aggression are common in dementia and pre-dementia. The dementia risk syndrome mild behavioral impairment (MBI) includes these symptoms in the impulse dyscontrol domain. However, the neural circuitry associated with impulse dyscontrol in neurodegenerative disease is not well understood. The objective of this work was to investigate if regional micro- and macro-structural brain properties were associated with impulse dyscontrol symptoms in older adults with normal cognition, mild cognitive impairment, and Alzheimer's disease (AD). METHODS: Clinical, neuropsychiatric, and T1-weighted and diffusion-tensor magnetic resonance imaging (DTI) data from 80 individuals with and 123 individuals without impulse dyscontrol were obtained from the AD Neuroimaging Initiative. Linear mixed effect models were used to assess if impulse dyscontrol was related to regional DTI and volumetric parameters. RESULTS: Impulse dyscontrol was present in 17% of participants with NC, 43% with MCI, and 66% with AD. Impulse dyscontrol was associated with: (1) lower fractional anisotropy (FA), and greater mean, axial, and radial diffusivity in the fornix; (2) lesser FA and greater radial diffusivity in the superior fronto-occipital fasciculus; (3) greater axial diffusivity in the cingulum; (4) greater axial and radial diffusivity in the uncinate fasciculus; (5) gray matter atrophy, specifically, lower cortical thickness in the parahippocampal gyrus. CONCLUSION: Our findings provide evidence that well-established atrophy patterns of AD are prominent in the presence of impulse dyscontrol, even when disease status is controlled for, and possibly in advance of dementia. Our findings support the growing evidence for impulse dyscontrol symptoms as an early manifestation of AD.

Building machine learning models without sharing patient data: A simulation-based analysis of distributed learning by ensembling.

The development of machine learning solutions in medicine is often hindered by difficulties associated with sharing patient data. Distributed learning aims to train machine learning models locally without requiring data sharing. However, the utility of distributed learning for rare diseases, with only a few training examples at each contributing local center, has not been investigated. The aim of this work was to simulate distributed learning models by ensembling with artificial neural networks (ANN), support vector machines (SVM), and random forests (RF) and evaluate them using four medical datasets. Distributed learning by ensembling locally trained agents improved performance compared to models trained using the data from a single institution, even in cases where only a very few training examples are available per local center. Distributed learning improved when more locally trained models were added to the ensemble. Local class imbalance reduced distributed SVM performance but did not impact distributed RF and ANN classification. Our results suggest that distributed learning by ensembling can be used to train machine learning models without sharing patient data and is suitable to use with small datasets.

Depressive Symptoms Are Associated With More Negative Functional Outcomes Than Anxiety Symptoms in Persons With Multiple Sclerosis.

Depression and anxiety are common among persons with multiple sclerosis (MS), and both negatively affect functional status. However, studies rarely account for overlap in depressive and anxiety symptoms on functional outcomes among people with MS. The authors aimed to examine the differential impact of depression and anxiety, measured by the Anxiety and Depression subscales of the Hospital Anxiety and Depression Scale (HADS-A and HADS-D), on functional outcomes among people with MS. Using a retrospective chart review of 128 people with MS, the authors used exploratory structural equation modeling to examine the relation of HADS-A and HADS-D to functional outcomes, namely employment status, fatigue (with the Fatigue Severity Scale), disability (with the Expanded Disability Status Scale [EDSS]), and cognition (with the Symbol Digit Modalities Test [SDMT]). After the authors controlled for the effects of covariates, HADS-A was negatively associated with EDSS (ß=-0.22, p<0.05) and positively associated with vocation (ß=0.23, p<0.05). In contrast, HADS-D was positively correlated with fatigue (ß=0.37, p<0.05) and EDSS (ß=0.26, p<0.05) and negatively correlated with vocation (ß=-0.32, p<0.05) and SDMT (ß=-0.28, p<0.05). HADS-A and HADS-D explained 5% of the variability in employment, 14.5% in fatigue, 1.6% in EDSS, and 4.3% in SDMT, beyond the effects of the covariates. Depressive symptoms have a significant negative impact on functional outcomes among people with MS, relative to anxiety symptoms. These findings support the importance of identifying and treating depressive symptoms among people with MS.

Progression to Dementia or Reversion to Normal Cognition in Mild Cognitive Impairment as a Function of Late-Onset Neuropsychiatric Symptoms.

BACKGROUND AND OBJECTIVES: Mild cognitive impairment (MCI) is an at-risk state for dementia; however, not all individuals with MCI transition to dementia, and some revert to normal cognition (NC). Here, we investigate whether mild behavioral impairment (MBI), the late-life onset of persistent neuropsychiatric symptoms (NPS), improves the prognostic specificity of MCI. METHODS: Participants with MCI from the National Alzheimer's Coordinating Center Uniform Data Set were included. NPS were operationalized with the Neuropsychiatric Inventory Questionnaire to identify participants without NPS and those with MBI (persistent, late-onset NPS). Individuals with late-onset NPS not meeting the MBI persistence criterion (NPS_NOT_MBI) were retained for secondary analyses. Progression to dementia, stable MCI, and reversion to NC after 3 years of follow-up were defined per National Institute on Aging-Alzheimer's Association and Petersen criteria. RESULTS: The primary sample consisted of 739 participants (NPS- n = 409 and MBI+ n = 330; 75.16 ± 8.6 years old, 40.5% female). After 3 years, 238 participants (33.6%) progressed to dementia, and 90 (12.2%) reverted to NC. Compared to participants without NPS, participants with MBI were significantly more likely to progress to dementia (adjusted odds ratio [AOR] 2.13, 95% CI 1.52-2.99), with an annual progression rate of 14.7% (vs 8.3% for participants with MCI without NPS). Compared to participants without NPS, participants with MBI were less likely to revert to NC (AOR 0.48, 95% CI 0.28-0.83, 2.5% vs 5.3% annual reversion rate). The NPS_NOT_MBI group (n = 331, 76.5 ± 8.6 years old, 45.9% female) were more likely to progress to dementia (AOR 2.18, 95% CI 1.56-3.03, 14.3% annual progression rate) but not less likely to revert to NC than those without NPS. Accordingly, both NPS_NOT_MBI and MBI+ participants had lower Mini-Mental State Examination scores than NPS- participants after 3 years. DISCUSSION: Late-onset NPS improve the specificity of MCI as an at-risk state for progression to dementia. However, only persistent late-onset NPS are associated with a lower likelihood of reversion to NC, with transient NPS (i.e., NPS_NOT_MBI) not differing from the NPS- group. Clinical prognostication can be improved by incorporating late-onset NPS, especially those that persist (i.e., MBI), into risk assessments. Clinical trials may benefit from enrichment with these higher-risk participants with MCI.

Contribution of vascular risk factors to the relationship between ADHD symptoms and cognition in adults and seniors.

Symptoms of attention-deficit/hyperactivity disorder (ADHD) in childhood have been found to be predictive of compromised cognitive function, and possibly even dementia, in later adulthood. This study aimed to test vascular risk as a hypothesized moderator or mediator of this association, because individuals with elevated ADHD symptoms frequently have comorbid vascular disease or risk factors which are recognized to contribute to later-life cognitive decline. Data from 1,092 adults aged 18-85 were drawn from the Enhanced Nathan Kline Institute Rockland Sample. Childhood ADHD symptoms (assessed using the Adult ADHD Clinical Diagnostic Scale) were assessed as predictors of cognitive functioning in adulthood (assessed using subtests from the University of Pennsylvania Computerized Neurocognitive Battery, the Delis-Kaplan Executive Functioning System, and the Wechsler Memory Scale). Vascular risk factors (including diabetes, tobacco use, obesity, hypertension, and hypercholesterolemia) were tested as both a moderator and mediator of this relationship. Childhood ADHD symptoms and vascular risk factors were both independently associated with later-life cognition, but vascular risk was not a significant moderator or mediator of relationships between ADHD symptoms and cognition in statistical models. Results from this large community sample suggest that the relationship between ADHD symptoms and cognition is not accounted for by vascular risk. This question should also be investigated in clinical samples.

Mild Behavioral Impairment and Subjective Cognitive Decline Predict Cognitive and Functional Decline.

BACKGROUND: Mild behavioral impairment (MBI) and subjective cognitive decline (SCD) are dementia risk states, and potentially represent neurobehavioral and neurocognitive manifestations, respectively, of early stage neurodegeneration. Both MBI and SCD predict incident cognitive decline and dementia, are associated with known dementia biomarkers, and are both represented in the NIA-AA research framework for AD in Stage 2 (preclinical disease). OBJECTIVE: To assess the associations of MBI and SCD, alone and in combination, with incident cognitive and functional decline in a population of older adults. We tested the hypothesis that MBI and SCD confer additive risk for decline. METHODS: Cognitively normal participants were followed up annually at Alzheimer's Disease Centers. Logistic regression assessed the relationship between baseline classification (MBI-SCD-, MBI-SCD+, MBI+SCD-, or MBI+SCD+) and 3-year outcome. RESULTS: Of 2,769 participants (mean age=76), 1,536 were MBI-SCD-, 254 MBI-SCD+, 743 MBI+SCD-, and 236 MBI+SCD+. At 3 years, 349 (12.6%) declined to CDR >0, including 23.1% of the MBI+group, 23.5% of the SCD+group, and 30.9% of the intersection group of both MBI+and SCD+participants. Compared to SCD-MBI-, we observed an ordinal progression in risk (ORs [95% CI]): 3.61 [2.42-5.38] for MBI-SCD+ (16.5% progression), 4.76 [3.57-6.34] for MBI+SCD- (20.7%), and 8.15 [5.71-11.64] for MBI+SCD+(30.9%). CONCLUSION: MBI and SCD together were associated with the greatest risk of decline. These complementary dementia risk syndromes can be used as simple and scalable methods to identify high-risk patients for workup or for clinical trial enrichment.

Using Machine Learning to Predict Dementia from Neuropsychiatric Symptom and Neuroimaging Data.

BACKGROUND: Machine learning (ML) is a promising technique for patient-specific prediction of mild cognitive impairment (MCI) and dementia development. Neuropsychiatric symptoms (NPS) might improve the accuracy of ML models but have barely been used for this purpose. OBJECTIVES: To investigate if baseline mild behavioral impairment (MBI) status used for NPS quantification along with brain morphology features are predictive of follow-up diagnosis, median 40 months later in patients with normal cognition (NC) or MCI. METHOD: Baseline neuroimaging, neuropsychiatric, and clinical data from 102 individuals with NC and 239 with MCI were extracted from the Alzheimer's Disease Neuroimaging Initiative database. Neuropsychiatric inventory questionnaire items were transformed to MBI domains using a published algorithm. Diagnosis at latest follow-up was used as the outcome variable and ground truth classification. A logistic model tree classifier combined with information gain feature selection was trained to predict follow-up diagnosis. RESULTS: In the binary classification (NC versus MCI/AD), the optimal ML model required only two features from over 200, MBI total score and left hippocampal volume. These features correctly classified participants as remaining normal or developing cognitive impairment with 84.4% accuracy (area under the receiver operating characteristics curve [ROC-AUC] = 0.86). Seven features were selected for the three-class model (NC versus MCI versus dementia) achieving an accuracy of 58.8% (ROC-AUC=0.73). CONCLUSION: Baseline NPS, categorized for MBI domain and duration, have prognostic utility in addition to brain morphology measures for predicting diagnosis change using ML. MBI total score, followed by impulse dyscontrol and affective dysregulation were most predictive of future diagnosis.

Plasma Neurofilament Light: A Marker of Neurodegeneration in Mild Behavioral Impairment.

BACKGROUND: Assessing neuropsychiatric symptoms (NPS) in older adults is important for determining dementia risk. Mild behavioral impairment (MBI) is an at-risk state for cognitive decline and dementia, characterized by emergent NPS in later life. MBI has significantly higher dementia incidence than late life psychiatric conditions. However, its utility as a proxy for neurodegeneration has not been demonstrated. Plasma neurofilament light (NfL) is a validated biomarker of axonal damage, and has been shown to associate with hallmarks of neurodegeneration. OBJECTIVE: The purpose of this investigation was to identify associations between NfL rate of change and the presence of MBI symptomatology. METHODS: We evaluated the association of MBI with changes in NfL in a cohort (n = 584; MBI + n = 190, MBI- n = 394) of non-demented participants from the Alzheimer's Disease Neuroimaging Initiative. MBI was determined by transforming Neuropsychiatric Inventory Questionnaire items using a published algorithm. Change in NfL was calculated over 2 years. RESULTS: Time*MBI status was the only significant interaction to predict change in NfL concentrations (F(1,574) = 4.59, p = 0.032), even after controlling for age, mild cognitive impairment, and demographics. Analyses reclassifying 64 participants with new onset MBI over 2 years similarly demonstrated greater increases in NfL (F(1,574) = 5.82, p = 0.016). CONCLUSION: These findings suggest MBI is a clinical proxy of early phase neurodegeneration with putative utility in identifying those at dementia risk. MBI can be used as a case ascertainment approach to capture those at high risk for cognitive decline and dementia, and is an important construct for clinicians dealing with cognitive and neuropsychiatric symptomatology in older adults.

Agitation and impulsivity in mid and late life as possible risk markers for incident dementia.

To identify knowledge gaps regarding new-onset agitation and impulsivity prior to onset of cognitive impairment or dementia the International Society to Advance Alzheimer's Research and Treatment Neuropsychiatric Syndromes (NPS) Professional Interest Area conducted a scoping review. Extending a series of reviews exploring the pre-dementia risk syndrome Mild Behavioral Impairment (MBI), we focused on late-onset agitation and impulsivity (the MBI impulse dyscontrol domain) and risk of incident cognitive decline and dementia. This scoping review of agitation and impulsivity pre-dementia syndromes summarizes the current biomedical literature in terms of epidemiology, diagnosis and measurement, neurobiology, neuroimaging, biomarkers, course and prognosis, treatment, and ongoing clinical trials. Validations for pre-dementia scales such as the MBI Checklist, and incorporation into longitudinal and intervention trials, are needed to better understand impulse dyscontrol as a risk factor for mild cognitive impairment and dementia.

Financial capacity in frontotemporal dementia and related presentations.

BACKGROUND: Changes in financial judgement and skills can herald a neurodegenerative dementia and are a common reason for referral for cognitive neurologic assessment. However, patients with neurodegenerative diseases affecting the frontal or temporal lobes may perform well on standard cognitive tests, complicating clinical determinations about their diagnosis and financial capacity. METHODS: Forty-five patients with possible or probable FTD or Alzheimer's disease and 22 healthy controls completed two financial assessment batteries, the FACT and the FCAI. Patients' performance was compared to study partner estimates of patients' financial abilities. RESULTS: All three patient groups performed worse than controls on both the FACT and the FCAI. Study partners over-estimated the performance of patients with Alzheimer's disease. CONCLUSIONS: These initial findings suggest that accurate clinical assessment of financial skills and judgement in patients with possible neurodegenerative dementias requires performance-based assessment.

Dialectical Behavior Group Therapy is Feasible and Reduces Emotional Dysfunction in Multiple Sclerosis.

We examined whether dialectical behavior therapy (DBT) was feasible and effective in multiple sclerosis (MS). A convenience sample of 20 patients with anxiety or depression symptoms received either DBT (n = 10) or standard medical care (n = 10). The DBT protocol was found to be feasible in the MS population studied (e.g., good retention and acceptability). For the DBT group, significant improvements were demonstrated in self-rated and clinician-rated depressive symptoms, clinician-rated anxiety symptoms, self-rated general psychopathology symptoms, and quality of life. In contrast, the standard medical care group retained for exploratory purposes showed no significant improvements. This pilot work provides preliminary support for the utility of DBT in MS, but further work is needed to clarify this benefit using a large, randomized controlled approach.

The mediating role of processing speed in the relationship between depressive symptoms and cognitive function in multiple sclerosis.

INTRODUCTION: Although disorders of mood and cognition are frequently observed in multiple sclerosis, their relationship remains unclear. We aimed to investigate whether this mood-cognition relationship is mediated by inefficient processing speed, a deficit typically associated with mood symptomatology in the psychiatric literature and a common deficit observed in multiple sclerosis patients. METHOD: In this study, comprehensive cognitive data and self-reported mood data were retrospectively analyzed from 349 patients with relapsing remitting multiple sclerosis. We performed a bootstrapping analysis to examine whether processing speed provided an indirect means by which depressive symptoms influenced cognitive functioning, specifically memory and executive function. RESULTS: We observed that processing speed mediated the relationship between depressive symptoms and measures of memory and executive function. Interestingly, exploratory analyses revealed that this mediational role of processing speed was specific to MS patients who were younger, had a lower disability level, and had fewer years since MS diagnosis. CONCLUSIONS: Together, these findings have implications for mood and cognitive intervention with multiple sclerosis patients.