Existing research has demonstrated that neuropsychiatric/behavioral-psychological symptoms of dementia (BPSD) frequently contribute to worse prognosis in patients with neurodegenerative conditions (e.g., increased functional dependence, worse quality of life, greater caregiver burden, faster disease progression). BPSD are most commonly measured via the Neuropsychiatric Inventory (NPI), or its briefer, informant-rated questionnaire (NPI-Q). Despite the NPI-Q’s common use in research and practice, there is disarray in the literature concerning the NPI-Q’s latent structure and reliability, possibly related to differences in methods between studies. Also, hierarchical factor models have not been considered, even though such models are gaining favor in the psychopathology literature. Therefore, we aimed to compare different factor structures from the current literature using confirmatory factor analyses (CFAs) to help determine the best latent model of the NPI-Q.

This sample included 20,500 individuals (57% female; 80% White, 12% Black, 8% Hispanic), with a mean age of 71 (SD = 10.41) and 15 average years of education (SD = 3.43). Individuals were included if they had completed an NPI-Q during their first visit at one of 33 Alzheimer Disease Research Centers reporting to the National Alzheimer Coordinating Center (NACC). All CFA and reliability analyses were performed with lavaan and semTools R packages, using a diagonally weighted least squares (DWLS) estimator. Eight single-level models using full or modified versions of the NPI-Q were compared, and the top three were later tested in bifactor form.

CFAs revealed all factor models of the full NPI-Q demonstrated goodness of fit across multiple indices (SRMR = 0.039-0.052, RMSEA = 0.025-0.029, CFI = 0.973-0.983, TLI = 0.9670.977). Modified forms of the NPI-Q also demonstrated goodness of fit across multiple indices (SRMR = 0.025-0.052, RMSEA = 0.0180.031, CFI = 0.976-0.993, TLI = 0.968-0.989). Top factor models later tested in bifactor form all demonstrated consistently stronger goodness of fit regardless of whether they were a full form (SRMR = 0.023-0.035, RMSEA = 0.015-0.02, CFI = 0.992-0.995, TLI = 0.985-0.991) or a modified form (SRMR = 0.023-0.042, RMSEA = 0.015-0.024, CFI = 0.985-0.995, TLI = 0.9770.992). Siafarikas and colleagues’ (2018) 3-factor model demonstrated the best fit among the full-form models, whereas Sayegh and Knight’s (2014) 4-factor model had the best fit among all single-level models, as well as among the bifactor models.

Although all factor models had adequate goodness of fit, the Sayegh & Knight 4-factor model had the strongest fit among both single-level and bifactor models. Furthermore, all bifactor models had consistently stronger fit than single-level models, suggesting that BPSD are best theoretically explained by a hierarchical, non-nested framework of general and specific contributors to symptoms. These findings also inform consistent use of NPI-Q subscales.

To evaluate changes in neuropsychiatric symptoms among patients with multiple sclerosis (MS) following coronavirus disease of 2019 (COVID-19) infection using the National COVID Cohort Collaborative (N3C). The N3C represents the largest cohort of COVID-19 cases, through the unification of electronic health records from over 60 medical centers.

Out of 5,631,225 COVID-19 confirmed positive patients, we identified a cohort of patients with MS who were diagnosed with COVID-19. Conditions were searched using terms denoting common neuropsychiatric comorbid diagnoses, including anxiety, depression, pain, sleep disorders, fatigue, and cognitive disorders. We examined descriptively the percentages of patients who were newly diagnosed with each comorbid condition after COVID-19 infection. Additionally, we searched for various patient-reported outcome measures in the N3C dataset; only the Patient Health Questionnaire-9 (PHQ-9) had an adequate sample size in our cohort for analysis. To control for variability due to non-COVID-19 factors, we only included PHQ-9 scores that were reported one year before and after COVID-19 infection. A repeated-measures analysis of variance (ANOVA) was conducted to analyze the difference between PHQ-9 scores before and after COVID-19 diagnosis among MS patients.

In our final dataset, there were 40,690 patients who were diagnosed with MS and COVID-19. Among patients without pre-existing anxiety conditions, 9.18% were diagnosed with an anxiety disorder after COVID-19 infection. Among those who did not have a pre-existing cognitive disorder, 1.73% had such diagnoses after COVID-19 infection. Among those without previous depressive disorders, 8.89% were diagnosed with a depressive disorder after COVID-19 infection. Of those without fatigue conditions prior to COVID-19 in their medical records, 8.81% had documented fatigue in their records after contracting COVID-19. Of those without pain conditions in their medical records, 11.37% had documented pain in their records after COVID-19 infection. Finally, among patients without pre-existing sleep disorders, 8.71% were diagnosed with sleep disorders after COVID-19 infection. Regarding PHQ-9 scores, 50 patients had documented scores before their COVID-19 diagnosis and 50 after COVID-19 diagnosis (17 had scores for both before and after COVID-19 diagnosis). There was no significant difference in PHQ-9 scores before and after COVID-19 diagnosis (F(df) = 0.326, p = 0.572; meanbefore = 8.77, meanafter = 9.32).

Approximately 2-11% of MS patients developed new neuropsychiatric conditions after COVID-19 infection, with pain being the most common, followed by anxiety, fatigue, depression, and sleep disorders. Cognitive disorders were the least prevalent new onset neuropsychiatric sequelae of COVID-19 in this cohort. Additionally, there was a non-significant increase in severity of depressive symptoms, as indicated by a 1.36-point increase in PHQ-9 scores. These results suggest that patients with MS who have also been diagnosed with COVID-19 may be at risk for developing newly onset neuropsychiatric symptoms.

Decline in everyday function is a hallmark of dementia and is associated with increased caregiver burden, medical spending, and poorer quality of life. Neuropsychiatric symptoms (e.g., apathy, hallucinations) can also occur in those with dementia and have been associated with worse everyday functioning cross-sectionally. However, research on which neuropsychiatric symptoms are most associated with everyday functioning in those with dementia longitudinally has been more limited. Further, it is unknown which neuropsychiatric symptoms may add incremental validity beyond cognition in predicting everyday function longitudinally. The current study aimed to address both of these gaps in the literature by identifying which neuropsychiatric symptoms are most associated with everyday function over time and if symptoms add incremental validity in predicting everyday function beyond cognition in those with dementia.

Older adult participants (N = 4525), classified as having dementia at baseline by the National Alzheimer's Coordinating Center, were examined. Severity of neuropsychiatric symptoms were measured via the Neuropsychiatric Symptoms Questionnaire-Informant. Everyday function was assessed via the Functional Activities Questionnaire-Informant. Memory (Logical Memory immediate and delayed) and executive function (Digit Symbol Test, TMT-A and TMT-B) composites were created to assess cognition. Severity of neuropsychiatric symptoms at baseline were analyzed as predictors of everyday functioning beyond demographic factors and cognition at baseline and over the course of five years using multilevel modeling.

At baseline, severity of the majority of symptoms, excluding irritability, manic symptoms, and changes in appetite, were associated with everyday function (all p < .05). When examining everyday functioning longitudinally, only severity of hallucinations, apathy, motor dysfunction, and sleep dysfunction were associated with differences in everyday function over time (all p < .01).

There is heterogeneity in the degree to which neuropsychiatric symptoms are associated with everyday functioning over time in those with dementia. Additionally, our results show that some neuropsychiatric symptoms are associated with longitudinal changes in everyday function beyond domains of cognition show to be associated with function. Clinicians should pay particular attention to which neuropsychiatric symptoms individuals with dementia and their families are reporting to aid with treatment planning and clinical decision making related to autonomy. Future research would benefit from examining pathways through which neuropsychiatric symptoms are associated with everyday functioning over time in this population, and if treatments of neuropsychiatric symptoms may improve everyday function in this population.

Parkinson’s disease (PD) affects the person’s quality of life, but the comorbidity of PD and impulsive control disorder (ICD), which has an average prevalence of 23%, can enhance the disruption of quality of life for the patients and their caregivers. The effects of ICD in PD on brain morphology and cognition have been little studied. Thus, this study proposes to investigate the differences in the evolution of cognitive performance and brain structures between PD patients with ICD (PD-ICD) vs. without ICD (PD-no-ICD).

Parkinson’s Progression Markers Initiative (PPMI) data of 58 patients with idiopathic PD, including their MRI data at baseline and three years later, were analyzed. The MRIs were processed with FreeSurfer (7.1.1) to extract cortical volumes, areas, thicknesses, curvatures and folding index as well as volumes of subcortical segmentations. All participants underwent cognitive evaluations. The Questionnaire for Impulsive-Compulsive Disorders in Parkinson’s Disease was used to differentiate those with at least one ICD from those without any ICD. 12 of the 58 patients had an ICD at their first visit and 19 had an ICD at their visit three years later. There was no significant difference between PD-ICD and PD-no-ICD with respect to sex, use of overall medication, age, age of onset, age at diagnosis, years of education and the Montreal cognitive assessment score. Two-way mixed ANOVAs were performed for each neuropsychological test and brain structure extracted from MRIs with the time of the visit as the repeated independent variable (within participants) and the presence or absence of an ICD as the other independent variable (between participants).

The mixed ANOVA revealed that PD-ICD had their performance decline after three years, for the Hopkins Verbal Learning Test delayed recall and the Symbol Digit Modalities Test while PD-no-ICD saw their performance increase. A whole brain analysis showed that PD-ICD had a significant decrease after three years of the right cortex area total brain volume in comparison to PD-no-ICD. Specific brain structures also underwent significant changes over three years. Cortical changes in PD-ICD were: (1) increased surface area in the left temporal parahippocampus and (2) decreased surface areas of the right insula, right middle and superior temporal regions, left occipital lingual as well as left cingulate isthmus. Furthermore, in the subcortical nuclei, PD-ICD showed (1) increased volumes of the paratenial thalamic nucleus and whole right amygdala and (2) decreased volumes of the right amygdalian basal nucleus and thalamic ventromedial nucleus.

This study suggests that PD patients who also have ICD might be prone to develop over three years: (1) significant changes in cognitive performance (memory, attention), (2) morphological changes in the amygdala and thalamic nuclei and (3) significant atrophy and area shrinkage in the temporal and insula regions.

Neuropsychiatric symptoms due to Alzheimer’s disease (AD) and mild cognitive impairment (MCI) can decrease quality of life for patients and increase caregiver burden. Better characterization of neuropsychiatric symptoms is needed to identify effective treatment targets. The current investigation leveraged the National Alzheimer’s Coordinating Center (NACC) Uniform Data Set (UDS) to examine the network structure of neuropsychiatric symptoms among symptomatic older adults with cognitive impairment.

The identified sample includes those from the NACC UDS (all versions) with complete data on the Neuropsychiatric Inventory Questionnaire (NPI-Q) at initial visit. The NPI-Q is an informant-based estimation of the presence and severity of neuropsychiatric symptoms (delusions, hallucinations, agitation or aggression, depression or dysphoria, anxiety, elation or euphoria, apathy or indifference, disinhibition, irritability or lability, motor disturbance, nighttime behaviors, appetite and eating problems). The following inclusionary criteria were applied for sample identification: age 50+; cognitive status of MCI or dementia; AD was the primary or contributing cause of observed impairment; and at least one symptom on the NPI-Q was endorsed. Participants were excluded if they endorsed “unknown” or “not available” on any NPI-Q items. The final sample (n = 12,507) consisted of older adults (Mage=73.94, SDage=9.41; 46.2% male, 53.8% female) who predominantly identified as non-Hispanic white (NHW) (74.5% NHW, 10.9% non-Hispanic Black, 8.5% other, 5.8% Hispanic white, .3% Hispanic Black). The majority of the sample met criteria for dementia (77.6% dementia, 22.4% MCI) and AD was the presumed primary etiology in 93.9%.

The eLasso method was used to estimate the binary network, wherein nodes represent NPI-Q variables and edges represent their pairwise dependency after controlling for all other symptom variables in the network. In other words, the network represents the conditional probability of an observed binary variable (e.g., presence/absence of delusions) given all other measured variables (e.g., presence/absence of all other NPI-Q symptoms) (Finnemann et al., 2021; van Borkulo et al., 2014). Strength centrality and expected influence were calculated to determine relative importance of each symptom variable in the network. Network accuracy was examined with methods recommended by Epskamp et al. (2018), including edge-weight accuracy, centrality stability, and difference tests.

Edge weights and node centrality (CS(cor=.7)=.75) were stable and interpretable. The network (M=.28) consisted of mostly positive edges and some negative edges. The strongest edges linked nodes within symptom domain (e.g., strong positive associations among externalizing symptoms). Disinhibition and agitation/aggression were the most central and influential symptoms in the network, respectively. Depression or dysphoria was the most frequently endorsed symptom, followed by anxiety, apathy or indifference, and irritability or lability.

Endorsed disinhibition and agitation yielded a higher probability of additional neuropsychiatric symptoms and influenced the activation, persistence, and remission of other neuropsychiatric symptoms within the network. Thus, interventions targeting these symptoms may lead to greater neuropsychiatric symptom improvement overall. Depression or dysphoria, while highly endorsed, was least influential in the network. This may suggest that depression and dysphoria are common, but not central neuropsychiatric features of AD pathology. Future work will compare neuropsychiatric symptom networks across racial and ethnic groups and between MCI and dementia.

Mild cognitive impairment (MCI) is common in Parkinson’s disease (PD). Recent scientific advances show that MCI in PD could also be impacted by neuropsychiatric symptoms (such as apathy, anxiety, depression), dopaminergic deficiency (more striatal denervation associated with MCI) and certain genotypes such as in APOE E4, MAPT H1 or SNCA C/C carriers. We used a python-based random forest machine-learning algorithm (scikit-learn) in order to evaluate the factors that are mostly involved in the MCI conversion over a 5-year follow-up period.

Baseline data of healthy individuals and participants with Parkinson’s disease were extracted from the PPMI dataset. All participants also had the evaluations of their cognitive status, neuropsychiatric symptoms (hallucinations, anxiety, apathy, depression, sleepiness, impulse control disorders and rapid eye movement behaviors), dopaminergic uptake (DaT-Scan) and genetic status (APOE, MAPT and SNCA) at baseline and after 5 years. Baseline demographic (age, sex, education years) and clinical values (duration of disease, age of onset) were also included in the model. The algorithm defined (1) the most important variables in predicting MCI, (2) the threshold values to distinguish “converting” vs. “non-converting” subgroups.

The algorithm showed that (1) age onset of disease, (2) dopaminergic uptake, (3) age, (4) anxiety, and (5) years of education were the most important factors in predicting MCI over 5 years. Among the factors involved in predicting conversion to MCI, a lower number of years of education associated with lower dopaminergic uptake in the right putamen increased the risk of conversion. Individuals with more years of education are at higher risk of conversion if they have symptoms of depression, anxiety, and lower right striatal dopamine uptake. Other factors that were involved in increasing the risk, were the presence of sleepiness and the presence of rapid eye movement disorders. Interestingly, the genetic factors were of negligible importance and were not considered by the algorithm. Finally, the model showed an accuracy of classification of participants (converters vs. non-converters) of 92.53%.

Random forest algorithm shows that (1) depression and anxiety are probably important factors for MCI conversion; (2) years of education influences the conversion; (3) presence of sleepiness and rapid eye movement increases the risk of conversion to MCI. Since the algorithm considers the disease’s age onset, but not the diagnosis of individuals, it would be necessary to generate a model for each group (Healthy on the one hand, Parkinson’s on the other).

Training and practice in neuropsychiatry varies across the world. However, little is known about the experiences and opinions of early career psychiatrists (ECPs) across different countries regarding neuropsychiatry.

To investigate neuropsychiatry training experiences, practices and opinions among ECPs across different countries. An online survey was distributed to ECPs in 35 countries across the world.

A total of 522 participants took part in this study. Responses show that neuropsychiatry is integrated to a variable extent in psychiatric training curricula across the world. Most respondents were not aware of the existence of neuropsychiatric training or of neuropsychiatric units. Most agreed that training in neuropsychiatry should be done during or after the psychiatry training period. Lack of interest among specialty societies, lack of time during training, and political and economic reasons are regarded as the main barriers.

These findings call for an improvement in the extent and in the quality of neuropsychiatry training across the world.

The co-occurrence of stroke and psychosis is a serious neuropsychiatric condition but little is known about the course of this comorbidity. We aimed to estimate longitudinal associations between stroke and psychosis over 10 years.

A 10-year population-based study using data from the English Longitudinal Study of Ageing. A structured health assessment recorded (i) first-occurrence stroke and (ii) psychosis, at each wave. Each were considered exposures and outcomes in separate analyses. Logistic and Cox proportional hazards regression and Kaplan–Meier methods were used. Models were adjusted for demographic and health behaviour covariates, with missing covariates imputed using random forest multiple imputation.

Of 19 808 participants, 24 reported both stroke and psychosis (median Wave 1 age 63, 71% female, 50% lowest quintile of net financial wealth) at any point during follow-up. By 10 years, the probability of an incident first stroke in participants with psychosis was 21.4% [95% confidence interval (CI) 12.1–29.6] compared to 8.3% (95% CI 7.8–8.8) in those without psychosis (absolute difference: 13.1%; 95% CI 20.8–4.3, log rank p < 0.001; fully-adjusted hazard ratio (HR): 3.57; 95% CI 2.18–5.84). The probability of reporting incident psychosis in participants with stroke was 2.3% (95% CI 1.4–3.2) compared to 0.9% (95% CI 0.7–1.1) in those without (absolute difference: 1.4%; 95% CI 0.7–2.1, log rank p < 0.001; fully-adjusted HR: 4.98; 95% CI 2.55–9.72).

Stroke is an independent predictor of psychosis (and vice versa), after adjustment for potential confounders.

People with neuropsychiatric symptoms often experience delay in accurate diagnosis. Although cerebrospinal fluid neurofilament light (CSF NfL) shows promise in distinguishing neurodegenerative disorders (ND) from psychiatric disorders (PSY), its accuracy in a diagnostically challenging cohort longitudinally is unknown.

We collected longitudinal diagnostic information (mean = 36 months) from patients assessed at a neuropsychiatry service, categorising diagnoses as ND/mild cognitive impairment/other neurological disorders (ND/MCI/other) and PSY. We pre-specified NfL > 582 pg/mL as indicative of ND/MCI/other.

Diagnostic category changed from initial to final diagnosis for 23% (49/212) of patients. NfL predicted the final diagnostic category for 92% (22/24) of these and predicted final diagnostic category overall (ND/MCI/other vs. PSY) in 88% (187/212), compared to 77% (163/212) with clinical assessment alone.

CSF NfL improved diagnostic accuracy, with potential to have led to earlier, accurate diagnosis in a real-world setting using a pre-specified cut-off, adding weight to translation of NfL into clinical practice.

Both stroke and psychosis are independently associated with high levels of disability. However, psychosis in the context of stroke has been under-researched. To date, there are no general population studies on their joint prevalence and association.

To estimate the joint prevalence of stroke and psychosis and their statistical association using nationally representative psychiatric epidemiology studies from two high-income countries (the UK and the USA) and two middle-income countries (Chile and Colombia) and, subsequently, in a combined-countries data-set.

Prevalences were calculated with 95% confidence intervals. Statistical associations between stroke and psychosis and between stroke and psychotic symptoms were tested using regression models. Overall estimates were calculated using an individual participant level meta-analysis on the combined-countries data-set. The analysis is available online as a computational notebook.

The overall prevalence of probable psychosis in stroke was 3.81% (95% CI 2.34–5.82) and that of stroke in probable psychosis was 3.15% (95% CI 1.94–4.83). The odds ratio of the adjusted association between stroke and probable psychosis was 3.32 (95% CI 2.05–5.38). On the individual symptom level, paranoia, hallucinated voices and thought passivity delusion were associated with stroke in the unadjusted and adjusted analyses.

Rates of association between psychosis and stroke suggest there is likely to be a high clinical need group who are under-researched and may be poorly served by existing services.