A Food and Drug Administration (FDA) safety communication in August 2011 warned that citalopram was associated with a dose dependent risk of QT prolongation and recommended dose restriction in patients over the age of 60 but did not provide data for this age group.

Amiselimod (MT-1303) is a selective sphingosine 1-phosphate 1 (S1P1 ) receptor modulator which is currently being developed for the treatment of various autoimmune diseases. Unlike some other S1P receptor modulators, amiselimod seemed to show a favourable cardiac safety profile in preclinical, phase I and II studies. The aim of the current study was to characterize the cardiac effects of amiselimod by directly comparing it with fingolimod and placebo.

Deep brain stimulation (DBS) is used to modulate the activity of dysfunctional brain circuits. The safety and efficacy of DBS in dementia is unknown.

People with dementia (PWD) represent some of the highest-need and highest-cost individuals living in the community. Maximizing Independence (MIND) at Home is a potentially cost-effective and scalable home-based dementia care coordination program that uses trained, nonclinical community workers as the primary contact between the PWD and their care partner, supported by a multidisciplinary clinical team with expertise in dementia care.

To evaluate the impact of lasmiditan, an oral, centrally-penetrant, selective serotonin 1F (5-HT1F ) receptor agonist developed for the acute treatment of migraine, on simulated driving.

The recent advances in creating pluripotent stem cells from somatic cells and differentiating them into a variety of cell types is allowing us to study them without the caveats associated with disease-related changes. We generated induced Pluripotent Stem Cells (iPSCs) from eight Alzheimer's disease (AD) patients and six controls and used lentiviral delivery to differentiate them into excitatory glutamatergic neurons. We then performed RNA sequencing on these neurons and compared the Alzheimer's and control transcriptomes. We found that 621 genes show differences in expression levels at adjusted p < 0.05 between the case and control derived neurons. These genes show significant overlap and directional concordance with genes reported from a single-cell transcriptome study of AD patients; they include five genes implicated in AD from genome-wide association studies and they appear to be part of a larger functional network as indicated by an excess of interactions between them observed in the protein-protein interaction database STRING. Exploratory analysis with Uniform Manifold Approximation and Projection (UMAP) suggests distinct clusters of patients, based on gene expression, who may be clinically different. Our research outcomes will enable the precise identification of distinct biological subtypes among individuals with Alzheimer's disease, facilitating the implementation of tailored precision medicine strategies.

Alterations of the gray and white matter have been identified in Alzheimer's disease (AD) by structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). However, whether the combination of these modalities could increase the diagnostic performance is unknown.

This Phase I study aimed to assess the potential drug-drug interactions (pharmacokinetic [PK] and safety profile) of Δ9-tetrahydrocannabinol (THC)/cannabidiol (CBD) oromucosal spray (Sativex (®), nabiximols) in combination with cytochrome P450 (CYP450) inducer (rifampicin) or inhibitors (ketoconazole or omeprazole). Thirty-six healthy male subjects were divided into three groups of 12, and then randomized to one of two treatment sequences per group. Subjects received four sprays of THC/CBD (10.8/10 mg) alongside single doses of the CYP3A and 2C19 inducer rifampicin (600 mg), CYP3A inhibitor ketoconazole (400 mg) or CYP2C19 inhibitor omeprazole (40 mg). Plasma samples were analyzed for CBD, THC and its metabolite 11-hydroxy-THC (11-OH-THC). A single dose of four sprays of THC/CBD spray (10.8/10 mg) following repeated doses of rifampicin (600 mg) reduced the Cmax and AUC of all analytes. Cmax reduced from 2.94 to 1.88 ng/mL (-36%), 1.03 to 0.50 ng/mL (-52%) and 3.38 to 0.45 ng/mL (-87%) for THC, CBD and 11-OH-THC, respectively compared to single dose administration of THC/CBD spray alone. Ketoconazole co-administration with THC/CBD spray had the opposite effect, increasing the Cmax of the respective analytes from 2.65 to 3.36 ng/mL (+27%), 0.66 to 1.25 ng/mL (+89%) and 3.59 to 10.92 ng/mL (+204%). No significant deviations in Cmax or AUC for any analyte were observed when THC/CBD spray was co-administered with omeprazole. THC/CBD spray was well tolerated by the study subjects both alone and in combination with rifampicin, ketoconazole and omeprazole. Evaluation of the PKs of THC/CBD spray alone and in combination with CYP450 inhibitors/inducers suggests that all analytes are substrates for the isoenzyme CYP3A4, but not CYP2C19. On the basis of our findings, there is likely to be little impact on other drugs metabolized by CYP enzymes on the PK parameters of THC/CBD spray, but potential effects should be taken into consideration when co-administering THC/CBD spray with compounds which share the CYP3A4 pathway such as rifampicin or ketoconazole.

We aimed to develop a new method to convert T1-weighted brain MRIs to feature vectors, which could be used for content-based image retrieval (CBIR). To overcome the wide range of anatomical variability in clinical cases and the inconsistency of imaging protocols, we introduced the Gross feature recognition of Anatomical Images based on Atlas grid (GAIA), in which the local intensity alteration, caused by pathological (e.g., ischemia) or physiological (development and aging) intensity changes, as well as by atlas-image misregistration, is used to capture the anatomical features of target images. As a proof-of-concept, the GAIA was applied for pattern recognition of the neuroanatomical features of multiple stages of Alzheimer's disease, Huntington's disease, spinocerebellar ataxia type 6, and four subtypes of primary progressive aphasia. For each of these diseases, feature vectors based on a training dataset were applied to a test dataset to evaluate the accuracy of pattern recognition. The feature vectors extracted from the training dataset agreed well with the known pathological hallmarks of the selected neurodegenerative diseases. Overall, discriminant scores of the test images accurately categorized these test images to the correct disease categories. Images without typical disease-related anatomical features were misclassified. The proposed method is a promising method for image feature extraction based on disease-related anatomical features, which should enable users to submit a patient image and search past clinical cases with similar anatomical phenotypes.

For patients with cognitive disorders and dementia, accurate prognosis of cognitive worsening is critical to their ability to prepare for the future, in collaboration with health-care providers. Despite multiple efforts to apply computational brain magnetic resonance image (MRI) analysis in predicting cognitive worsening, with several successes, brain MRI is not routinely quantified in clinical settings to guide prognosis and clinical decision-making. To encourage the clinical use of a cutting-edge image segmentation method, we developed a prediction model as part of an established web-based cloud platform, MRICloud. The model was built in a training dataset from Alzheimer's Disease Neuroimaging Initiative (ADNI) where baseline MRI scans were combined with clinical data over time. Each MRI was parcellated into 265 anatomical units based on the MRICloud fully automated image segmentation function, to measure the volume of each parcel. The Mini Mental State Examination (MMSE) was used as a measure of cognitive function. The normalized volume of 265 parcels, combined with baseline MMSE score, age, and sex were input variables for a Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, with MMSE change in the subsequent two years as the target for prediction. A leave-one-out analysis performed on the training dataset estimated a correlation coefficient of 0.64 between true and predicted MMSE change. A receiver operating characteristic (ROC) analysis estimated a sensitivity of 0.88 and a specificity of 0.76 in predicting substantial cognitive worsening after two years, defined as MMSE decline of ≥4 points. This MRICloud prediction model was then applied to a test dataset of clinically acquired MRIs from the Johns Hopkins Memory and Alzheimer's Treatment Center (MATC), a clinical care setting. In the latter setting, the model had both sensitivity and specificity of 1.0 in predicting substantial cognitive worsening. While the MRICloud prediction model demonstrated promise as a platform on which computational MRI findings can easily be extended to clinical use, further study with a larger number of patients is needed for validation.

White matter disruption has been suggested as one of anatomical features associated with Alzheimer's disease (AD). Diffusion tensor imaging (DTI), which has been widely used in AD studies, obtains new insights into the white matter structure.

To examine whether associations between individual neuropsychiatric symptoms (NPS) and incident Alzheimer's dementia (AD) differ in men versus women.

There is strong association of Alzheimer's disease (AD) pathology with gait disorder and falls in older adults without dementia. The goal of the study was to examine the prevalence and severity of AD pathology in older adults without dementia who fall and sustain hip fracture.

There are growing concerns about potential delayed, neuropsychiatric consequences (e.g, cognitive decline, mood or anxiety disorders) of sports-related traumatic brain injury (TBI). Autopsy studies of brains from a limited number of former athletes have described characteristic, pathologic changes of chronic traumatic encephalopathy (CTE) leading to questions about the relationship between these pathologic and the neuropsychiatric disturbances seen in former athletes. Research in this area will depend on in vivo methods that characterize molecular changes in the brain, linking CTE and other sports-related pathologies with delayed emergence of neuropsychiatric symptoms. In this pilot project we studied former National Football League (NFL) players using new neuroimaging techniques and clinical measures of cognitive functioning. We hypothesized that former NFL players would show molecular and structural changes in medial temporal and parietal lobe structures as well as specific cognitive deficits, namely those of verbal learning and memory. We observed a significant increase in binding of [(11)C]DPA-713 to the translocator protein (TSPO), a marker of brain injury and repair, in several brain regions, such as the supramarginal gyrus and right amygdala, in 9 former NFL players compared to 9 age-matched, healthy controls. We also observed significant atrophy of the right hippocampus. Finally, we report that these same former players had varied performance on a test of verbal learning and memory, suggesting that these molecular and pathologic changes may play a role in cognitive decline. These results suggest that localized brain injury and repair, indicated by increased [(11)C]DPA-713 binding to TSPO, may be linked to history of NFL play. [(11)C]DPA-713 PET is a promising new tool that can be used in future study design to examine further the relationship between TSPO expression in brain injury and repair, selective regional brain atrophy, and the potential link to deficits in verbal learning and memory after NFL play.

Alzheimer's disease (AD) is associated with variable cognitive and functional decline, and it is difficult to predict who will develop the disease and how they will progress.

In this study we compared Pittsburgh compound-B (PIB) positron emission tomography (PET) amyloid imaging, fluorodeoxyglucose PET for metabolism, and magnetic resonance imaging (MRI) for structure to predict conversion from amnestic mild cognitive impairment (MCI) to Alzheimer's dementia using data from the Alzheimer's Disease Neuroimaging Initiative cohort. Numeric neuroimaging variables generated by the Alzheimer's Disease Neuroimaging Initiative-funded laboratories for each neuroimaging modality along with apolipoprotein-E genotype (n = 29) were analyzed. Performance of these biomarkers for predicting conversion from MCI to Alzheimer's dementia at 2 years was evaluated in 50 late amnestic MCI subjects, 20 of whom converted. Multivariate modeling found that among individual modalities, MRI had the highest predictive accuracy (67%) which increased by 9% to 76% when combined with PIB-PET, producing the highest accuracy among any biomarker combination. Individually, PIB-PET generated the best sensitivity, and fluorodeoxyglucose PET had the lowest. Among individual brain regions, the temporal cortex was found to be most predictive for MRI and PIB-PET.

The purpose of this paper is to establish single-participant white matter atlases based on diffusion tensor imaging. As one of the applications of the atlas, automated brain segmentation was performed and the accuracy was measured using Large Deformation Diffeomorphic Metric Mapping (LDDMM). High-quality diffusion tensor imaging (DTI) data from a single-participant were B0-distortion-corrected and transformed to the ICBM-152 atlas or to Talairach coordinates. The deep white matter structures, which have been previously well documented and clearly identified by DTI, were manually segmented. The superficial white matter areas beneath the cortex were defined, based on a population-averaged white matter probability map. The white matter was parcellated into 176 regions based on the anatomical labeling in the ICBM-DTI-81 atlas. The automated parcellation was achieved by warping this parcellation map to normal controls and to Alzheimer's disease patients with severe anatomical atrophy. The parcellation accuracy was measured by a kappa analysis between the automated and manual parcellation at 11 anatomical regions. The kappa values were 0.70 for both normal controls and patients while the inter-rater reproducibility was 0.81 (controls) and 0.82 (patients), suggesting "almost perfect" agreement. A power analysis suggested that the proposed method is suitable for detecting FA and size abnormalities of the white matter in clinical studies.

Agitation is one of the most eminent characteristics of neuropsychiatric symptoms (NPS) affecting people living with Alzheimer's and Dementia and has serious consequences for patients and caregivers. The current consensus is that agitation results, in part, from the disruption of ascending monoamine regulators of cortical circuits, especially the loss of serotonergic activity. It is believed that the first line of treatment for these conditions is selective serotonin reuptake inhibitors (SSRIs), but these are effective in only about 40% of patients. Person-specific biomarkers, for example, ones based on in vitro iPSC-derived models of serotonin activity, which predict who with Agitation responds to an SSRI, are a major clinical priority. Here, we report the generation of human-induced pluripotent stem cells (iPSCs) from a 74-year-old AD patient, the homozygous APOE ε4/ε4 carrier, who developed Agitation. His iPSCs were reprogrammed from peripheral blood mononuclear cells (PBMCs) using the transient expression of pluripotency genes. These display typical iPSC characteristics that are karyotypically normal and attain the capacity to differentiate into three germ layers. The newly patient-derived iPSC line offers a unique resource to investigate the underlying mechanisms associated with neuropsychiatric symptom progression in AD.

The Montreal Cognitive Assessment (MoCA) was developed to enable earlier detection of mild cognitive impairment (MCI) relative to familiar multi-domain tests like the Mini-Mental State Exam (MMSE). Clinicians need to better understand the relationship between MoCA and MMSE scores.

Lasmiditan is a centrally penetrant, highly selective 5-hydroxytryptamine (serotonin) receptor 1F (5HT1F ) agonist under development as a novel therapy for acute treatment of migraine. A phase 1 randomized, placebo- and positive-controlled crossover study assessed the abuse potential of lasmiditan in adult recreational polydrug users. Following a qualification phase, subjects were randomized into treatment sequences, each consisting of 5 study treatments: placebo, alprazolam 2 mg, lasmiditan 100, 200 (lasmiditan 100 and 200 mg are proposed therapeutic doses), and 400 mg (supratherapeutic). The abuse potential of lasmiditan was investigated and compared with alprazolam and with placebo using the maximal effect score (Emax ) of the Drug-Liking Visual Analog Scale as the primary end point. Lasmiditan was not similar to placebo in drug-liking scores at all doses tested, with a maximum difference observed with the lasmiditan 400-mg dose (upper 90% confidence limit on difference in least-squares [LS] means > 14 for all lasmiditan doses). Drug-liking scores for lasmiditan 400 mg were not significantly different from alprazolam (lower 90% confidence limit on difference in LS means < 5), but drug-liking scores at lower doses (100 and 200 mg) were significantly different from alprazolam. During the treatment phase, the incidence of treatment-emergent adverse events (TEAEs) increased with increasing dose of lasmiditan; all TEAEs reported with lasmiditan treatment were mild. Subjective drug-liking effects for lasmiditan versus placebo and versus alprazolam, and the safety and tolerability profile of lasmiditan suggest that lasmiditan has a low potential for abuse.