Brain Imaging Biomarkers Research Articles

Psychotropic Polypharmacy Leading to Reversible Dementia: A Case Report.

Psychotropic polypharmacy presents a diagnostic challenge that may be further complicated by inadequate medication history and underappreciation of the cognitive effects of such polypharmacy. Here we present the case of a 57-year-old man who presented to our memory clinic with progressive cognitive decline and a prior neuropsychological evaluation supporting the diagnosis of a neurodegenerative disorder. He was taking multiple psychotropic medications at the time, but the exact dosages were unclear due to a lack of collateral history. He was also taking prescribed opioids and a combination of buprenorphine and naloxone for pain relief, again with unclear dosages at the time of presentation. Brain imaging and cerebrospinal spinal fluid biomarker testing were negative for Alzheimer pathophysiologic processes. Months later, the patient was taken to the emergency room after an overdose caused by overuse of opioid medications. Once he was taken off all psychoactive medications, the patient's cognitive impairment completely reversed, and he became independent in activities of daily living. Psychotropic polypharmacy can have a myriad of cognitive manifestations which need to be better recognized by clinicians. Deprescription of such medications should be attempted whenever clinically appropriate.

Identification of multimodal brain imaging biomarkers in first-episode drugs-naive major depressive disorder through a multi-site large-scale MRI consortium data

BackgroundMajor depressive disorder (MDD) is a severe and common mental illness. The first-episode drugs-naive MDD (FEDN-MDD) patients, who have not undergone medication intervention, contribute to understanding the biological basis of MDD. Multimodal Magnetic Resonance Imaging can provide a comprehensive understanding of brain functional and structural abnormalities in MDD. However, most MDD studies use single-modal, small-scale MRI data. And several multimodal studies of MDD are limited to simple linear combinations of functional and structural features. MethodsWe screened a large sample of FEDN-MDD patients and healthy controlsmultimodal MRI data. Extracting the fractional amplitude of low-frequency fluctuations (fALFF) feature from functional magnetic resonance imaging and the gray matter volume (GMV) feature from structural magnetic resonance imaging. The mCCA-jICA method was used to integrate these two modal features to investigate the functional-structural co-variation abnormalities in MDD. To validate the stability of the extracted functional-structural covariant abnormalities features, we apply them to identify FEDN-MDD patients. ResultsThe results show that compared to healthy controls, FEDN-MDD patients exhibit joint group-discriminative independent component and modality-specific group-discriminative independent component, suggesting functional-structural covariant abnormalities in MDD patients. Using lightGBM classifier, we achieve a classification accuracy of 99.84 %. LimitationWe use GMV and fALFF for multimodal fusion shows promise, but requires further validation with other datasets and exploration of additional multimodal features. ConclusionsThis may indicate that multimodal fusion features can effectively explore information between different modalities and can accurately identify FEDN-MDD patients, suggesting their potential as multimodal brain imaging biomarkers for MDD.

Predicting regional tau accumulation with machine learning‐based tau‐PET and advanced radiomics

AbstractINTRODUCTIONAlzheimer's disease is partially characterized by the progressive accumulation of aggregated tau‐containing neurofibrillary tangles. Although the association between accumulated tau, neurodegeneration, and cognitive decline is critical for disease understanding and clinical trial design, we still lack robust tools to predict individualized trajectories of tau accumulation. Our objective was to assess whether brain imaging biomarkers of flortaucipir‐positron emission tomography (PET), in combination with clinical and genomic measures, could predict future pathological tau accumulation.METHODSWe quantified the disease profile of participants (N = 276) using a comprehensive set of descriptors, including clinical/demographic (age, diagnosis, amyloid status, sex, race, ethnicity), genetic (apolipoprotein E [APOE]‐ε4), and flortaucipir‐PET imaging measures (regional flortaucipir standardized uptake value ratio [SUVr] and comprehensive radiomic texture features extracted from Automated Anatomical Labeling template regions). We trained an AdaBoost machine learning algorithm in a 2:1 split train‐test configuration to derive a prognostic index that (i) stratifies individualized brain regions including global (AD‐signature region) and lobar regions (frontal, occipital, parietal, temporal) into stable/slow‐ and fast‐progressors based on future tau accumulation, and (ii) forecasts individualized regional annualized‐rate‐of‐change in flortaucipir‐PET SUVr. Further, we developed an adaptive model incorporating flortaucipir‐PET measurements from the baseline and intermediate timepoints to predict annualized‐rate‐of‐change.RESULTSIn binary classification for predicting stable/slow‐ versus fast‐progressors, the area‐under‐the‐receiver‐operating‐characteristic curve was 0.86 in the AD‐signature region and 0.83, 0.82, 0.84, and 0.83 in frontal, occipital, parietal, and temporal regions, respectively. The trained models successfully predicted annualized‐rate‐of‐change of flortaucipir‐PET regional flortaucipir SUVr in AD‐signature and lobar regions (Pearson‐correlation [R]: AD‐signature = 0.73; frontal = 0.73; occipital = 0.71; parietal = 0.70; temporal = 0.69). The models’ performance in predicting annualized‐rate‐of‐change slightly increased when imaging features from intermediate timepoints were used in the adaptive setting (R: AD‐signature = 0.79; frontal = 0.87; occipital = 0.83; parietal = 0.74; temporal = 0.82).DISCUSSIONTaken together, our results propose a robust approach to predict future tau accumulation that may improve the ability to enroll, stratify, and gauge efficacy in clinical trial participants.Highlights Machine learning predicts the future rate of tau accumulation in Alzheimer's disease. Tau prediction in lobar/global regions benefits from diverse multimodal features. This prognostic index can serve as a sensitive tool for patient stratification.

The contribution of white matter changes to clinical phenotype in progressive supranuclear palsy

White matter hyperintensities (WMH) are considered magnetic brain imaging (MRI) biomarkers of cerebral small vessel disease but their clinical role in neurodegenerative-related disorders is poorly understood. This study describes the distribution of WMH on brain MRI in Progressive Supranuclear Palsy (PSP) in comparison with Parkinson’s disease (PD) and explores their possible impact on disease’s features. Sixty PSP and 33 PD patients were included. Motor symptoms, cardiovascular risk factors and the age-related white matter changes (ARWMC) score was computed to rate WMH for both groups. Pearson’s correlation and linear or logistic regression analysis were used to check for relationships between ARWMC and PSP clinical scores. The mean (standard deviation) ARWMC total score in the PSP cohort was 4.66 (3.25). Any degree of WMH was present in 68% of PSP (ARWMC +). Compared to ARWMC-, ARWMC + did not have greater disease severity or more cardiovascular risk factors. WMH were frequently localized in fronto-parietal lobes and were mild in severity. Linear regression analysis showed that ARWMC total score was related to the PSP-rating scale, irrespective of age, disease duration and the Charlson modified comorbidity index. Logistic regression analysis confirmed that ARWMC total score was related to the use of wheelchair, irrespective of above-mentioned covariates. Vascular risk factors as well as severity and distribution of WMH did not have an impact on the PSP phenotype. No differences were found with PD patients. Our results suggest that WMH in PSP might be markers of neurodegenerative-related pathology rather than being simple expression of atherosclerotic cerebrovascular changes.

Dynamic undirected graphical models for time-varying clinical symptom and neuroimaging networks.

In this work, we propose methods to examine how the complex interrelationships between clinical symptoms and, separately, brain imaging biomarkers change over time leading up to the diagnosis of a disease in subjects with a known genetic near-certainty of disease. We propose a time-dependent undirected graphical model that ensures temporal and structural smoothness across time-specific networks to examine the trajectories of interactions between markers aligned at the time of disease onset. Specifically, we anchor subjects relative to the time of disease diagnosis (anchoring time) as in a revival process, and we estimate networks at each time point of interest relative to the anchoring time. To use all available data, we apply kernel weights to borrow information across observations that are close to the time of interest. Adaptive lasso weights are introduced to encourage temporal smoothness in edge strength, while a novel elastic fused- penalty removes spurious edges and encourages temporal smoothness in network structure. Our approach can handle practical complications such as unbalanced visit times. We conduct simulation studies to compare our approach with existing methods. We then apply our method to data from PREDICT-HD, a large prospective observational study of pre-manifest Huntington's disease (HD) patients, to identify symptom and imaging network changes that precede clinical diagnosis of HD.

Optic nerve compression associated with visual cortex functional alteration in dysthyroid optic neuropathy: A combined orbital and brain imaging study

AbstractAimsTo investigate the alterations of the optic nerve and visual cortex in dysthyroid optic neuropathy (DON), a subgroup of thyroid eye disease (TED).MethodsMultiple orbital imaging biomarkers related to optic nerve compression and the amplitude of low‐frequency fluctuations (ALFF) of the brain were obtained from 47 patients with DON, 56 TED patients without DON (nDON), and 37 healthy controls (HC). Correlation analyses and diagnostic tests were implemented.ResultsCompared with HC, the nDON group showed alterations in orbital imaging biomarkers related to optic nerve compression in posterior segments, as well as ALFF of the right inferior temporal gyrus and left fusiform gyrus. DON differed from nDON group mainly in the modified muscle index of the posterior segment of optic nerve, and ALFF of orbital part of right superior frontal gyrus, right hippocampus, and right superior temporal gyrus. Orbital and brain imaging biomarkers were significantly correlated with each other. Diagnostic models attained an area under a curve of 0.80 for the detection of DON.ConclusionThe combined orbital and brain imaging study revealed alterations of the visual pathway in patients with TED and DON as well as provided diagnostic value. The initiation of alterations in the visual cortex in TED may precede the onset of DON.

Resting-state brain plasticity is associated with the severity in cervical spondylotic myelopathy

ObjectiveTo investigate the brain mechanism of non-correspondence between imaging presentations and clinical symptoms in cervical spondylotic myelopathy (CSM) patients and to test the utility of brain imaging biomarkers for predicting prognosis of CSM.MethodsForty patients with CSM (22 mild-moderate CSM, 18 severe CSM) and 25 healthy controls (HCs) were recruited for rs-fMRI and cervical spinal cord diffusion tensor imaging (DTI) scans. DTI at the spinal cord (level C2/3) with fractional anisotropy (FA) and degree centrality (DC) were recorded. Then one-way analysis of covariance (ANCOVA) was conducted to detect the group differences in the DC and FA values across the three groups. Pearson correlation analysis was then separately performed between JOA with FA and DC.ResultsAmong them, degree centrality value of left middle temporal gyrus exhibited a progressive increase in CSM groups compared with HCs, the DC value in severe CSM group was higher compared with mild-moderate CSM group. (P < 0.05), and the DC values of the right superior temporal gyrus and precuneus showed a decrease after increase. Among them, DC values in the area of precuneus in severe CSM group were significantly lower than those in mild-moderate CSM and HCs. (P < 0.05). The fractional anisotropy (FA) values of the level C2/3 showed a progressive decrease in different clinical stages, that severe CSM group was the lowest, significantly lower than those in mild-moderate CSM and HCs (P < 0.05). There was negative correlation between DC value of left middle temporal gyrus and JOA scores (P < 0.001), and the FA values of dorsal column in the level C2/3 positively correlated with the JOA scores (P < 0.001).ConclusionStructural and functional changes have taken place in the cervical spinal cord and brain of CSM patients. The Brain reorganization plays an important role in maintaining the symptoms and signs of CSM, aberrant DC values in the left middle temporal gyrus may be the possible mechanism of inconsistency between imaging findings and clinical symptoms. Degree centrality is a potentially useful prognostic functional biomarker in cervical spondylotic myelopathy.

A Strategy for Allowing Earlier Diagnosis and Rigorous Evaluation of BACE1 Inhibitors in Preclinical Alzheimer's Disease.

Given continued failure of BACE1 inhibitor programs at symptomatic and prodromal stages of Alzheimer's disease (AD), clinical trials need to target the earlier preclinical stage. However, trial design is complex in this population with negative diagnosis of classical hippocampal amnesia on standard memory tests. Besides recent advances in brain imaging, electroencephalogram, and fluid-based biomarkers, new cognitive markers should be established for earlier diagnosis that can optimize recruitment to BACE1 inhibitor trials in presymptomatic AD. Notably, accelerated long-term forgetting (ALF) is emerging as a sensitive cognitive measure that can discriminate between asymptomatic individuals with high risks for developing AD and healthy controls. ALF is a form of declarative memory impairment characterized by increased forgetting rates over longer delays (days to months) despite normal storage within the standard delays of testing (20-60 min). Therefore, ALF may represent a harbinger of preclinical dementia and the impairment of systems memory consolidation, during which memory traces temporarily stored in the hippocampus become gradually integrated into cortical networks. This review provides an overview of the utility of ALF in a rational design of next-generation BACE1 inhibitor trials in preclinical AD. I explore potential mechanisms underlying ALF and relevant early-stage biomarkers useful for BACE1 inhibitor evaluation, including synaptic protein alterations, astrocytic dysregulation and neuron hyperactivity in the hippocampal-cortical network. Furthermore, given the physiological role of the isoform BACE2 as an AD-suppressor gene, I also discuss the possible association between the poor selectivity of BACE1 inhibitors and their side effects (e.g., cognitive worsening) in prior clinical trials.

Correlations of Plasma Biomarkers and Imaging Characteristics of Cerebral Small Vessel Disease.

Cerebral small vessel disease (CSVD), which is a group of pathological processes affecting cerebral microvessels, leads to functional loss in the elderly population and mostly presents as cognitive impairment and gait decline. CSVD is diagnosed based on brain imaging biomarkers, but blood biomarkers are of great significance for the early diagnosis and progression prediction of CSVD and have become a research focus because of their noninvasiveness and easy accessibility. Notably, many blood biomarkers have been reported to be associated with CSVD in a relatively large population, particularly serum neurofilament light chain (NfL), which has been regarded as a promising biomarker to track the variation trend in WMH and to predict the further status of white matter hyperintensities (WMH) and lacunar infarcts. And neuro-glio-vascular unit structure and blood-brain barrier function have been proposed as underlying mechanisms of CSVD. The article starts from the neuroimaging markers of CSVD, including recent small subcortical infarcts (RSSI), white matter hyperintensities (WMH), lacunes, cerebral microbleeds (CMB), enlarged perivascular spaces (EPVS), cerebral atrophy, and the combined small vessel disease score, and attempts to systematically review and summarize the research progress regarding the blood biomarkers of CSVD that form the changes in the neuro-glio-vascular unit structure and blood-brain barrier function.

Systematic reduction of gray matter volume in anorexia nervosa, but relative enlargement with clinical symptoms in the prefrontal and posterior insular cortices: a multicenter neuroimaging study

Although brain morphological abnormalities have been reported in anorexia nervosa (AN), the reliability and reproducibility of previous studies were limited due to insufficient sample sizes, which prevented exploratory analysis of the whole brain as opposed to regions of interest (ROIs). Objective was to identify brain morphological abnormalities in AN and the association with severity of AN by brain structural magnetic resonance imaging (MRI) in a multicenter study, and to conduct exploratory analysis of the whole brain. Here, we conducted a cross-sectional multicenter study using T1-weighted imaging (T1WI) data collected between May 2014 and February 2019 in Japan. We analyzed MRI data from 103 female AN patients (58 anorexia nervosa restricting type [ANR] and 45 anorexia nervosa binge-purging type [ANBP]) and 102 age-matched female healthy controls (HC). MRI data from five centers were preprocessed using the latest harmonization method to correct for intercenter differences. Gray matter volume (GMV) was calculated from T1WI data of all participants. Of the 205 participants, we obtained severity of eating disorder symptom scores from 179 participants, including 87 in the AN group (51 ANR, 36 ANBP) and 92 HC using the Eating Disorder Examination Questionnaire (EDE-Q) 6.0. GMV reduction were observed in the AN brain, including the bilateral cerebellum, middle and posterior cingulate gyrus, supplementary motor cortex, precentral gyrus medial segment, and thalamus. In addition, the orbitofrontal cortex (OFC), ventromedial prefrontal cortex (vmPFC), rostral anterior cingulate cortex (ACC), and posterior insula volumes showed positive correlations with severity of symptoms. This multicenter study was conducted with a large sample size to identify brain morphological abnormalities in AN. The findings provide a better understanding of the pathogenesis of AN and have potential for the development of brain imaging biomarkers of AN. Trial Registration: UMIN000017456. https://center6.umin.ac.jp/cgi-open-bin/icdr/ctr_view.cgi?recptno=R000019303.

Association between brain imaging biomarkers and continuous glucose monitoring-derived glycemic control indices in Japanese patients with type 2 diabetes mellitus

IntroductionAlthough type 2 diabetes mellitus (T2DM) is associated with alterations in brain structure, the relationship between glycemic control indices and brain imaging markers remains unclear. This study aimed to investigate...

Structural Brain Imaging Biomarkers of Autism Spectrum Disorder.

Since the early 1990s, there have literally been thousands of reports related to magnetic resonance imaging of the autistic brain. The goals of these studies have ranged from identifying the earliest biological predictors of an autistic diagnosis to determining brain systems most altered in autistic individuals. Some of the later works attempt to use distinct patterns of brain alterations to help define more homogenous subtypes of autism. Far less work has been done to identify brain changes that are associated with therapeutic interventions. In this chapter, we will touch on all of these efforts as they relate to the general topic of the usefulness of brain imaging as a biomarker of autism.

Diagnosis patterns among diverse populations with mild cognitive impairment and Alzheimer’s disease: A large nationwide electronic health record cohort study

AbstractBackgroundWith recent advances and emerging research on diagnostic biomarkers and evolving diagnostic criteria, practitioners managing patients with cognitive complaints operate within a complex, changing environment. Although some evidence is available on the importance of prompt diagnosis, little information exists on the diagnostic process used in real‐world settings among diverse populations. This study’s objective was to characterize diagnosis patterns among adults with mild cognitive impairment (MCI), Alzheimer’s disease (AD), and/or dementia in the United States, and stratify by gender, age and race/ethnicity.MethodThis retrospective observational cohort study utilized the Optum® Market Clarity database from January 1, 2017–September 30, 2021. Eligible individuals had ≥1 diagnosis of MCI, AD, and/or dementia in their electronic health records or ≥2 diagnosis codes separated by ≥30 days in claims data, had ≥12 months of continuous enrollment before the cohort entry date, and had ≥1 day of continuous healthcare plan enrollment after the index date. Demographic information and the occurrence of diagnostic tests were also obtained from electronic medical records and insurance claims. Descriptive statistics were calculated and stratified by demographic factors.ResultA total of 179,419 newly diagnosed MCI cases, 81,267 AD cases, and 338,739 dementia cases were identified. The mean age at MCI, AD, or dementia diagnosis was similar overall for males and females. Among Caucasians, the mean age of MCI diagnosis was 70.5±14.8 for females and 69.8±14.5 for males. Among African Americans, the mean age at MCI diagnosis was 66.3±15.4 for females and 63.6±16.1 for males. PET scans and CSF biomarker screening occurred less often than MRI and CT (MCI: PET 0.6%, CSF 1.6%, MRI 16.6%, CT 17.5%; AD: PET 0.5%; CSF 0.9%; MRI 9.0%; CT 18.4%; dementia: PET 0.2%; CSF 1.6%; MRI 9.5%; CT 25.9%).ConclusionThe mean age at MCI, AD, and dementia diagnosis varied by demographic groups. The diagnosis of MCI, AD, and dementia remains largely clinical, with low use of brain imaging or CSF biomarkers. This knowledge of the patient populations and diagnostic experiences in a real‐world setting allows for greater understanding and growth to improve diagnosis across diverse groups and lead to faster support.

Effects of de‐facing T1 MRI, FLAIR MRI, Amyloid PET, and Tau PET scans on imaging‐clinical correlations analyses

AbstractBackgroundAutomated face recognition can potentially re‐identify de‐identified research brain MRI, PET, and CT, and direct differences of brain measurements from original and de‐faced images are statistically significant but very minor, i.e. below scan‐rescan differences. Their effects on analyses correlating brain imaging biomarkers with clinical variables remain unknown.MethodWe sampled 3 age‐and‐sex‐matched groups (CU, MCI, and clinical AD) from Mayo Clinic aging studies, each with 61 participants with same‐visit 3D T1‐weighted and 3D T2‐FLAIR MRI, PiB amyloid PET, and Flortaucipir tau PET (total n = 183). We automatically measured hippocampal volume, PIB global SUVR, and tau temporal meta‐ROI SUVR using a) our in‐house pipeline with SPM12, MCALT, and ANTs, and b) FreeSurfer 7.3.2 (PETSurfer), and we measured voxels associated with clinical group separation using SPM12. We also measured WMH from FLAIR using a) our in‐house pipeline, b) Lesion Segmentation Tool (LST), and c) FreeSurfer’s Samseg. For each measurement, we calculated pair‐wise group separation AUC values and Spearman’s rho with age and CDR‐SOB. We then de‐faced all PET and MRI using mri_reface, our top‐performing automated de‐facing software that replaces imaged faces with an average face (“re‐facing”), and we repeated all measurements and analyses with de‐faced images. We then compared the magnitudes of clinical correlations with original images and those of de‐faced images.ResultAnalyses of original and de‐faced images had very high agreement. Only 2/55 paired comparisons were significant (Tables 1‐2): FreeSurfer‐measured global PIB SUVR from de‐faced images was more correlated with age (rho 0.003 vs ‐0.227, p = 0.046), and Mayo‐measured WMH volume from original images was more correlated with CDR‐SOB (rho 0.321 vs 0.308, p = 0.020). Another comparison‐pair tested p = 0.041 for an AUC change +0.008, which we consider not practically meaningful. All others had p&gt;0.05. In voxel‐based analyses of CU vs AD participants, all comparisons were qualitatively similar between original and de‐faced variants (Figure 1).ConclusionDe‐facing PET and MRI using mri_reface did not significantly affect 53/55 correlations between imaging and clinical variables; of the two affected, one became stronger with re‐faced images. Stronger correlations with clinical variables may be explained by de‐facing’s standardizing face voxels and artifacts that irrelevantly affect brain segmentations.

Topological properties analysis and identification of mild cognitive impairment based on individual morphological brain network connectome.

Mild cognitive impairment is considered the prodromal stage of Alzheimer's disease. Accurate diagnosis and the exploration of the pathological mechanism of mild cognitive impairment are extremely valuable for targeted Alzheimer's disease prevention and early intervention. In all, 100 mild cognitive impairment patients and 86 normal controls were recruited in this study. We innovatively constructed the individual morphological brain networks and derived multiple brain connectome features based on 3D-T1 structural magnetic resonance imaging with the Jensen-Shannon divergence similarity estimation method. Our results showed that the most distinguishing morphological brain connectome features in mild cognitive impairment patients were consensus connections and nodal graph metrics, mainly located in the frontal, occipital, limbic lobes, and subcortical gray matter nuclei, corresponding to the default mode network. Topological properties analysis revealed that mild cognitive impairment patients exhibited compensatory changes in the frontal lobe, while abnormal cortical-subcortical circuits associated with cognition were present. Moreover, the combination of multidimensional brain connectome features using multiple kernel-support vector machine achieved the best classification performance in distinguishing mild cognitive impairment patients and normal controls, with an accuracy of 84.21%. Therefore, our findings are of significant importance for developing potential brain imaging biomarkers for early detection of Alzheimer's disease and understanding the neuroimaging mechanisms of the disease.

16 Longitudinal Cognitive Functioning in Gulf War veterans with and without Gulf War Illness: Data Mining from the BBRAIN Repository

Objective:Veterans from the 1991 Gulf War (GW) experienced several neurotoxicant exposures, including chemical weapons, pesticide sprays and creams, oil well fires and pyridostigmine bromide anti-nerve gas pills during the war. Research has shown these exposures to affect cognition and mood. Moreover, MR diffusion imaging has shown microstructural changes in the white matter that may be related to psychomotor slowing. Over a third of all GW veterans suffer from a chronic multi-symptom disorder called Gulf War Illness (GWI). The Kansas Criteria for GWI consists of six distinct criteria including symptoms of fatigue/sleep problems, pain symptoms, neurologic/cognitive/mood symptoms, gastrointestinal symptoms, respiratory symptoms, and skin symptoms. The Boston Gulf War Illness Consortium (GWIC) was a multi-site study designed to assess symptoms of GWI. After the conclusion of the GWIC study, the Boston Biorepository Recruitment and Integrative Network for Gulf War Illness (BBRAIN) was developed to harmonize retrospectively collected GW Veteran data while simultaneously collecting Time 2 data and samples from GW veterans who participated in the original study. This analysis includes the first 58 participants who have completed the GWIC study and the BBRAIN study.Participants and Methods:We conducted a longitudinal analysis of cognitive outcomes from the BBRAIN data repository. Verbal learning, memory, attention, and executive functioning were assessed using neuropsychological tests including the Continuous Performance Test (CPT3), Trail Making Test A, Delis-Kaplan Executive Function System (DKEFS), California Verbal Learning Test (CVLT-II). A total of 58 participants were re-evaluated from the original GWIC cohort with a total of 47 cases and 11 controls. Paired t-tests for the cognitive measures were completed separately for GWI cases and healthy GW veteran controls for each of the neuropsychological test measures. Average time between assessments was four years.Results:The overall sample was on average 56 years old, 84% male and 75% White. The average level of education was 15 years. GWI cases showed significantly more commission errors and slower reaction times on the CPT3 at Time 2 compared to Time 1 (p &lt; 0.05). Cases also showed a slowing in time to completion on Trails A at the second time point (p&lt;0.05). On the other hand, controls only showed significantly slower reaction times on the CPT3 at Time 2 (p&lt;0.05).Conclusions:These results showed that veterans with GWI are showing more decline over time in cognitive functioning particularly on psychomotor slowing and impulsivity than control veterans. It is important to document illness trajectories for veterans with GWI in order to devise strategies for interventions and treatments. The importance of studying longitudinal cohorts is to document changes in the same individuals over time. The next steps are to assess if this accelerated aging develops into neurodegenerative conditions by using brain imaging and other biomarkers in addition to cognitive evaluations. This could identify individuals who should be the focus of targeted treatment strategies while there is still time to intervene.

IC3 protocol: a longitudinal observational study of cognition after stroke using novel digital health technology

IntroductionStroke is a major cause of death and disability worldwide, frequently resulting in persistent cognitive deficits among survivors. These deficits negatively impact recovery and therapy engagement, and their treatment is...

Role of Brain Imaging in the Prediction of Intracerebral Hemorrhage Following Endovascular Therapy for Acute Stroke.

Currently most acute ischemic stroke patients presenting with a large vessel occlusion are treated with endovascular therapy (EVT), which results in high rates of successful recanalization. Despite this success, more than half of EVT-treated patients are significantly disabled 3 months later partly due to the occurrence of post-EVT intracerebral hemorrhage. Predicting post-EVT intracerebral hemorrhage is important for individualizing treatment strategies in clinical practice (eg, safe initiation of early antithrombotic therapies), as well as in selecting the optimal candidates for clinical trials that aim to reduce this deleterious outcome. Emerging data suggest that brain and vascular imaging biomarkers may be particularly relevant since they provide insights into the ongoing acute stroke pathophysiology. In this review/perspective, we summarize the accumulating literature on the role of cerebrovascular imaging biomarkers in predicting post-EVT-associated intracerebral hemorrhage. We focus on imaging acquired before EVT, during the EVT procedure, and in the early post-EVT time frames when new therapeutic therapies could be tested. Accounting for the complex pathophysiology of post-EVT-associated intracerebral hemorrhage, this review may provide some guidance for future prospective observational or therapeutic studies.

Baseline Findings of PreventE4: A Double-Blind Placebo Controlled Clinical Trial Testing High Dose DHA in APOE4 Carriers before the Onset of Dementia

IntroductionLower blood levels of the omega-3 polyunsaturated fatty acid docosahexaenoic acid (DHA) are correlated with worse cognitive functions, particularly among APOE ε4 carriers. Whether DHA supplementation in APOE ε4 carriers with limited DHA consumption and dementia risk factors can delay or slow down disease progression when started before the onset of clinical dementia is not known.MethodsPreventE4 is a double-blind, single site, randomized, placebo-controlled trial in cognitively unimpaired individuals with limited omega-3 consumption and dementia risk factors (n=368). Its objectives are to determine (1) whether carrying the APOE ε4 allele is associated with lower delivery of DHA to the brain; and (2) whether high dose DHA supplementation affects brain imaging biomarkers of AD and cognitive function.Results365 cognitively unimpaired individuals between 55 and 80 (mean age 66) were randomized to 2 grams of DHA per day or identically appearing placebo for a period of 2 years. Half the participants were asked to complete lumbar punctures at baseline and 6-month visits to obtain cerebrospinal fluid (CSF). The primary trial outcome measure is the change in CSF DHA to arachidonic acid ratio after 6 months of the intervention (n=181). Secondary trial outcomes include the change in functional and structural connectivity using resting state functional MRI at 24 months (n=365). Exploratory outcomes include the change in Repeatable Battery of the Assessment of Neuropsychological Status at 24 months (n=365).ConclusionsFindings from PreventE4 will clarify the brain delivery of DHA in individuals carrying the APOE ε4 allele with implications for dementia prevention strategies. Trial was registered as NCT03613844.

P58 SCREENING FOR SUBCLINICAL ATRIAL FIBRILLATION AFTER EMBOLIC STROKE OF UNDETERMINED SOURCE: A SINGLE CENTRE EXPERIENCE

Abstract Introduction Embolic stroke of undetermined source (ESUS) describes a stroke for which no cause has been found, which implicates embolism as a cause of stroke. There are several mechanisms of cryptogenic ischemic stroke including a covert structural cardiac lesion, paroxysmal atrial fibrillation (AF), hypercoagulable state or undiagnosed malignancy. As ESUS is associated with a significant stroke recurrence, a clear risk prediction and management is of utmost importance to improve prognosis. In ESUS, the options for further investigation include long–term cardiac monitoring, transesophageal echocardiography, investigation for occult malignancy or arterial hypercoagulability. Implantable cardiac monitors offer the capability to maximize the chances to detect AF and should be considered after non–invasive monitoring in patients with previous stroke at higher probability of having AF. Current guidelines do not suggest a specified approach (ie, blood, echocardiographic, ECG or brain imaging biomarkers) to screen AF in ESUS patients. Methods We enrolled consecutive patients with ESUS admitted to our Stroke Unit from January 2021 to October 2022. Results In the study period, 509 patients with acute ischemic stroke were admitted in our Stroke Unit. ESUS was diagnosed in 10.6% of patients (54/509). Only 12 of these patients underwent loop recorder implantation (mean age 76.5 y, 75% male), according to clinician judgment. AF was detected in 16.6% of these patients (2/12). Conclusions AF is one of the causes of ESUS; a recognition of AF in these patients is important, because its treatment can modify the risk of recurrence. A tailored diagnostic pathway, including clinical and echocardiographic parameters, is advisable to improve the recognition of this arrhythmia in ESUS patients.