Neuroimaging Profile Research Articles

Abstract 126: Genetic Analyses Of Oral Health And Neuroimaging Markers Of Brain Health In Persons Without Stroke

Background: Oral health is a modifiable risk factor for stroke. However, the role of oral health in the brain health of clinically asymptomatic persons remains understudied. We hypothesize that genetically-determined poor oral health leads to worse neuroimaging brain health profiles in persons without stroke. Methods: We conducted a two-sample Mendelian Randomization (MR) study. As instruments, we used 105 genetic variants known to be associated (p<5x10 -8 ) with a composite of caries, dentures and missing teeth in the GLIDE Consortium. In stroke-free participants enrolled in the UK Biobank, we tested for association between these genetic variants and white matter hyperintensity volume (natural log-transformed), fraction anisotropy and mean diffusivity. For the last two neuroimaging traits, we evaluated the first principal component of measurements obtained across 48 brain regions. Results: Our primary analysis using the inverse variance-weighted MR method indicated that genetically-increased risk of poor oral health was associated with: (1) higher burden of silent cerebrovascular disease, as represented by higher volumes of white matter hyperintensities (beta=0.24, SE=0.07 p-value=0.001), and (2) increased microstructural damage, as represented by lower fractional anisotropy (beta=-2.53, SE=0.38; p=1x10 -9 ) and higher mean diffusivity (beta=3.42, SE=0.41; p=2x10 -11 ). Sensitivity analyses identified horizontal pleiotropy in our primary results, but an outlier-corrected analysis confirmed all three initial results (all p-values <0.001, Table 1). Conclusion: Among persons without stroke, genetically-determined poor oral health is associated with worse neuroimaging brain health profiles. Because gene-disease associations are immune to confounding, our results indicate that this association is causal. Early treatment of poor oral health may lead to significant brain health benefits, even in persons without stroke.

Deciphering the clinico-radiological heterogeneity of dysexecutive Alzheimer's disease.

Dysexecutive Alzheimer's disease (dAD) manifests as a progressive dysexecutive syndrome without prominent behavioral features, and previous studies suggest clinico-radiological heterogeneity within this syndrome. We uncovered this heterogeneity using unsupervised machine learning in 52 dAD patients with multimodal imaging and cognitive data. A spectral decomposition of covariance between FDG-PET images yielded six latent factors ("eigenbrains") accounting for 48% of variance in patterns of hypometabolism. These eigenbrains differentially related to age at onset, clinical severity, and cognitive performance. A hierarchical clustering on the eigenvalues of these eigenbrains yielded four dAD subtypes, i.e. "left-dominant," "right-dominant," "bi-parietal-dominant," and "heteromodal-diffuse." Patterns of FDG-PET hypometabolism overlapped with those of tau-PET distribution and MRI neurodegeneration for each subtype, whereas patterns of amyloid deposition were similar across subtypes. Subtypes differed in age at onset and clinical severity where the heteromodal-diffuse exhibited a worse clinical picture, and the bi-parietal had a milder clinical presentation. We propose a conceptual framework of executive components based on the clinico-radiological associations observed in dAD. We demonstrate that patients with dAD, despite sharing core clinical features, are diagnosed with variability in their clinical and neuroimaging profiles. Our findings support the use of data-driven approaches to delineate brain-behavior relationships relevant to clinical practice and disease physiology.

Discriminating Mild Traumatic Brain Injury and Posttraumatic Stress Disorder Using Latent Neuroimaging and Neuropsychological Profiles in Active-Duty Military Service Members.

Mild traumatic brain injury (mTBI) and posttraumatic stress disorder (PTSD) commonly occur among military Service Members and Veterans and have heterogenous, but also overlapping symptom presentations, which often complicate the diagnoses of underlying impairments and development of effective treatment plans. Thus, we sought to examine whether the combination of whole brain gray matter (GM) and white matter (WM) structural measures with neuropsychological performance can aid in the classification of military personnel with mTBI and PTSD. Active-Duty US Service Members ( n = 156; 87.8% male) with a history of mTBI, PTSD, combined mTBI+PTSD, or orthopedic injury completed a neuropsychological battery and T1- and diffusion-weighted structural neuroimaging. Cortical, subcortical, ventricular, and WM volumes and whole brain fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) were calculated. Latent profile analyses were performed to determine how the GM and WM indicators, together with neuropsychological indicators, classified individuals. For both GM and WM, respectively, a 4-profile model was the best fit. The GM model identified greater ventricular volumes in Service Members with cognitive symptoms, including those with a diagnosis of mTBI, either alone or with PTSD. The WM model identified reduced FA and elevated RD in those with psychological symptoms, including those with PTSD or mTBI and comorbid PTSD. However, contrary to expectation, a global neural signature unique to those with comorbid mTBI and PTSD was not identified. The findings demonstrate that neuropsychological performance alone is more robust in differentiating Active-Duty Service Members with mTBI and PTSD, whereas global neuroimaging measures do not reliably differentiate between these groups.

Deciphering the clinico‐radiological heterogeneity of dysexecutive Alzheimer’s disease using unsupervised machine learning techniques

AbstractBackgroundDysexecutive Alzheimer's disease (dAD) is a relatively early‐onset variant of AD primarily degrading core executive functions in the absence of predominant behavioral symptoms. Clinical observations suggest substantial variability in clinical, cognitive and neuroimaging profiles within this syndrome. In this presentation, we will discuss our ongoing work disentangling this heterogeneity using unsupervised machine learning techniques.MethodWe collected clinical, neuropsychological and multimodal imaging (MRI, FDG‐PET, amyloid‐PET, tau‐PET) data on 52 dAD patients assessed in our behavioral neurology clinic at Mayo Clinic Rochester. We first performed a spectral clustering analysis based on FDG‐PET to delineate latent patterns of network degeneration (referred as “eigenbrains”) and assessed the relationships between these eigenbrains and clinical and cognitive symptomatology. We then performed a hierarchical clustering on these eigenbrains to derive data‐driven subtypes of dAD. We compared clinical and cognitive data between subtypes, and then compared the imaging profiles to 52 amyloid‐negative age‐ and sex‐matched controls.ResultSix eigenbrains explained approximatively 48% of the variance in FDG‐PET patterns and primarily reflected heteromodal to primary motor/sensory, left‐right hemispheric asymmetry, and anterior‐to‐posterior gradients of macro‐scale cortical organization (Fig 1). These eigenbrains differentially related to reported age at symptom onset, degree of clinical impairment, and performance on a wide range of cognitive domains (executive functions, episodic memory, and visuospatial). Hierarchical clustering revealed four dAD subtypes (diffuse, biparietal, left‐hemisphere, right‐hemisphere). These subtypes differed in reported age at symptom onset and cognitive profile, where the heteromodal‐diffuse subtype exhibited an overall worse clinical picture and the biparietal had a milder profile compared to other subtypes, which was not explained by disease duration. Additionally, spatial patterns of tau distribution and neurodegeneration overlapped with patterns of FDG‐PET hypometabolism in each subtype, whereas patterns of amyloid deposition were similar across subtypes (Figs 2 & 3).ConclusionAlmost half of the variance in macro‐scale patterns of hypometabolism in this dAD cohort was accounted for by six eigenbrains. These eigenbrains captured the inter‐individual variability in age at symptom onset and cognitive impairment. Four dAD subtypes derived from these eigenbrains revealed meaningful differences in clinical, cognitive, and imaging profiles. Recognizing this heterogeneity has significant clinical implications for diagnosis, prognosis, and symptom monitoring.

Clinical and Neuroimaging Profile of Neonates With Moderate-to-Severe Encephalopathy Treated With Therapeutic Hypothermia

Objective: To demonstrate the clinical and neuroimaging profile of infants with moderate-to-severe encephalopathy after therapeutic hypothermia (TH) in the Indian setting. The secondary objective was to determine the logistics involved in performing neuroimaging in these infants. Design and Setting: A retrospective descriptive study in 2 tertiary centers. Methods: All infants who underwent cooling in both centers from January 2019 to December 2019 were included. Demographic data, details of cooling, clinical and neuroimaging profile (magnetic resonance imaging [MRI] and magnetic resonance spectroscopy [MRS]), and details of the logistics involved in performing the imaging (sedation, neonatal transport, personnel, distance traveled, and time taken) were collected and analyzed. Results: A total of 44 infants were enrolled of which 37 (84.0%) underwent both TH and neuroimaging (MRI and MRS). A total of 33 (75.0%) of infants were outborn, 20 (45.5%) of them had sentinel events before delivery and 17 (38.6%) were intubated at birth. Apgar score <5 min was seen in 8 (18.2%) and seizures in 23 (52.3%) of infants. A total of 37 (84.1%) survived until discharge and underwent neuroimaging. Neuroimaging was performed between 5 and 14 days after birth. The most common abnormal finding on MRI brain was the signal changes in watershed areas of brain 8 (18.2%) and on MRS was low N-acetyl aspartate and high lactate 13 (29.5%). Midazolam (68.2%) and Triclofos (15.2%) were the sedatives used during neuroimaging these infants. Conclusion: TH for infants with neonatal encephalopathy resulted in excellent survival outcomes in tertiary private sector hospitals.

Clinical, Etiological and Neuroimaging Profile in Children with Microcephaly Under Five Years of Age

Objective: To describe the clinical, etiological and neuroimaging profile of children with microcephaly at a tertiary care children hospital of Karachi Study Design: Cross sectional study Place and Duration of Study: The study was conducted at neurology outpatient department of National institute of child health (NICH) Karachi from 1st December 2020 to 30th November 2021. Methodology: Patients with microcephaly who presented to outpatient department of National Institute of Child Health (NICH) Karachi were included. Clinical history and physical examination findings were recorded in proforma and neuroimaging was done in all patients including computed tomography or magnetic resonance imaging of brain. Data entry and analysis was done in SPSS 25.0. Results: Total 130 children were enrolled with mean age was 7.17 ±7.1 (range 1-41) months of which male were 57(43.5%) and females were 74(56.5%). Mean weight was 7.74 ±2.9 kg. Mean FOC was 40.6 ±3.3 (range 33-49) cm. common clinical symptoms for presentation were seizures 95 (73%) and developmental delay 95 (73%). Most frequent etiology was birth asphyxia 60 (46%), and neuroimaging shows brain atrophy in 32 (24%). Conclusion: In our study common cause of microcephaly was cerebral palsy and was commonly associated with of epilepsy, developmental delay, hearing and vision problems. Further plans are needed for prevention of perinatal asphyxia by regular antenatal checkups and delivery by trained person with co-ordination between pediatrician and obstetrics along with early identification of danger signs, which may improve outcome and prevent lifelong disabilities Keywords: Microcephaly; developmental delay; seizures.

Unraveling Parkinson's disease heterogeneity using subtypes based on multimodal data

BackgroundParkinson's disease (PD) is a clinically and neuroanatomically heterogeneous neurodegenerative disease characterized by different subtypes. To this date, no studies have used multimodal data that combines clinical, motor, cognitive and neuroimaging assessments to identify these subtypes, which may provide complementary, clinically relevant information. To address this limitation, we subtyped participants with mild-moderate PD based on a rich, multimodal dataset of clinical, cognitive, motor, and neuroimaging variables. MethodsCross-sectional data from 95 PD participants from our randomized EXPANd (EXercise in PArkinson's disease and Neuroplasticity) controlled trial were included. Participants were subtyped using clinical, motor, and cognitive assessments as well as structural and resting-state MRI data. Subtyping was done by random forest clustering. We extracted information about the subtypes by inspecting their neuroimaging profiles and descriptive statistics. ResultsOur multimodal subtyping analysis yielded three PD subtypes: a motor-cognitive subtype characterized by widespread alterations in brain structure and function as well as impairment in motor and cognitive abilities; a cognitive dominant subtype mainly impaired in cognitive function that showed frontoparietal structural and functional changes; and a motor dominant subtype impaired in motor variables without any brain alterations. Motor variables were most important for the subtyping, followed by gray matter volume in the right medial postcentral gyrus. ConclusionsThree distinct PD subtypes were identified in our multimodal dataset. The most important features to subtype PD participants were motor variables in addition to structural MRI in the sensorimotor region. These findings have the potential to improve our understanding of PD heterogeneity, which in turn can lead to personalized interventions and rehabilitation.

Foster Kennedy syndrome secondary to a giant prolactinoma with a remarkable response to cabergoline.

SummaryPituitary adenomas are intracranial neoplasms, usually demonstrating a benign phenotype. We present the case of 21-year-old male with an 18-month history of reduced visual function (acuity and field) in the left eye. Based on neuroimaging and endocrine profile, a giant prolactinoma causing hypogonadotropic hypogonadism was diagnosed and cabergoline was commenced. After a month of treatment, the tumour size reduced, and visual function improved to normal; however, he developed Foster Kennedy syndrome with a swollen right optic disc. After almost 1 year of follow-up, he regained full visual functioning. Two years since his diagnosis, his prolactin remains normal with no adverse effects or further visual complications.Learning pointsFoster Kennedy syndrome is a rare entity but can be a feature of pituitary adenomas.Visual deterioration secondary to a compressive optic neuropathy can be reversible, provided that diagnosis and treatment are prompt.This case highlights the importance of frequent monitoring of visual function during follow-up of these lesions, particularly when there are deficits at diagnosis.

Neuroimaging Profile of drug-resistant Epilepsy from a Tertiary Care Center in South India.

Drug-resistant epilepsy (DRE) is a common and important neurological problem to identify with scope for curative surgical treatment if underlying cause is delineated. There are very few prospective structured studies in our population. This study aimed to look at the neuroimaging profile of DRE presenting in a tertiary care center in South India. All patients diagnosed clinically as DRE as per International League Against Epilepsy (ILAE) criteria and who underwent magnetic resonance imaging (MRI) over a period of 24 months were included in the study. Their clinical and MRI features were documented and analyzed. A total of 150 patients diagnosed with DRE were included in the study. Clinically, 96 of them presented with generalized tonic-clonic seizures (GTCS), 36 with complex partial seizures (CPS), 14 with simple focal seizures, and two each with atonic seizures and focal seizures with secondary generalization. Magnetic resonance imaging (done in 1.5 T) was normal in 32%. In those with abnormal MRI, mesial temporal sclerosis (MTS) was the commonest pathology seen in 41.3%, followed by cortical malformations (6.7%), tumors (2.6%), vascular malformations (2.7%), and other nonspecific lesions (12%). The clinical and neuroimaging profile of DRE showed that DRE is more common in younger age (of less than 30 years); presents mainly with GTCS or CPS; mesial temporal sclerosis is the commonest underlying pathology which was bilateral in 8.6%; temporal lobe lesions predominate (49.3% of all DRE); and cortical malformation, low-grade tumors, and vascular lesions are other important causes.

Atypical Alzheimer Disease Variants.

This article discusses the clinical, neuroimaging, and biomarker profiles of sporadic atypical Alzheimer disease (AD) variants, including early-onset AD, posterior cortical atrophy, logopenic variant primary progressive aphasia, dysexecutive variant and behavioral variant AD, and corticobasal syndrome. Significant advances are being made in the recognition and characterization of the syndromically diverse AD variants. These variants are identified by the predominant cognitive and clinical features: early-onset amnestic syndrome, aphasia, visuospatial impairments, dysexecutive and behavioral disturbance, or motor symptoms. Although understanding of regional susceptibility to disease remains in its infancy, visualizing amyloid and tau pathology in vivo and CSF examination of amyloid-β and tau proteins are particularly useful in atypical AD, which can be otherwise prone to misdiagnosis. Large-scale research efforts, such as LEADS (the Longitudinal Early-Onset Alzheimer Disease Study), are currently ongoing and will continue to shed light on our understanding of these diverse presentations. Understanding the clinical, neuroimaging, and biomarker profiles of the heterogeneous group of atypical AD syndromes improves diagnostic accuracy in patients who are at increased risk of misdiagnosis. Earlier accurate identification facilitates access to important interventions, social services and disability assistance, and crucial patient and family education.

Cerebrospinal Fluid Profile of Tau, Phosphorylated Tau, Aβ42, and Aβ40 in Probable Cerebral Amyloid Angiopathy.

There is no consensus regarding the diagnostic value of cerebrospinal fluid (CSF) Alzheimer's disease (AD) biomarkers in cerebral amyloid angiopathy (CAA). To describe the CSF levels of Aβ42, Aβ40, total protein Tau, and phosphorylated-Tau (p-Tau) in a large series of probable CAA patients and to compare with AD patients in order to identify a specific pattern in CAA but also to look for correlations with the neuroimaging profile. We retrospectively included from 2 French centers probable CAA patients according to modified Boston criteria who underwent lumbar puncture (LP) with CSF AD biomarker quantifications. Two neurologists independently analyzed all MRI sequences. A logistic regression and Spearman's correlation coefficient were used to identify correlation between MRI and CSF biomarkers in CAA. We included 63 probable CAA and 27 AD patients. Among CAA 50.8% presented with decreased Aβ42 level associated with elevated p-Tau and/or Tau, 34.9% with isolated decreased Aβ42 level and 14.3% patients with normal Aβ42 level. Compared to AD, CAA showed lower levels of Tau (p = 0.008), p-Tau (p = 0.004), and Aβ40 (p = 0.001) but similar Aβ42 level (p = 0.07). No correlation between Aβ42 or Aβ40 levels and neuroimaging was found. CSF biomarkers may improve the accuracy of the modified Boston criteria with altered profile in 85% of the patients fulfilling revised Boston criteria for probable CAA. Aβ40 appears as an interesting selective biomarker in differential diagnosis.

Biomarkers of Age-Related Frailty and Frailty Related to Diseases: An Exploratory, Cross-Sectional Analysis from the MAPT Study

BackgroundFrailty may in most cases result from two main causes: the aging process (age-related frailty) and diseases (evolving chronic conditions or acute medical illnesses — disease-related frailty). The biological determinants characterizing these two main causes of frailty may be different.ObjectivesThe aim of this study is to compare the biological and neuroimaging profile of people without frailty, those with age-related frailty, and subjects with disease-related frailty in community-dwelling older adults.Material and MethodsWe performed a secondary, cross-sectional analysis from the Multidomain Alzheimer Preventive Trial (MAPT). We included 1199 subjects without frailty throughout the 5-year follow-up, 82 subjects with incident age-related frailty, and 53 with incident disease-related frailty. Available blood biomarkers involved nutritional (eg, vitamin D, omega-3 fatty acids), inflammatory-related (IL-6, TNFR1, GDF15), neurodegenerative (eg, beta-amyloid, neurofilament light chain) and neuroimaging markers (MRI, Amyloid-PET).ResultsAlthough not statistically significant, the results of the unadjusted model showed increasing gradients for inflammatory markers (GDF15, TNFR1) and decreasing gradients for nutritional and neuroimaging markers (omega 3 index, hippocampal volume) from age-related frailty participants to individuals with disease-related frailty. Considering the linear models we observed higher GDF15 values in disease-related frailty group compared to age-related frailty individuals [β = 242.8 (49.5, 436.2)]. We did not find any significant difference between subjects without frailty and those with age-related frailty. Subjects with disease-related frailty compared to subjects without frailty had lower values of DHA [β = −2.42 (−4.76, −0.08)], Omega 3 Index [β = −0.50 (−0.95, −0.06)] and hippocampal volume [β = −0.22 (−0.42,−0.02)]. They also had higher values of GDF15 [β = 246.1 (88.9, 403.4)] and TNFR1 [β = 157.5 (7.8, 307.2)].ConclusionAge-related frailty and disease-related frailty may represent different degrees of frailty severity on a biological level. Further research is needed to identify biomarkers potentially able to distinguish these classifications of frailty.

Evidence of discontinuity between psychosis-risk and non-clinical samples in the neuroanatomical correlates of social function

ObjectiveSocial dysfunction is a major feature of clinical-high-risk states for psychosis (CHR-P). Prior research has identified a neuroanatomical pattern associated with impaired social function outcome in CHR-P. The aim of the current study was to test whether social dysfunction in CHR-P is neurobiologically distinct or in a continuum with the lower end of the normal distribution of individual differences in social functioning. MethodsWe used a machine learning classifier to test for the presence of a previously validated brain structural pattern associated with impaired social outcome in CHR-P (CHR-outcome-neurosignature) in the neuroimaging profiles of individuals from two non-clinical samples (total n = 1763) and examined its association with social function, psychopathology and cognition. ResultsAlthough the CHR-outcome-neurosignature could be detected in a subset of the non-clinical samples, it was not associated was adverse social outcomes or higher psychopathology levels. However, participants whose neuroanatomical profiles were highly aligned with the CHR-outcome-neurosignature manifested subtle disadvantage in fluid (PFDR = 0.004) and crystallized intelligence (PFDR = 0.01), cognitive flexibility (PFDR = 0.02), inhibitory control (PFDR = 0.01), working memory (PFDR = 0.0005), and processing speed (PFDR = 0.04). ConclusionsWe provide evidence of divergence in brain structural underpinnings of social dysfunction derived from a psychosis-risk enriched population when applied to non-clinical samples. This approach appears promising in identifying brain mechanisms bound to psychosis through comparisons of patient populations to non-clinical samples with the same neuroanatomical profiles.

Naming-related spectral responses predict neuropsychological outcome after epilepsy surgery.

This prospective study determined the use of intracranially recorded spectral responses during naming tasks in predicting neuropsychological performance following epilepsy surgery. We recruited 65 patients with drug-resistant focal epilepsy who underwent preoperative neuropsychological assessment and intracranial EEG recording. The Clinical Evaluation of Language Fundamentals evaluated the baseline and postoperative language function. During extra-operative intracranial EEG recording, we assigned patients to undergo auditory and picture naming tasks. Time-frequency analysis determined the spatiotemporal characteristics of naming-related amplitude modulations, including high gamma augmentation at 70-110 Hz. We surgically removed the presumed epileptogenic zone based on the intracranial EEG and MRI abnormalities while maximally preserving the eloquent areas defined by electrical stimulation mapping. The multivariate regression model incorporating auditory naming-related high gamma augmentation predicted the postoperative changes in Core Language Score with r2 of 0.37 and in Expressive Language Index with r2 of 0.32. Independently of the effects of epilepsy and neuroimaging profiles, higher high gamma augmentation at the resected language-dominant hemispheric area predicted a more severe postoperative decline in Core Language Score and Expressive Language Index. Conversely, the model incorporating picture naming-related high gamma augmentation predicted the change in Receptive Language Index with an r2 of 0.50. Higher high gamma augmentation independently predicted a more severe postoperative decline in Receptive Language Index. Ancillary regression analysis indicated that naming-related low gamma augmentation and alpha/beta attenuation likewise independently predicted a more severe Core Language Score decline. The machine learning-based prediction model suggested that naming-related high gamma augmentation, among all spectral responses used as predictors, most strongly contributed to the improved prediction of patients showing a >5-point Core Language Score decline (reflecting the lower 25th percentile among patients). We generated the model-based atlas visualizing sites, which, if resected, would lead to such a language decline. With a 5-fold cross-validation procedure, the auditory naming-based model predicted patients who had such a postoperative language decline with an accuracy of 0.80. The model indicated that virtual resection of an electrical stimulation mapping-defined language site would have increased the relative risk of the Core Language Score decline by 5.28 (95% confidence interval: 3.47-8.02). Especially, that of an electrical stimulation mapping-defined receptive language site would have maximized it to 15.90 (95% confidence interval: 9.59-26.33). In summary, naming-related spectral responses predict neuropsychological outcomes after epilepsy surgery. We have provided our prediction model as an open-source material, which will indicate the postoperative language function of future patients and facilitate external validation at tertiary epilepsy centres.

Phenotypic subtypes of progressive dysexecutive syndrome due to Alzheimer's disease: a series of clinical cases.

Diagnostic criteria for a progressive dysexecutive syndrome due to Alzheimer's disease (dAD) were proposed. Clinical observations suggest substantial variability in the clinico-radiological profiles within this syndrome. We report a case series of 6 patients with dAD highlighting this heterogeneity. Average age at diagnosis was 57.3years, and patients were followed annually with clinical, cognitive, and multimodal imaging assessments for an average of 3.7years. Cases were divided based into three subtypes based on their pattern of FDG-PET hypometabolism: predominantly left parieto-frontal (ldAD), predominantly right parieto-frontal (rdAD), or predominantly biparietal (bpdAD) (n = 2 for each). Prominent executive dysfunction was evidenced in all patients. ldAD cases showed greater impairment on measures of verbal working memory and verbal fluency compared to other subtypes. rdAD cases showed more severe alterations in measures of visual abilities compared to language-related domains and committed more perseverative errors on a measure of cognitive flexibility. bpdAD cases presented with predominant cognitive flexibility and inhibition impairment with relative sparing of working memory and a slower rate of clinical progression. rdAD and bpdAD patients developed neuropsychiatric symptoms, whereas none of the ldAD patients did. For each subtype, patterns of tau deposition relatively corresponded to the spatial pattern of FDG hypometabolism. dAD cases could be differentiated from two clinical cases of atypical AD variants (language and visual) in terms of clinical, cognitive and neuroimaging profiles, suggesting that dAD subtypes represent clinical entities separable from other variants of the disease. The recognition of distinct dAD phenotypes has clinical relevance for diagnosis, prognosis, and symptom management.

An Empirical Analysis of Structural Neuroimaging Profiles in a Staging Model of Depression

An Empirical Analysis of Structural Neuroimaging Profiles in a Staging Model of Depression

Genetic, clinical and neuroimaging profiles of sporadic and autosomal recessive hereditary spastic paraplegia cases in Chinese

Spastic paraplegias (SPGs) are a group of clinically and genetically heterogeneous neurodegenerative diseases. Mutations in 78 genes have been identified in autosomal dominant hereditary SPG (AD-HSP) and autosomal recessive hereditary SPG (AR-HSP). Compared to familial HSP, much less is known about the genetic and clinical profiles of sporadic SPGs. In this study, we have screened mutations for 18 sporadic SPGs or AR-HSP patients (mainly Northern Chinese) by whole-exome sequencing. We identified 12 mutations in five genes in 9 (50%) patients, including 9 novel ones: SPG5A/CYP7B1 (c.851C > A; c.122 + 2 T > G), SPG11/KIAA1840 (c.1735 + 3_ 1735 + 6del AAGT); SPG7/SPG7 (c.1454G > A; c.1892_ 1906dup GAGGACGGGCCTCGG); SPG39/PNPLA6 (c.1591G > A; c. 2990C > T); SPG15/ ZFYVE26 (c. 4804C > T; c. 4278 G > A). Among all the mutations, 7 were detected in the SPG5A and SPG11. Age at onset was significantly younger in cases with mutations (15.45 ± 6.78 years) than those without mutations (25.56 ± 10.90 years) (P = 0.03). Except for two cases with the SPG5A mutations, all cases presented with complicated SPGs. Three cases carrying mutations in SPG7, SPG15, SPG39 showed symptoms and signs of ataxia. One case carrying the homozygous c.259 + 2 T > C mutation in CYP7B1 showed serum parameters indicating liver impairment. Magnetic resonance imaging showed significantly thinned corpus callosum in cases with SPG11 and SPG15, but not in those with SPG5A, SPG7 or SPG39. In contrast, cerebellar atrophy was prominent in the SPG7 and SPG39 cases. These findings expand the spectrum of genetic, clinical and imaging features of sporadic SPG and AR-HSP, and have important implications in genetic counselling, molecular mechanisms and precise diagnosis of the disease.

Abstract P603: Atrial Cardiopathy and Brain Infarction in Multiple Vascular Territories in the ARCADIA Trial

Introduction: Some embolic strokes of undetermined source (ESUS) are likely caused by occult cardiac embolism. One potential cardioembolic source is atrial cardiopathy without atrial fibrillation (AF). Patients with cardiac embolism more often have brain infarcts in multiple vascular territories than those with stroke mechanisms not involving a central embolic source. Hypothesis: In patients with ESUS, atrial cardiopathy is associated with brain infarction in multiple vascular territories. Methods: The ARCADIA trial is enrolling ESUS patients, screening them for atrial cardiopathy, and randomly assigning those with atrial cardiopathy to aspirin or apixaban. In the trial, atrial cardiopathy is defined as ≥1 of the following: P-wave terminal force >5,000 μV*ms in ECG lead V 1 , serum NT-proBNP >250 pg/mL, and left atrial diameter index ≥3 cm/m 2 on echocardiogram. Site investigators report whether the index CT or MRI showed brain infarction in the left carotid, right carotid, or posterior circulation, or some combination. In this analysis, our primary outcome was brain infarction in more than one of these three vascular territories. Our secondary outcome was infarction in both the left and right carotid territories. Results: Among 1,707 ESUS patients enrolled in ARCADIA, 679 (39.8%) met the trial’s randomization criteria for atrial cardiopathy and 213 (12.5%) had multi-territorial brain infarcts. The prevalence of brain infarction in more than one vascular territory was 14.0% in those with atrial cardiopathy versus 11.5% in those without (prevalence ratio, 1.22; 95% CI, 0.95-1.57). The prevalence of brain infarction in both the left and right carotid territories was 9.1% in those with atrial cardiopathy versus 7.4% in those without (prevalence ratio, 1.24; 95% CI, 0.90-1.70). Conclusions: These preliminary analyses from ARCADIA suggest a possible association between atrial cardiopathy and brain infarction in multiple vascular territories, but further analysis of a larger sample is needed to conclusively test whether our atrial cardiopathy definition is associated with the classic neuroimaging profile of cardiac embolism.

Parkinson's Disease Cognitive Phenotypes Show Unique Clock Drawing Features when Measured with Digital Technology.

A companion paper (Crowley et al., 2020) reports on the neuroimaging and neuropsychological profiles of statistically determined idiopathic non-dementia Parkinson's disease (PD). The current investigation sought to further examine subtle behavioral clock drawing differences within the same PD cohort by comparing 1) PD to non-PD peers on digitally acquired clock drawing latency and graphomotor metrics, and 2) PD memory, executive, and cognitively well phenotypes on the same variables. 230 matched participants (115 PD, 115 non-PD) completed neuropsychological tests and dCDT. Statistically-derived PD cognitive phenotypes characterized PD participants as PD low executive (PDExe; n = 25), PD low memory (PDMem; n = 34), PD cognitively well (PDWell; n = 56). Using a Bayesian framework and based on apriori hypotheses, we compared groups on: total completion time (TCT), pre-first hand latency (PFHL), post-clock face latency (PCFL), total clock face area (TCFA), and total number of pen strokes. Fewer strokes and slower performance to command were associated with higher odds of PD diagnosis, while a larger clock face in the copy condition was associated with lower odds of PD diagnosis. Within PD cognitive phenotypes, slower performance (TCT, PCFL) and smaller clock face to command were associated with higher odds of being PDExe than PDWell, whereas larger clock faces associated with higher odds of being PDMem than PDWell. Longer disease duration, more pen strokes (command) and smaller clocks (command) associated with higher odds of being PDExe than PDWell. Digitally-acquired clock drawing profiles differ between PD and non-PD peers, and distinguish PD cognitive phenotypes.

The Aging Brain: Crossroad of Normal Aging and Dementia

The functional consequences of the aging brain include several aspects of physical and cognitive decline that ultimately cause loss of mobility and dementia. Although in certain individuals, the cognitive and physical correlates of the aging brain occur in parallel, others show decline in one of these functional parameters. Underlying mechanism of this complex process that lead to different manifestations is not well understood. Proposed mechanisms include brain structural changes, tau pathology, specific white matter degeneration, metabolic derangement mostly including lipids metabolism and others. This symposium aims to address the complexity of the aging brain by showcasing studies that span the continuum from normal aging to dementia using data from the Baltimore Longitudinal Study of Aging (BLSA). The wealth of neuroimaging and phenotype data from the BLSA provides the unique opportunity to investigate neural substrates and predictors of aging phenotype, and mechanisms of age-related neurodegeneration and pathology, such as dementia, and loss of mobility. First, we identify multimodal neuroimaging predictors of important aging phenotypes of gait decline (Sargent/Tian) and memory decline (Bilgel). Second, using advanced quantitative MRI technology, we investigate underlying mechanisms of age-related white matter degeneration through potential oligodendrocyte metabolism (Bouhrara). Third, we demonstrate unique cognitive and neuroimaging profiles of dual memory and gait decline (Tian) and neural substrates for bile acids, the primary cholesterol breakdown products (Varma) in relation to dementia. We seek to generate discussions of mechanisms of the aging brain that connect the age-related phenotypes, such as decline of mobility and cognition, to the development of dementia.