In vivo effect of LATE-NC on integrity of white matter connections to the hippocampus.

INTRODUCTION: TAR DNA-binding protein 43 (TDP-43) is a highly prevalent proteinopathy that is involved in neurodegenerative processes, including axonal damage. To date, no ante mortem biomarkers exist for TDP-43, and few studies have directly assessed its impact on neuroimaging measures utilizing pathologic quantification. METHODS: Ante mortem diffusion-weighted images were obtained from community-dwelling older adults. Regression models calculated the relationship between post mortem TDP-43 burden and ante mortem fractional anisotropy (FA) within each voxel in connection with the hippocampus, controlling for coexisting Alzheimer's disease and demographics. RESULTS: Results revealed a significant negative relationship (false discovery rate [FDR] corrected p < .05) between post mortem TDP-43 and ante mortem FA in one cluster within the left medial temporal lobe connecting to the parahippocampal cortex, entorhinal cortex, and cingulate, aligning with the ventral subdivision of the cingulum. FA within this cluster was associated with cognition. DISCUSSION: Greater TDP-43 burden is associated with lower FA within the limbic system, which may contribute to impairment in learning and memory. HIGHLIGHTS: Post mortem TDP-43 pathological burden is associated with reduced ante mortem fractional anisotropy. Reduced FA located in the parahippocampal portion of the cingulum. FA in this area was associated with reduced episodic and semantic memory. FA in this area was associated with increased inward hippocampal surface deformation.

Disinhibition in dementia related to reduced morphometric similarity of cognitive control network.

Disinhibition is one of the most distressing and difficult to treat neuropsychiatric symptoms of dementia. It involves socially inappropriate behaviours, such as hypersexual comments, inappropriate approaching of strangers and excessive jocularity. Disinhibition occurs in multiple dementia syndromes, including behavioural variant frontotemporal dementia, and dementia of the Alzheimer's type. Morphometric similarity networks are a relatively new method for examining brain structure and can be used to calculate measures of network integrity on large scale brain networks and subnetworks such as the salience network and cognitive control network. In a cross-sectional study, we calculated morphometric similarity networks to determine whether disinhibition in behavioural variant frontotemporal dementia (n = 75) and dementia of the Alzheimer's type (n = 111) was associated with reduced integrity of these networks independent of diagnosis. We found that presence of disinhibition, measured by the Neuropsychiatric Inventory Questionnaire, was associated with reduced global efficiency of the cognitive control network in both dementia of the Alzheimer's type and behavioural variant frontotemporal dementia. Future research should replicate this transdiagnostic finding in other dementia diagnoses and imaging modalities, and investigate the potential for intervention at the level of the cognitive control network to target disinhibition.

Longitudinal Spatial Relationships Between Atrophy and Hypometabolism Across the Alzheimer's Disease Continuum.

BACKGROUND: The A/T/N framework allows for the assessment of pathology-specific markers of MRI-derived structural atrophy and hypometabolism on 18FDG-PET. However, how these measures relate to each other locally and distantly across pathology-defined A/T/N groups is currently unclear. OBJECTIVE: To determine the regions of association between atrophy and hypometabolism in A/T/N groups both within and across time points. METHODS: We examined multivariate multimodal neuroimaging relationships between MRI and 18FDG-PET among suspected non-Alzheimer's disease pathology (SNAP) (A-T/N+; n = 14), Amyloid Only (A+T-N-; n = 24) and Probable AD (A+T+N+; n = 77) groups. Sparse canonical correlation analyses were employed to model spatially disjointed regions of association between MRI and 18FDG-PET data. These relationships were assessed at three combinations of time points -cross-sectionally, between baseline visits and between month 12 (M-12) follow-up visits, as well as longitudinally between baseline and M-12 follow-up. RESULTS: In the SNAP group, spatially overlapping relationships between atrophy and hypometabolism were apparent in the bilateral temporal lobes when both modalities were assessed at the M-12 timepoint. Amyloid-Only subjects showed spatially discordant distributed atrophy-hypometabolism relationships at all time points assessed. In Probable AD subjects, local correlations were evident in the bilateral temporal lobes when both modalities were assessed at baseline and at M-12. Across groups, hypometabolism at baseline correlated with non-local, or distant, atrophy at M-12. CONCLUSION: These results support the view that local concordance of atrophy and hypometabolism is the result of a tau-mediated process driving neurodegeneration.

Investigating the temporal pattern of neuroimaging-based brain age estimation as a biomarker for Alzheimer's Disease related neurodegeneration.

Neuroimaging-based brain-age estimation via machine learning has emerged as an important new approach for studying brain aging. The difference between one's estimated brain age and chronological age, the brain age gap (BAG), has been proposed as an Alzheimer's Disease (AD) biomarker. However, most past studies on the BAG have been cross-sectional. Quantifying longitudinal changes in an individual's BAG temporal pattern would likely improve prediction of AD progression and clinical outcome based on neurophysiological changes. To fill this gap, our study conducted predictive modeling using a large neuroimaging dataset with up to 8 years of follow-up to examine the temporal patterns of the BAG's trajectory and how it varies by subject-level characteristics (sex, APOEɛ4 carriership) and disease status. Specifically, we explored the pattern and rate of change in BAG over time in individuals who remain stable with normal cognition or mild cognitive impairment (MCI), as well as individuals who progress to clinical AD. Combining multimodal imaging data in a support vector regression model to estimate brain age yielded improved performance over single modality. Multilevel modeling results showed the BAG followed a linear increasing trajectory with a significantly faster rate in individuals with MCI who progressed to AD compared to cognitively normal or MCI individuals who did not progress. The dynamic changes in the BAG during AD progression were further moderated by sex and APOEɛ4 carriership. Our findings demonstrate the BAG as a potential biomarker for understanding individual specific temporal patterns related to AD progression.

Network-wise concordance of multimodal neuroimaging features across the Alzheimer's disease continuum.

Background: Concordance between cortical atrophy and cortical glucose hypometabolism within distributed brain networks was evaluated among cerebrospinal fluid (CSF) biomarker-defined amyloid/tau/neurodegeneration (A/T/N) groups. Method: We computed correlations between cortical thickness and fluorodeoxyglucose metabolism within 12 functional brain networks. Differences among A/T/N groups (biomarker normal [BN], Alzheimer's disease [AD] continuum, suspected non-AD pathologic change [SNAP]) in network concordance and relationships to longitudinal change in cognition were assessed. Results: Network-wise markers of concordance distinguish SNAP subjects from BN subjects within the posterior multimodal and language networks. AD-continuum subjects showed increased concordance in 9/12 networks assessed compared to BN subjects, as well as widespread atrophy and hypometabolism. Baseline network concordance was associated with longitudinal change in a composite memory variable in both SNAP and AD-continuum subjects. Conclusions: Our novel study investigates the interrelationships between atrophy and hypometabolism across brain networks in A/T/N groups, helping disentangle the structure-function relationships that contribute to both clinical outcomes and diagnostic uncertainty in AD.

Transfer learning for predicting conversion from mild cognitive impairment to dementia of Alzheimer's type based on a three-dimensional convolutional neural network.

Dementia of Alzheimer's type (DAT) is associated with devastating and irreversible cognitive decline. Predicting which patients with mild cognitive impairment (MCI) will progress to DAT is an ongoing challenge in the field. We developed a deep learning model to predict conversion from MCI to DAT. Structural magnetic resonance imaging scans were used as input to a 3-dimensional convolutional neural network. The 3-dimensional convolutional neural network was trained using transfer learning; in the source task, normal control and DAT scans were used to pretrain the model. This pretrained model was then retrained on the target task of classifying which MCI patients converted to DAT. Our model resulted in 82.4% classification accuracy at the target task, outperforming current models in the field. Next, we visualized brain regions that significantly contribute to the prediction of MCI conversion using an occlusion map approach. Contributory regions included the pons, amygdala, and hippocampus. Finally, we showed that the model's prediction value is significantly correlated with rates of change in clinical assessment scores, indicating that the model is able to predict an individual patient's future cognitive decline. This information, in conjunction with the identified anatomical features, will aid in building a personalized therapeutic strategy for individuals with MCI.

Grant Report on PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis.

We report on the ongoing project "PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis" describing completed and future work supported by this grant. This project is a multi-site, multi-study collaboration effort with research spanning seven sites across the US and Canada. The overall goal of the project is to study neurodegeneration within Alzheimer's Disease, Frontotemporal Dementia, and related neurodegenerative disorders, using a variety of brain imaging and computational techniques to develop methods for the early and accurate prediction of disease and its course. The overarching goal of the project is to develop the earliest and most accurate biomarker that can differentiate clinical diagnoses to inform clinical trials and patient care. In its third year, this project has already completed several projects to achieve this goal, focusing on (1) structural MRI (2) machine learning and (3) FDG-PET and multimodal imaging. Studies utilizing structural MRI have identified key features of underlying pathology by studying hippocampal deformation that is unique to clinical diagnosis and also post-mortem confirmed neuropathology. Several machine learning experiments have shown high classification accuracy in the prediction of disease based on Convolutional Neural Networks utilizing MRI images as input. In addition, we have also achieved high accuracy in predicting conversion to DAT up to five years in the future. Further, we evaluated multimodal models that combine structural and FDG-PET imaging, in order to compare the predictive power of multimodal to unimodal models. Studies utilizing FDG-PET have shown significant predictive ability in the prediction and progression of disease.

Evidence of prefrontal hyperactivation to food-cue reversal learning in adolescents with anorexia nervosa.

Food avoidance in anorexia nervosa (AN) has been hypothesized to occur as a result of deficits in reversal learning and altered neuronal processing of food-cue relationships. Extant findings suggest that those with AN may rely on heightened recruitment of prefrontal regions during initial formation of food-cue learning and difficulty extinguishing these relationships may result from elevated insula activity. We tested this hypothesis by comparing behavioral and neuronal responses to food-cue acquisition and reversal between adolescents with AN and healthy controls. Compared to controls, acquisition of a food-cue association and its reversal were associated with elevated prefrontal activation in adolescents with AN. There were no significant differences between groups in insula activation and no behavioral differences in the ability to acquire or reverse the food-cue association. The results suggest that adolescents with AN recruit prefrontal regulatory networks to acquire and alter expectancies to food. This pattern of top-down prefrontal control suggests that clinical interventions that target changes in food-cue relationships and rely on cognitive control may be less effective. Interventions that alter behavior without reliance on this top-down control may have advantages with this population.

Development of a methodology to combine fMRI and EMG to measure emotional responses in patients with anorexia nervosa.

OBJECTIVE: Individuals with eating disorders are theorized to have basic impairments in affective appraisal and social-emotional processing that contribute to pathogenesis of the disease. We aimed to determine if facial electromyography could be used to discriminate between happy and disgust emotions during simultaneous acquisition of an fMRI BOLD sequence in efforts to establish a novel tool for investigating emotion-driven hypotheses about eating pathology. In line with standards for rigor and reproducibility, we provide detailed protocols and code to support each step of this project. METHOD: Sixteen adolescents with low-weight eating disorders viewed emotional faces (Happy or Disgust) and were asked to mimic their facial expression during simultaneous BOLD and EMG (Corrugator supercilli, Lavator lavii, Zygomaticus major) acquisition. Trials were repeated with the scanner off and again with scanner on (i.e., fatigue). RESULTS: The Levator and Zygomaticus activation patterns discriminated disgust and happy faces successfully. The pattern held between scanner on and off conditions, but muscle activation attenuated in the Fatigue condition, especially for the Zygomaticus. DISCUSSION: Simultaneous fMRI-EMG is a new tool capable of discriminating specific emotions based on muscle activation patterns and can be leveraged to answer emotion-driven hypotheses about clinical populations characterized by difficulty labeling or processing emotions.

Defining the Construct of Synthetic Androgen Intoxication: An Application of General Brain Arousal.

Synthetic androgens (i. e., anabolic-androgenic steroids) are the primary component to the majority of problematic appearance and performance enhancing drug (APED) use. Despite evidence that these substances are associated with increased risk for aggression, violence, body image disturbances, and polypharmacy and can develop a pattern of chronic use consistent with drug dependence, there are no formal definitions of androgen intoxication. Consequently, the purpose of this paper is to establish a testable theory of androgen intoxication. We present evidence and theorize that synthetic androgen intoxication can be defined by a pattern of poor self-regulation characterized by increased propensity for a range of behaviors (e.g., aggression, sex, drug seeking, exercise, etc.) via androgen mediated effects on general brain arousal. This theory posits that androgens reduce threshold for emotional reactivity, motor response, and alertness to sensory stimuli and disrupt inhibitory control over the behaviors associated with synthetic androgen use. These changes result from alteration to basic neurocircuitry that amplifies limbic activation and reduces top-down cortical control. The implications for this definition are to inform APED specific hypotheses about the behavioral and psychological effects of APED use and provide a basis for establishing clinical, legal, and public health guidelines to address the use and misuse of these substances.