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.

Spatial and Temporal Relationships Between Atrophy and Hypometabolism in Behavioral-Variant Frontotemporal Dementia.

PURPOSE: Individuals with behavioral-variant frontotemporal dementia (bvFTD) show changes in brain structure as assessed by MRI and brain function assessed by 18FDG-PET hypometabolism. However, current understanding of the spatial and temporal interplay between these measures remains limited. METHODS: Here, we examined longitudinal atrophy and hypometabolism relationships in 15 bvFTD subjects with 2 to 4 follow-up MRI and PET scans (56 visits total). Subject-specific slopes of atrophy and hypometabolism over time were extracted across brain regions and correlated with baseline measures both locally, via Pearson correlations, and nonlocally, via sparse canonical correlation analyses (SCCA). RESULTS: Notably, we identified a robust link between initial hypometabolism and subsequent cortical atrophy rate changes in bvFTD subjects. Network-level exploration unveiled alignment between baseline hypometabolism and ensuing atrophy rates in the dorsal attention, language, and default mode networks. SCCA identified 2 significant and highly localized components depicting the connection between baseline hypometabolism and atrophy slope over time. The first centered around bilateral orbitofrontal, frontopolar, and medial prefrontal lobes, whereas the second concentrated in the left temporal lobe and precuneus. CONCLUSIONS: This study highlights 18FDG-PET as a dependable predictor of forthcoming atrophy in spatially adjacent brain regions for individuals with bvFTD.

Differential diagnosis of frontotemporal dementia subtypes with explainable deep learning on structural MRI.

Background: Frontotemporal dementia (FTD) represents a collection of neurobehavioral and neurocognitive syndromes that are associated with a significant degree of clinical, pathological, and genetic heterogeneity. Such heterogeneity hinders the identification of effective biomarkers, preventing effective targeted recruitment of participants in clinical trials for developing potential interventions and treatments. In the present study, we aim to automatically differentiate patients with three clinical phenotypes of FTD, behavioral-variant FTD (bvFTD), semantic variant PPA (svPPA), and nonfluent variant PPA (nfvPPA), based on their structural MRI by training a deep neural network (DNN). Methods: Data from 277 FTD patients (173 bvFTD, 63 nfvPPA, and 41 svPPA) recruited from two multi-site neuroimaging datasets: the Frontotemporal Lobar Degeneration Neuroimaging Initiative and the ARTFL-LEFFTDS Longitudinal Frontotemporal Lobar Degeneration databases. Raw T1-weighted MRI data were preprocessed and parcellated into patch-based ROIs, with cortical thickness and volume features extracted and harmonized to control the confounding effects of sex, age, total intracranial volume, cohort, and scanner difference. A multi-type parallel feature embedding framework was trained to classify three FTD subtypes with a weighted cross-entropy loss function used to account for unbalanced sample sizes. Feature visualization was achieved through post-hoc analysis using an integrated gradient approach. Results: The proposed differential diagnosis framework achieved a mean balanced accuracy of 0.80 for bvFTD, 0.82 for nfvPPA, 0.89 for svPPA, and an overall balanced accuracy of 0.84. Feature importance maps showed more localized differential patterns among different FTD subtypes compared to groupwise statistical mapping. Conclusion: In this study, we demonstrated the efficiency and effectiveness of using explainable deep-learning-based parallel feature embedding and visualization framework on MRI-derived multi-type structural patterns to differentiate three clinically defined subphenotypes of FTD: bvFTD, nfvPPA, and svPPA, which could help with the identification of at-risk populations for early and precise diagnosis for intervention planning.

Adverse childhood experiences, cognitive functioning, depression, and anxiety in adulthood.

BACKGROUND: Evidence suggests that adverse childhood experiences (ACEs) predict cognitive dysfunction, possibly through direct (e.g., brain structure/function changes) and indirect (e.g., increased psychopathology risk) pathways. However, extant studies have focused on young and older adults, with limited understanding of how ACEs affect cognitive health in midadulthood. OBJECTIVE: This study compared psychiatric and cognitive differences between adults at high- and low-risk of adverse health outcomes based on the ACE risk classification scheme. PARTICIPANTS AND SETTING: Adult patients (N = 211; 46.9% female; Mage = 44.1, SD = 17.1; Meducation = 13.8, SD = 3.0) consecutively referred for outpatient neuropsychological evaluation within a large, Midwestern academic medical center. METHOD: Patients were divided into high and low ACE groups based on the number of ACEs endorsed. Subsequently, a series of one-way analyses of variances were conducted to compare high versus low ACE groups on the Test of Premorbid Functioning, Wechsler Adult Intelligence Scale-Fourth Edition Digit Span Test, Trail Making Test-Parts A and B, Rey Auditory Verbal Learning Test, Beck Depression Inventory-II, and Beck Anxiety Inventory scores. RESULTS: Significant group differences were detected for anxiety and depression with the high ACE group endorsing significantly greater depression and anxiety symptoms relative to the low ACE group. High and low ACE groups did not significantly differ on any cognitive measures. CONCLUSIONS: Results indicate that an individual's psychological health, but not cognitive functioning, is impacted by the level of ACE exposure. Study findings highlight the importance of including ACE measures in neuropsychological evaluations, as it will aid in case conceptualization and tailoring treatment recommendations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Social Determinants of Health: Associations Between Dichotomous Versus Dimensional Scores, Neuropsychological Test Performance, and Psychiatric Symptoms.

This study examined the utility of dichotomous versus dimensional scores across two measures of social determinants of health (SDOH) regarding their associations with cognitive performance and psychiatric symptoms in a mixed clinical sample of 215 adults referred for neuropsychological evaluation (Mage = 43.91, 53.5% male, 44.2% non-Hispanic White). Both dimensional and dichotomous health literacy scores accounted for substantial variance in all cognitive outcomes assessed, whereas dimensional and dichotomous adverse childhood experience scores were significantly associated with psychiatric symptoms. Tests of differences between correlated correlations indicated that correlations with cognitive and psychiatric outcomes were not significantly different across dimensional versus dichotomous scores, suggesting that these operationalizations of SDOH roughly equivalently characterize risk of poorer cognitive performance and increased psychiatric symptoms. Results highlight the necessity of measuring multiple SDOH, as different SDOH appear to be differentially associated with cognitive performance versus psychiatric symptoms. Furthermore, results suggest that clinicians can use cut-scores when characterizing patients' risk of poor cognitive or psychiatric outcomes based on SDOH.

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.

The impact of visual memory impairment on Victoria Symptom Validity Test performance: A known-groups analysis.

OBJECTIVE: We assessed the effect of visual learning and recall impairment on Victoria Symptom Validity Test (VSVT) accuracy and response latency for Easy, Difficult, and Total Items. METHOD: A sample of 163 adult patients administered the VSVT and Brief Visuospatial Memory Test-Revised were classified as valid (114/163) or invalid (49/163) groups via independent criterion performance validity tests (PVTs). Classification accuracies for all VSVT indices were examined for the overall sample, and separately for subgroups based on visual memory functioning. RESULTS: In the overall sample, all indices produced acceptable classification accuracy (areas under the curve [AUCs] ≥ 0.79). When stratified by visual learning/recall impairment, accuracy indices yielded acceptable classification for both the unimpaired (AUCs ≥0.79) and impaired subsamples (AUCs ≥0.75). Latency indices had acceptable classification accuracy for the unimpaired subsample (AUCs ≥0.74), but accuracy and sensitivity dropped for the impaired sample (AUCs ≥0.67). CONCLUSIONS: VSVT accuracy and response latency yielded acceptable classification accuracies in the overall sample, and this effect was maintained in those with and without visual learning/recall impairment for the accuracy indices. Findings indicate that the VSVT is a psychometrically robust PVT with largely invariant cut-scores, even in the presence of bona fide visual learning/recall impairment.

Eye Movements Detect Differential Change after Participation in Male Collegiate Collision versus Non-Collision Sports.

Although neuroimaging studies of collision (COLL) sport athletes demonstrate alterations in brain structure and function from pre- to post-season, reliable tools to detect behavioral/cognitive change relevant to functional networks associated with participation in collision sports are lacking. This study evaluated the use of eye-movement testing to detect change in cognitive and sensorimotor processing among male club collegiate athletes after one season of participation in collision sports of variable exposure. We predicted that COLL (High Dose [hockey], n = 8; Low Dose [rugby], n = 9) would demonstrate longer reaction times (antisaccade and memory-guided saccade [MGS] latencies), increased inhibitory errors (antisaccade error rate), and poorer spatial working memory (MGS spatial accuracy) at post-season, relative to pre-season, whereas non-collision collegiate athletes (NON-COLL; n = 17) would remain stable. We also predicted that whereas eye-movement performance would detect pre- to post-season change, ImPACT (Immediate Post-Concussion Assessment and Cognitive Test) performance would remain stable. Our data showed that NON-COLL had shorter (improved performance) post- versus pre-season antisaccade and MGS latencies, whereas COLL groups showed stable, longer, or attenuated reduction in latency (ps ≤ 0.001). Groups did not differ in antisaccade error rate. On the MGS task, NON-COLL demonstrated improved spatial accuracy over time, whereas COLL groups showed reduced spatial accuracy (p < 0.05, uncorrected). No differential change was observed on ImPACT. This study provides preliminary evidence for eye-movement testing as a sensitive marker of subtle changes in attentional control and working memory resulting from participation in sports with varying levels of subconcussive exposure.

Structural volume and cortical thickness differences between males and females in cognitively normal, cognitively impaired and Alzheimer's dementia population.

We investigated differences due to sex in brain structural volume and cortical thickness in older cognitively normal (N=742), cognitively impaired (MCI; N=540) and Alzheimer's Dementia (AD; N=402) individuals from the ADNI and AIBL datasets (861 Males and 823 Females). General linear models were used to control the effect of relevant covariates including age, intracranial volume, magnetic resonance imaging (MRI) scanner field strength and scanner types. Significant volumetric differences due to sex were observed within different cortical and subcortical regions of the cognitively normal group. The number of significantly different regions was reduced in the MCI group, and no region remained different in the AD group. Cortical thickness was overall thinner in males than females in the cognitively normal group, and likewise, the differences due to sex were reduced in the MCI and AD groups. These findings were sustained after including cerebrospinal fluid (CSF) Tau and phosphorylated tau (pTau) as additional covariates.

Blinded Clinical Evaluation for Dementia of Alzheimer's Type Classification Using FDG-PET: A Comparison Between Feature-Engineered and Non-Feature-Engineered Machine Learning Methods.

BACKGROUND: Advanced machine learning methods can aid in the identification of dementia risk using neuroimaging-derived features including FDG-PET. However, to enable the translation of these methods and test their usefulness in clinical practice, it is crucial to conduct independent validation on real clinical samples, which has yet to be properly delineated in the current literature. OBJECTIVE: In this paper, we present our efforts to enable such clinical translational through the evaluation and comparison of two machine-learning methods for discrimination between dementia of Alzheimer's type (DAT) and Non-DAT controls. METHODS: FDG-PET-based dementia scores were generated on an independent clinical sample whose clinical diagnosis was blinded to the algorithm designers. A feature-engineered approach (multi-kernel probability classifier) and a non-feature-engineered approach (3D convolutional neural network) were analyzed. Both classifiers were pre-trained on cognitively normal subjects as well as subjects with DAT. These two methods provided a probabilistic dementia score for this previously unseen clinical data. Performance of the algorithms were compared against ground-truth dementia rating assessed by experienced nuclear physicians. RESULTS: Blinded clinical evaluation on both classifiers showed good separation between the cognitively normal subjects and the patients diagnosed with DAT. The non-feature-engineered dementia score showed higher sensitivity among subjects whose diagnosis was in agreement between the machine-learning models, while the feature-engineered approach showed higher specificity in non-consensus cases. CONCLUSION: In this study, we demonstrated blinded evaluation using data from an independent clinical sample for assessing the performance in DAT classification models in a clinical setting. Our results showed good generalizability for two machine-learning approaches, marking an important step for the translation of pre-trained machine-learning models into clinical practice.

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.

Differential Change in Oculomotor Performance among Female Collegiate Soccer Players versus Non-Contact Athletes from Pre- to Post-Season.

Sensitive and reliable tools are needed to evaluate potential behavioral and cognitive changes following head impact exposure in contact and collision sport participation. We evaluated change in oculomotor testing performance among female, varsity, collegiate athletes following variable exposure to head impacts across a season. Female, collegiate, contact sport (soccer, CONT) and non-contact sport (NON-CONT) athletes were assessed pre-season and post-season. Soccer athletes were grouped according to total season game headers into low dose (≤40 headers; CONT-Low Dose) or high dose (>40 headers; CONT-High Dose) groups. Performance on pro-saccade (reflexive visual response), anti-saccade (executive inhibition), and memory-guided saccade (MGS, spatial working memory) computer-based laboratory tasks were assessed. Primary saccade measures included latency/reaction time, inhibition error rate (anti-saccade only), and spatial accuracy (MGS only). NON-CONT (n = 20), CONT-Low Dose (n = 17), and CONT-High Dose (n = 7) groups significantly differed on pre-season versus post-season latency on tasks with executive functioning demands (anti-saccade and MGS, p ≤ 0.001). Specifically, NON-CONT and CONT-Low Dose demonstrated shorter (i.e., faster) anti-saccade (1.84% and 2.68%, respectively) and MGS (5.74% and 2.76%, respectively) latencies from pre-season to post-season, whereas CONT-High Dose showed 1.40% average longer anti-saccade, and 0.74% shorter MGS, latencies. NON-CONT and CONT-Low Dose demonstrated reduced (i.e., improved) inhibition error rate on the anti-saccade task at post-season versus pre-season, whereas CONT-High Dose demonstrated relative stability (p = 0.021). The results of this study suggest differential exposure to subconcussive head impacts in collegiate female athletes is associated with differential change in reaction time and inhibitory control performances on executive saccadic oculomotor testing.

The effect of high vs. low dose lurasidone on eye movement biomarkers of prefrontal abilities in treatment-resistant schizophrenia.

OBJECTIVE: Eye movement (EM) measures can serve as biomarkers to evaluate pharmacological effects on brain systems involved in cognition. In recent onset schizophrenia, antipsychotic treatment can improve attentional control on the antisaccade task and exacerbate working memory impairment on the memory guided saccade task; effects in treatment-resistant schizophrenia (TRS) are less clear. This study evaluated the effects of high versus low dose lurasidone on EM performance in TRS. METHODS: TRS patients completed EM testing: 1) at baseline, on existing medication regimen (n = 42), 2) after 6 weeks of low dose (80 mg) lurasidone (n = 38), 3) after 12 weeks following randomization to low (80 mg) or high dose (240 mg) lurasidone (n = 27), and 4) after 24 weeks of treatment (n = 23). EM testing included prosaccade, antisaccade, and memory guided saccade tasks. RESULTS: Six weeks of lurasidone resulted in increased prosaccade saccade latency and reduced antisaccade errors, with no change in memory guided saccade accuracy. After randomization, prosaccade and antisaccade latencies increased in only the high dose group, with no change in antisaccade errors in both groups. Memory guided saccade error increased in the high dose group and remained stable in the low dose group. CONCLUSION: Among TRS, stabilization on low dose lurasidone was associated with improved executive control of attention reflected by reduced antisaccade errors. High dose lurasidone resulted in prolonged speed of reflexive and executive shifts of attention and reduced spatial working memory relative to low dose. These findings indicate that EM measures are helpful biomarkers of dose-dependent antipsychotic treatment effects on executive cognitive abilities in TRS.

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.