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1. Stability in cognitive classification as a function of severity of impairment and ethnicity: A longitudinal analysis

   https://doi.org/10.1080/23279095.2023.2222861  

   Arruda, Fernanda ; Rosselli, Mónica ; Mejia Kurasz, Andrea ; Loewenstein, David A. ; DeKosky, Steven T. ; Lang, Merike K. ; Conniff, Joshua ; Vélez-Uribe, Idaly ; Ahne, Emily ; Shihadeh, Layaly ; et al ( July 2023 , Applied Neuropsychology: Adult)

   Objective: The interaction of ethnicity, progression of cognitive impairment, and neuroimaging biomarkers of Alzheimer’s Disease remains unclear. We investigated the stability in cognitive status classification (cognitively normal [CN] and mild cognitive impairment [MCI]) of 209 participants (124 Hispanics/Latinos and 85 European Americans). Methods: Biomarkers (structural MRI and amyloid PET scans) were compared between Hispanic/Latino and European American individuals who presented a change in cognitive diagnosis during the second or third follow-up and those who remained stable over time. Results: There were no significant differences in biomarkers between ethnic groups in any of the diagnostic categories. The frequency of CN and MCI participants who were progressors (progressed to a more severe cognitive diagnosis at follow-up) and non-progressors (either stable through follow-ups or unstable [progressed but later reverted to a diagnosis of CN]) did not significantly differ across ethnic groups. Progressors had greater atrophy in the hippocampus (HP) and entorhinal cortex (ERC) at baseline compared to unstable non-progressors (reverters) for both ethnic groups, and more significant ERC atrophy was observed among progressors of the Hispanic/Latino group. For European Americans diagnosed with MCI, there were 60% more progressors than reverters (reverted from MCI to CN), while among Hispanics/Latinos with MCI, there were 7% more reverters than progressors. Binomial logistic regressions predicting progression, including brain biomarkers, MMSE, and ethnicity, demonstrated that only MMSE was a predictor for CN participants at baseline. However, for MCI participants at baseline, HP atrophy, ERC atrophy, and MMSE predicted progression. 

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2. Diffusion MRI tractography of the locus coeruleus‐transentorhinal cortex connections using GO‐ESP

   https://doi.org/10.1002/mrm.29088  

   Solders, Seraphina K. ; Galinsky, Vitaly L. ; Clark, Alexandra L. ; Sorg, Scott F. ; Weigand, Alexandra J. ; Bondi, Mark W. ; Frank, Lawrence R. ( November 2021 , Magnetic Resonance in Medicine)

   The locus coeruleus (LC) is implicated as an early site of protein pathogenesis in Alzheimer's disease (AD). Tau pathology is hypothesized to propagate in a prion‐like manner along the LC‐transentorhinal cortex (TEC) white matter (WM) pathway, leading to atrophy of the entorhinal cortex and adjacent cortical regions in a progressive and stereotypical manner. However, WM damage along the LC‐TEC pathway may be an earlier observable change that can improve detection of preclinical AD.

   Diffusion‐weighted MRI (dMRI) allows reconstruction of WM pathways in vivo, offering promising potential to examine this pathway and enhance our understanding of neural mechanisms underlying the preclinical phase of AD. However, standard dMRI analysis tools have generally been unable to reliably reconstruct this pathway. We apply a novel method, geometric‐optics based entropy spectrum pathways (GO‐ESP) and produce a new measure of connectivity: the equilibrium probability (EP).

   We demonstrated reliable reconstruction of LC‐TEC pathways in 50 cognitively normal older adults and showed a negative association between LC‐TEC EP and cerebrospinal fluid tau. Using Human Connectome Project data, we demonstrated replicability of the method across acquisition schemes and scanners. Finally, we compared our findings with the only other existing LC‐TEC tractography template, and replicated their pathway as well as investigated the source of these discrepant findings.

   AD‐related tau pathology may be detectable within GO‐ESP‐identified LC‐TEC pathways. Furthermore, there may be multiple possible routes from LC to TEC, raising important questions for future research on the LC‐TEC connectome and its role in AD pathogenesis.

    

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3. A multimodal magnetoencephalography 7 T fMRI and 7 T proton MR spectroscopy study in first episode psychosis

   https://doi.org/10.1038/s41537-020-00113-4  

   Gawne, Timothy J. ; Overbeek, Gregory J. ; Killen, Jeffery F. ; Reid, Meredith A. ; Kraguljac, Nina V. ; Denney, Thomas S. ; Ellis, Charles A. ; Lahti, Adrienne C. ( September 2020 , npj Schizophrenia)

   We combined magnetoencephalography (MEG), 7 T proton magnetic resonance spectroscopy (MRS), and 7 T fMRI during performance of a task in a group of 23 first episode psychosis (FEP) patients and 26 matched healthy controls (HC). We recorded both the auditory evoked response to 40 Hz tone clicks and the resting state in MEG. Neurometabolite levels were obtained from the anterior cingulate cortex (ACC). The fMRI BOLD response was obtained during the Stroop inhibitory control task. FEP showed a significant increase in resting state low frequency theta activity (p < 0.05; Cohend= 0.69), but no significant difference in the 40 Hz auditory evoked response compared to HC. An across-groups whole brain analysis of the fMRI BOLD response identified eight regions that were significantly activated during task performance (p < 0.01, FDR-corrected); the mean signal extracted from those regions was significantly different between the groups (p = 0.0006;d = 1.19). In the combined FEP and HC group, there was a significant correlation between the BOLD signal during task performance and MEG resting state low frequency activity (p < 0.05). In FEP, we report significant alteration in resting state low frequency MEG activity, but no alterations in auditory evoked gamma band response, suggesting that the former is a more robust biomarker of early psychosis. There were no correlations between gamma oscillations and GABA levels in either HC or FEP. Finally, in this study, each of the three imaging modalities differentiated FEP from HC; fMRI with good and MEG and MRS with moderate effect size.

    

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4. The association of depression and apathy with Alzheimer’s disease biomarkers in a cross-cultural sample

   https://doi.org/10.1080/23279095.2022.2079414  

   Greig Custo, María T. ; Lang, Merike K. ; Barker, Warren W. ; Gonzalez, Joanna ; Vélez-Uribe, Idaly ; Arruda, Fernanda ; Conniff, Joshua ; Rodriguez, Miriam J. ; Loewenstein, David A. ; Duara, Ranjan ; et al ( July 2022 , Applied Neuropsychology: Adult)

   Cross-cultural differences in the association between neuropsychiatric symptoms and Alzheimer's disease (AD) biomarkers are not well understood. This study aimed to (1) compare depressive symptoms and frequency of reported apathy across diagnostic groups of participants with normal cognition (CN), mild cognitive impairment (MCI), and dementia, as well as ethnic groups of Hispanic Americans (HA) and European Americans (EA); (2) evaluate the relationship between depression and apathy with A beta deposition and brain atrophy. Statistical analyses included ANCOVAs, chi-squared, nonparametric tests, correlations, and logistic regressions. Higher scores on the Geriatric Depression Scale (GDS-15) were reported in the MCI and dementia cohorts, while older age corresponded with lower GDS-15 scores. The frequency of apathy differed across diagnoses within each ethnicity, but not when comparing ethnic groups. Reduced volume in the rostral anterior cingulate cortex (ACC) significantly correlated with and predicted apathy for the total sample after applying false discovery rate corrections (FDR), controlling for covariates. The EA group separately demonstrated a significant negative relationship between apathy and superior frontal volume, while for HA, there was a relationship between rostral ACC volume and apathy. Apathy corresponded with higher A beta levels for the total sample and for the CN and HA groups. 

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5. Machine learning based multi-modal prediction of future decline toward Alzheimer’s disease: An empirical study

   https://doi.org/10.1371/journal.pone.0277322  

   Karaman, Batuhan K. ; Mormino, Elizabeth C. ; Sabuncu, Mert R. ( November 2022 , PLOS ONE)

   Thung, Kim Han (Ed.)

   Alzheimer’s disease (AD) is a neurodegenerative condition that progresses over decades. Early detection of individuals at high risk of future progression toward AD is likely to be of critical significance for the successful treatment and/or prevention of this devastating disease. In this paper, we present an empirical study to characterize how predictable an individual subjects’ future AD trajectory is, several years in advance, based on rich multi-modal data, and using modern deep learning methods. Crucially, the machine learning strategy we propose can handle different future time horizons and can be trained with heterogeneous data that exhibit missingness and non-uniform follow-up visit times. Our experiments demonstrate that our strategy yields predictions that are more accurate than a model trained on a single time horizon (e.g. 3 years), which is common practice in prior literature. We also provide a comparison between linear and nonlinear models, verifying the well-established insight that the latter can offer a boost in performance. Our results also confirm that predicting future decline for cognitively normal (CN) individuals is more challenging than for individuals with mild cognitive impairment (MCI). Intriguingly, however, we discover that prediction accuracy decreases with increasing time horizon for CN subjects, but the trend is in the opposite direction for MCI subjects. Additionally, we quantify the contribution of different data types in prediction, which yields novel insights into the utility of different biomarkers. We find that molecular biomarkers are not as helpful for CN individuals as they are for MCI individuals, whereas magnetic resonance imaging biomarkers (hippocampus volume, specifically) offer a significant boost in prediction accuracy for CN individuals. Finally, we show how our model’s prediction reveals the evolution of individual-level progression risk over a five-year time horizon. Our code is available at https://github.com/batuhankmkaraman/mlbasedad . 

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