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Accurate integration of sensory inputs and motor commands is essential to achieve successful behavioral goals. A robust model of sensorimotor integration is the pitch perturbation response, in which speakers respond rapidly to shifts of the pitch in their auditory feedback. In a previous study, we demonstrated abnormal sensorimotor integration in patients with Alzheimer’s disease (AD) with an abnormally enhanced behavioral response to pitch perturbation. Here we examine the neural correlates of the abnormal pitch perturbation response in AD patients, using magnetoencephalographic imaging. The participants phonated the vowel /α/ while a real-time signal processor briefly perturbed the pitch (100 cents, 400 ms) of their auditory feedback. We examined the high-gamma band (65–150 Hz) responses during this task. AD patients showed significantly reduced left prefrontal activity during the early phase of perturbation and increased right middle temporal activity during the later phase of perturbation, compared to controls. Activity in these brain regions significantly correlated with the behavioral response. These results demonstrate that impaired prefrontal modulation of speech-motor-control network and additional recruitment of right temporal regions are significant mediators of aberrant sensorimotor integration in patients with AD. The abnormal neural integration mechanisms signify the contribution of cortical network dysfunction to cognitive and behavioral deficits in AD.

 

The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N  = 351) and Alzheimer’s disease (AD, N  = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk. 

A major sex difference in Alzheimer’s disease (AD) is that men with the disease die earlier than do women. In aging and preclinical AD, men also show more cognitive deficits. Here, we show that the X chromosome affects AD-related vulnerability in mice expressing the human amyloid precursor protein (hAPP), a model of AD. XY-hAPP mice genetically modified to develop testicles or ovaries showed worse mortality and deficits than did XX-hAPP mice with either gonad, indicating a sex chromosome effect. To dissect whether the absence of a second X chromosome or the presence of a Y chromosome conferred a disadvantage on male mice, we varied sex chromosome dosage. With or without a Y chromosome, hAPP mice with one X chromosome showed worse mortality and deficits than did those with two X chromosomes. Thus, adding a second X chromosome conferred resilience to XY males and XO females. In addition, the Y chromosome, its sex-determining region Y gene (Sry), or testicular development modified mortality in hAPP mice with one X chromosome such that XY males with testicles survived longer than did XY or XO females with ovaries. Furthermore, a second X chromosome conferred resilience potentially through the candidate geneKdm6a, which does not undergo X-linked inactivation. In humans, genetic variation inKDM6Awas linked to higher brain expression and associated with less cognitive decline in aging and preclinical AD, suggesting its relevance to human brain health. Our study suggests a potential role for sex chromosomes in modulating disease vulnerability related to AD.

 

In the Alzheimer’s disease (AD) continuum, the prodromal state of mild cognitive impairment (MCI) precedes AD dementia and identifying MCI individuals at risk of progression is important for clinical management. Our goal was to develop generalizable multivariate models that integrate high-dimensional data (multimodal neuroimaging and cerebrospinal fluid biomarkers, genetic factors, and measures of cognitive resilience) for identification of MCI individuals who progress to AD within 3 years. Our main findings were i) we were able to build generalizable models with clinically relevant accuracy (~93%) for identifying MCI individuals who progress to AD within 3 years; ii) markers of AD pathophysiology (amyloid, tau, neuronal injury) accounted for large shares of the variance in predicting progression; iii) our methodology allowed us to discover that expression ofCR1(complement receptor 1), an AD susceptibility gene involved in immune pathways, uniquely added independent predictive value. This work highlights the value of optimized machine learning approaches for analyzing multimodal patient information for making predictive assessments.