The cerebellum in Alzheimer’s disease: evaluating its role in cognitive decline

Jacobs, Heidi I.L.; Hopkins, David A.; Mayrhofer, Helen C.; Bruner, Emiliano; Leeuwen, Fred W. van; Raaijmakers, Wijnand; Schmahmann, Jeremy D.

doi:10.1093/brain/awx194

Cited by 231 publications

(203 citation statements)

References 127 publications

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“…The CB, which only accounts for 10% of the brain but contains over 50% of the brains total neurons, is often regarded as being partially spared from AD as plaques and tangles are generally not reported [40] [42], and in this study are free from hallmark AD pathology in both AsymAD and AD subjects. For subjects with hallmark AD pathology (BRAAK >=2, AsymAD and AD), genes significantly and consistently perturbed across the EC, TC and FC tissues that are not or are significantly reversed in the CB, may be associated with hallmark AD pathology, although, it remains unclear if these genes are causative or a response to the pathology itself.…”

Section: Discussionmentioning

confidence: 67%

Transcriptomic analysis of probable asymptomatic and symptomatic Alzheimer brains

Patel

Curtis

Lee

et al. 2019

Preprint

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Individuals with intact cognition and neuropathology consistent with Alzheimer's disease (AD) are referred to as asymptomatic AD (AsymAD). These individuals are highly likely to develop AD, yet transcriptomic changes in the brain which might reveal mechanisms for their AD vulnerability are currently unknown. Entorhinal cortex, frontal cortex, temporal cortex and cerebellum tissue from 27 control, 33AsymAD and 52 AD human brains were microarray expression profiled. Differential expression analysis identified a significant increase of transcriptomic activity in the frontal cortex of AsymAD subjects, suggesting fundamental changes in AD may initially begin within the frontal cortex region prior to AD diagnosis. Co-expression analysis identified an overactivation of the brain "glutamate-glutamine cycle", and disturbances in the brain energy pathways in both AsymAD and AD subjects, while connectivity of key hub genes in this network indicates a shift from an already increased cell proliferation in AsymAD subjects to stress response and removal of amyloidogenic proteins in AD subjects. This study provides new insight into the earliest biological changes occurring in the brain prior to the manifestation of clinical AD symptoms and provides new potential therapeutic targets for early disease intervention.

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Section: Discussionmentioning

confidence: 67%

Transcriptomic analysis of probable asymptomatic and symptomatic Alzheimer brains

Patel

Curtis

Lee

et al. 2019

Preprint

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“…In the specific case of cerebellar cortex, damage to the cerebellar posterior lobe, which provides inputs to the DN, causes the cerebellar cognitive affective syndrome, characterized by deficits in executive, linguistic, visuospatial and affective processing (Guell et al 2015;Hoche et al 2016Hoche et al , 2018Schmahmann & Sherman 1998, Schmahmann et al 2009, Stoodley et al 2016). In addition, multiple studies have reported cerebellar cortical abnormalities in numerous disorders such as Alzheimer's disease (Guo et al 2016, Jacobs et al 2018, autism spectrum disorder (Arnold Anteraper et al 2018, D'Mello & Stoodley 2015, and schizophrenia (Moberget et al 2018), with preliminary evidence suggesting that cerebellar cortical stimulation might improve symptoms in these diseases (Brady et al 2019, Demirtas-Tatlidede et al 2010, Di Lorenzo et al 2013, Garg et al 2016, Stoodley et al 2017, Tikka et al 2015. The importance of our findings is thus underscored by the relevance of cerebellar cortex in neurology and psychiatry (Schmahmann et al 2019), as the majority of fibers exiting the cerebellar cortex synapse in DN before reaching extracerebellar structures such as cerebral cortex.…”

Section: Relevance For Cerebellar Neuroscience Neurology and Psychimentioning

confidence: 99%

Functional Territories of Human Dentate Nucleus

Guell

D’Mello

Hubbard

et al. 2019

Preprint

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Anatomical connections link the cerebellar cortex with multiple distinct sensory, motor, association, and paralimbic areas of the cerebrum. These projections allow a topographically precise cerebellar modulation of multiple domains of neurological function, and underscore the relevance of the cerebellum for the pathophysiology of numerous disorders in neurology and psychiatry. The majority of fibers that exit the cerebellar cortex synapse in the dentate nuclei (DN) before reaching extracerebellar structures such as cerebral cortex. Although the DN have a central position in the anatomy of the cerebello-cerebral circuits, the functional neuroanatomy of human DN remains largely unmapped. Neuroimaging research has redefined broad categories of functional division in the human brain showing that primary processing, attentional (task positive) processing, and default-mode (task negative) processing are three central poles of neural macro-scale functional organization. This new macro-scale understanding of the range and poles of brain function has revealed that a broad spectrum of human neural processing categories (primary, task positive, task negative) is represented not only in the cerebral cortex, but also in the thalamus, striatum, and cerebellar cortex. Whether functional organization in DN obeys a similar set of macroscale divisions, and whether DN are yet another compartment of representation of a broad spectrum of human neural processing categories, remains unknown. Here we show for the first time that human DN is optimally divided into three functional territories as indexed by high spatio-temporal resolution resting-state MRI in 60 healthy adolescents, and that these three distinct territories contribute uniquely to default-mode, salience-motor, and visual brain networks. These conclusions are supported by novel analytical strategies in human studies of DN organization, including 64-channel MRI imaging, data-driven methods, and replication in an independent sample. Our findings provide a systems neuroscience substrate for cerebellar output to influence multiple broad categories of neural control -namely defaultmode, attentional, and multiple unimodal streams of information processing including motor and visual. They also provide a validated data-driven mapping of functions in human DN, crucial for the design of methodology and interpretation of results in future neuroimaging studies of brain function and dysfunction.

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“…Besides the above discussed frontotemporal networks, AD is characterized by a decline in critical parietal networks such as precuneus (Apostolova and Thompson 2008;Bailly et al 2015;Fennema-Notestine et al 2009;Scahill et al 2002;Walhovd et al 2010;Whitwell et al 2008). Cerebellum, a critical brain region in several motor, cognitive and behavioral functions, is also more recently being increasingly suggested as a direct contributor to cognitive and neuropsychiatric deficits in AD (Guo et al 2016;Jacobs et al 2017;Schmahmann 2016). Deteriorating cerebellum health resulting in several symptoms such as lack of balance and coordination, tremors, slurred speech and abnormal eye movements in the elderly.…”

Section: Discriminative Brain Regionsmentioning

confidence: 99%

Deep Residual Learning for Neuroimaging: An application to Predict Progression to Alzheimer’s Disease

Abrol

Bhattarai

Fedorov

et al. 2018

Preprint

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ABSTRACTThis work investigates the suitability of deep residual neural networks (ResNets) for studying neuroimaging data in the specific application of predicting progression from mild cognitive impairment (MCI) to Alzheimer’s disease (AD). We focus on predicting the subset of MCI individuals that would progress to AD within three years (progressive MCI) and the subset of MCI individuals that do not progress to AD within this period (stable MCI). This prediction was conducted first as a standard binary classification task by training a ResNet architecture using MCI individuals only, followed by a modified domain transfer learning version that additionally trained on the AD and cognitively normal (CN) individuals. For this modified inter-MCI classification task, the ResNet architecture achieved a significant performance improvement over the classical support vector machine and the stacked autoencoder machine learning frameworks (p< 0.005), numerically better than state-of-the-art performance in predicting progression to AD using structural MRI data alone (> 7% than the second-best performing method) and within 1% of the state-of-the-art performance considering learning using multiple structural modalities as well. The learnt predictive models in this modified classification task showed highly similar peak activations, significant correspondence of which in the medial temporal lobe and other areas could be established with previous reports in AD literature, thus further validating our findings. Our results highlight the possibility of early identification of modifiable risk factors for understanding progression to AD using similar advanced deep learning architectures.

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The cerebellum in Alzheimer’s disease: evaluating its role in cognitive decline

Cited by 231 publications

References 127 publications

Transcriptomic analysis of probable asymptomatic and symptomatic Alzheimer brains

Transcriptomic analysis of probable asymptomatic and symptomatic Alzheimer brains

Functional Territories of Human Dentate Nucleus

Deep Residual Learning for Neuroimaging: An application to Predict Progression to Alzheimer’s Disease

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