Advanced patient-specific microglia cell models for pre-clinical studies in Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is an incurable neurodegenerative disorder with a rapidly increasing prevalence worldwide. Current approaches targeting hallmark pathological features of AD have had no consistent clinical benefit. Neuroinflammation is a major contributor to neurodegeneration and hence, microglia, the brain's resident immune cells, are an attractive target for potentially more effective therapeutic strategies. However, there is no current in vitro model system that captures AD patient-specific microglial characteristics using physiologically relevant and experimentally flexible culture conditions. METHODS: To address this shortcoming, we developed novel 3D Matrigel-based monocyte-derived microglia-like cell (MDMi) mono-cultures and co-cultures with neuro-glial cells (ReNcell VM). We used single-cell RNA sequencing (scRNAseq) analysis to compare the transcriptomic signatures of MDMi between model systems (2D, 3D and 3D co-culture) and against published human microglia datasets. To demonstrate the potential of MDMi for use in personalized pre-clinical strategies, we generated and characterized MDMi models from sixteen AD patients and matched healthy controls, and profiled cytokine responses upon treatment with anti-inflammatory drugs (dasatinib and spiperone). RESULTS: MDMi in 3D exhibited a more branched morphology and longer survival in culture compared to 2D. scRNAseq uncovered distinct MDMi subpopulations that exhibit higher functional heterogeneity and best resemble human microglia in 3D co-culture. AD MDMi in 3D co-culture showed altered cell-to-cell interactions, growth factor and cytokine secretion profiles and responses to amyloid-ß. Drug testing assays revealed patient- and model-specific cytokine responses. CONCLUSION: Our study presents a novel, physiologically relevant and AD patient-specific 3D microglia cell model that opens avenues towards improving personalized drug development strategies in AD.

40 years of actigraphy in sleep medicine and current state of the art algorithms.

For the last 40 years, actigraphy or wearable accelerometry has provided an objective, low-burden and ecologically valid approach to assess real-world sleep and circadian patterns, contributing valuable data to epidemiological and clinical insights on sleep and sleep disorders. The proper use of wearable technology in sleep research requires validated algorithms that can derive sleep outcomes from the sensor data. Since the publication of the first automated scoring algorithm by Webster in 1982, a variety of sleep algorithms have been developed and contributed to sleep research, including many recent ones that leverage machine learning and / or deep learning approaches. However, it remains unclear how these algorithms compare to each other on the same data set and if these modern data science approaches improve the analytical validity of sleep outcomes based on wrist-worn acceleration data. This work provides a systematic evaluation across 8 state-of-the-art sleep algorithms on a common sleep data set with polysomnography (PSG) as ground truth. Despite the inclusion of recently published complex algorithms, simple regression-based and heuristic algorithms demonstrated slightly superior performance in sleep-wake classification and sleep outcome estimation. The performance of complex machine learning and deep learning models seem to suffer from poor generalization. This independent and systematic analytical validation of sleep algorithms provides key evidence on the use of wearable digital health technologies for sleep research and care.

Functional re-organization of hippocampal-cortical gradients during naturalistic memory processes.

The functional organization of the hippocampus mirrors that of the cortex, changing smoothly along connectivity gradients and abruptly at inter-areal boundaries. Hippocampal-dependent cognitive processes require flexible integration of these hippocampal gradients into functionally related cortical networks. To understand the cognitive relevance of this functional embedding, we acquired fMRI data while participants viewed brief news clips, either containing or lacking recently familiarized cues. Participants were 188 healthy mid-life adults and 31 adults with mild cognitive impairment (MCI) or Alzheimer's disease (AD). We employed a recently developed technique - connectivity gradientography - to study gradually changing patterns of voxel to whole brain functional connectivity and their sudden transitions. We observed that functional connectivity gradients of the anterior hippocampus map onto connectivity gradients across the default mode network during these naturalistic stimuli. The presence of familiar cues in the news clips accentuates a stepwise transition across the boundary from the anterior to the posterior hippocampus. This functional transition is shifted in the posterior direction in the left hippocampus of individuals with MCI or AD. These findings shed new light on the functional integration of hippocampal connectivity gradients into large-scale cortical networks, how these adapt with memory context and how these change in the presence of neurodegenerative disease.

Lower hypothalamic volume with lower body mass index is associated with shorter survival in patients with amyotrophic lateral sclerosis.

BACKGROUND AND PURPOSE: Weight loss in patients with amyotrophic lateral sclerosis (ALS) is associated with faster disease progression and shorter survival. Decreased hypothalamic volume is proposed to contribute to weight loss due to loss of appetite and/or hypermetabolism. We aimed to investigate the relationship between hypothalamic volume and body mass index (BMI) in ALS and Alzheimer's disease (AD), and the associations of hypothalamic volume with weight loss, appetite, metabolism and survival in patients with ALS. METHODS: We compared hypothalamic volumes from magnetic resonance imaging scans with BMI for patients with ALS (n = 42), patients with AD (n = 167) and non-neurodegenerative disease controls (n = 527). Hypothalamic volumes from patients with ALS were correlated with measures of appetite and metabolism, and change in anthropomorphic measures and disease outcomes. RESULTS: Lower hypothalamic volume was associated with lower and higher BMI in ALS (quadratic association; probability of direction = 0.96). This was not observed in AD patients or controls. Hypothalamic volume was not associated with loss of appetite (p = 0.58) or hypermetabolism (p = 0.49). Patients with lower BMI and lower hypothalamic volume tended to lose weight (p = 0.08) and fat mass (p = 0.06) over the course of their disease, and presented with an increased risk of earlier death (hazard ratio [HR] 3.16, p = 0.03). Lower hypothalamic volume alone trended for greater risk of earlier death (HR 2.61, p = 0.07). CONCLUSION: These observations suggest that lower hypothalamic volume in ALS contributes to positive and negative energy balance, and  is not universally associated with loss of appetite or hypermetabolism. Critically, lower hypothalamic volume with lower BMI was associated with weight loss and earlier death.

Digital Devices for Assessing Motor Functions in Mobility-Impaired and Healthy Populations: Systematic Literature Review.

BACKGROUND: With the advent of smart sensing technology, mobile and wearable devices can provide continuous and objective monitoring and assessment of motor function outcomes. OBJECTIVE: We aimed to describe the existing scientific literature on wearable and mobile technologies that are being used or tested for assessing motor functions in mobility-impaired and healthy adults and to evaluate the degree to which these devices provide clinically valid measures of motor function in these populations. METHODS: A systematic literature review was conducted by searching Embase, MEDLINE, CENTRAL (January 1, 2015, to June 24, 2020), the United States and European Union clinical trial registries, and the United States Food and Drug Administration website using predefined study selection criteria. Study selection, data extraction, and quality assessment were performed by 2 independent reviewers. RESULTS: A total of 91 publications representing 87 unique studies were included. The most represented clinical conditions were Parkinson disease (n=51 studies), followed by stroke (n=5), Huntington disease (n=5), and multiple sclerosis (n=2). A total of 42 motion-detecting devices were identified, and the majority (n=27, 64%) were created for the purpose of health care-related data collection, although approximately 25% were personal electronic devices (eg, smartphones and watches) and 11% were entertainment consoles (eg, Microsoft Kinect or Xbox and Nintendo Wii). The primary motion outcomes were related to gait (n=30), gross motor movements (n=25), and fine motor movements (n=23). As a group, sensor-derived motion data showed a mean sensitivity of 0.83 (SD 7.27), a mean specificity of 0.84 (SD 15.40), a mean accuracy of 0.90 (SD 5.87) in discriminating between diseased individuals and healthy controls, and a mean Pearson r validity coefficient of 0.52 (SD 0.22) relative to clinical measures. We did not find significant differences in the degree of validity between in-laboratory and at-home sensor-based assessments nor between device class (ie, health care-related device, personal electronic devices, and entertainment consoles). CONCLUSIONS: Sensor-derived motion data can be leveraged to classify and quantify disease status for a variety of neurological conditions. However, most of the recent research on digital clinical measures is derived from proof-of-concept studies with considerable variation in methodological approaches, and much of the reviewed literature has focused on clinical validation, with less than one-quarter of the studies performing analytical validation. Overall, future research is crucially needed to further consolidate that sensor-derived motion data may lead to the development of robust and transformative digital measurements intended to predict, diagnose, and quantify neurological disease state and its longitudinal change.

Quantification of acceleration as activity counts in ActiGraph wearable.

Digital clinical measures based on data collected by wearable devices have seen rapid growth in both clinical trials and healthcare. The widely-used measures based on wearables are epoch-based physical activity counts using accelerometer data. Even though activity counts have been the backbone of thousands of clinical and epidemiological studies, there are large variations of the algorithms that compute counts and their associated parameters-many of which have often been kept proprietary by device providers. This lack of transparency has hindered comparability between studies using different devices and limited their broader clinical applicability. ActiGraph devices have been the most-used wearable accelerometer devices for over two decades. Recognizing the importance of data transparency, interpretability and interoperability to both research and clinical use, we here describe the detailed counts algorithms of five generations of ActiGraph devices going back to the first AM7164 model, and publish the current counts algorithm in ActiGraph's ActiLife and CentrePoint software as a standalone Python package for research use. We believe that this material will provide a useful resource for the research community, accelerate digital health science and facilitate clinical applications of wearable accelerometry.

ALS monocyte-derived microglia-like cells reveal cytoplasmic TDP-43 accumulation, DNA damage, and cell-specific impairment of phagocytosis associated with disease progression.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a multifactorial neurodegenerative disease characterised by the loss of upper and lower motor neurons. Increasing evidence indicates that neuroinflammation mediated by microglia contributes to ALS pathogenesis. This microglial activation is evident in post-mortem brain tissues and neuroimaging data from patients with ALS. However, the role of microglia in the pathogenesis and progression of amyotrophic lateral sclerosis remains unclear, partly due to the lack of a model system that is able to faithfully recapitulate the clinical pathology of ALS. To address this shortcoming, we describe an approach that generates monocyte-derived microglia-like cells that are capable of expressing molecular markers, and functional characteristics similar to in vivo human brain microglia. METHODS: In this study, we have established monocyte-derived microglia-like cells from 30 sporadic patients with ALS, including 15 patients with slow disease progression, 6 with intermediate progression, and 9 with rapid progression, together with 20 non-affected healthy controls. RESULTS: We demonstrate that patient monocyte-derived microglia-like cells recapitulate canonical pathological features of ALS including non-phosphorylated and phosphorylated-TDP-43-positive inclusions. Moreover, ALS microglia-like cells showed significantly impaired phagocytosis, altered cytokine profiles, and abnormal morphologies consistent with a neuroinflammatory phenotype. Interestingly, all ALS microglia-like cells showed abnormal phagocytosis consistent with the progression of the disease. In-depth analysis of ALS microglia-like cells from the rapid disease progression cohort revealed significantly altered cell-specific variation in phagocytic function. In addition, DNA damage and NOD-leucine rich repeat and pyrin containing protein 3 (NLRP3) inflammasome activity were also elevated in ALS patient monocyte-derived microglia-like cells, indicating a potential new pathway involved in driving disease progression. CONCLUSIONS: Taken together, our work demonstrates that the monocyte-derived microglia-like cell model recapitulates disease-specific hallmarks and characteristics that substantiate patient heterogeneity associated with disease subgroups. Thus, monocyte-derived microglia-like cells are highly applicable to monitor disease progression and can be applied as a functional readout in clinical trials for anti-neuroinflammatory agents, providing a basis for personalised treatment for patients with ALS.

A prospective cohort study of prodromal Alzheimer's disease: Prospective Imaging Study of Ageing: Genes, Brain and Behaviour (PISA).

This prospective cohort study, "Prospective Imaging Study of Ageing: Genes, Brain and Behaviour" (PISA) seeks to characterise the phenotype and natural history of healthy adult Australians at high future risk of Alzheimer's disease (AD). In particular, we are recruiting midlife and older Australians with high and low genetic risk of dementia to discover biological markers of early neuropathology, identify modifiable risk factors, and establish the very earliest phenotypic and neuronal signs of disease onset. PISA utilises genetic prediction to recruit and enrich a prospective cohort and follow them longitudinally. Online surveys and cognitive testing are used to characterise an Australia-wide sample currently totalling over 3800 participants. Participants from a defined at-risk cohort and positive controls (clinical cohort of patients with mild cognitive impairment or early AD) are invited for onsite visits for detailed functional, structural and molecular neuroimaging, lifestyle monitoring, detailed neurocognitive testing, plus blood sample donation. This paper describes recruitment of the PISA cohort, study methodology and baseline demographics.

Cerebellar Purkinje cells control eye movements with a rapid rate code that is invariant to spike irregularity.

The rate and temporal pattern of neural spiking each have the potential to influence computation. In the cerebellum, it has been hypothesized that the irregularity of interspike intervals in Purkinje cells affects their ability to transmit information to downstream neurons. Accordingly, during oculomotor behavior in mice and rhesus monkeys, mean irregularity of Purkinje cell spiking varied with mean eye velocity. However, moment-to-moment variations revealed a tight correlation between eye velocity and spike rate, with no additional information conveyed by spike irregularity. Moreover, when spike rate and irregularity were independently controlled using optogenetic stimulation, the eye movements elicited were well-described by a linear population rate code with 3-5 ms temporal precision. Biophysical and random-walk models identified biologically realistic parameter ranges that determine whether spike irregularity influences responses downstream. The results demonstrate cerebellar control of movements through a remarkably rapid rate code, with no evidence for an additional contribution of spike irregularity.

Detecting changes in facial temperature induced by a sudden auditory stimulus based on deep learning-assisted face tracking.

Thermal Imaging (Infrared-Imaging-IRI) is a promising new technique for psychophysiological research and application. Unlike traditional physiological measures (like skin conductance and heart rate), it is uniquely contact-free, substantially enhancing its ecological validity. Investigating facial regions and subsequent reliable signal extraction from IRI data is challenging due to head motion artefacts. Exploiting its potential thus depends on advances in analytical methods. Here, we developed a novel semi-automated thermal signal extraction method employing deep learning algorithms for facial landmark identification. We applied this method to physiological responses elicited by a sudden auditory stimulus, to determine if facial temperature changes induced by a stimulus of a loud sound can be detected. We compared thermal responses with psycho-physiological sensor-based tools of galvanic skin response (GSR) and electrocardiography (ECG). We found that the temperatures of selected facial regions, particularly the nose tip, significantly decreased after the auditory stimulus. Additionally, this response was quite rapid at around 4-5 seconds, starting less than 2 seconds following the GSR changes. These results demonstrate that our methodology offers a sensitive and robust tool to capture facial physiological changes with minimal manual intervention and manual pre-processing of signals. Newer methodological developments for reliable temperature extraction promise to boost IRI use as an ecologically-valid technique in social and affective neuroscience.

Effective connectivity of the anterior hippocampus predicts recollection confidence during natural memory retrieval.

Human interactions with the world are influenced by memories of recent events. This effect, often triggered by perceptual cues, occurs naturally and without conscious effort. However, the neuroscience of involuntary memory in a dynamic milieu has received much less attention than the mechanisms of voluntary retrieval with deliberate purpose. Here, we investigate the neural processes driven by naturalistic cues that relate to, and presumably trigger the retrieval of recent experiences. Viewing the continuation of recently viewed clips evokes greater bilateral activation in anterior hippocampus, precuneus and angular gyrus than naïve clips. While these regions manifest reciprocal connectivity, continued viewing specifically modulates the effective connectivity from the anterior hippocampus to the precuneus. The strength of this modulation predicts participants' confidence in later voluntary recall of news details. Our study reveals network mechanisms of dynamic, involuntary memory retrieval and its relevance to metacognition in a rich context resembling everyday life.

Neural Correlates of Temporal Complexity and Synchrony during Audiovisual Correspondence Detection.

We often perceive real-life objects as multisensory cues through space and time. A key challenge for audiovisual integration is to match neural signals that not only originate from different sensory modalities but also that typically reach the observer at slightly different times. In humans, complex, unpredictable audiovisual streams lead to higher levels of perceptual coherence than predictable, rhythmic streams. In addition, perceptual coherence for complex signals seems less affected by increased asynchrony between visual and auditory modalities than for simple signals. Here, we used functional magnetic resonance imaging to determine the human neural correlates of audiovisual signals with different levels of temporal complexity and synchrony. Our study demonstrated that greater perceptual asynchrony and lower signal complexity impaired performance in an audiovisual coherence-matching task. Differences in asynchrony and complexity were also underpinned by a partially different set of brain regions. In particular, our results suggest that, while regions in the dorsolateral prefrontal cortex (DLPFC) were modulated by differences in memory load due to stimulus asynchrony, areas traditionally thought to be involved in speech production and recognition, such as the inferior frontal and superior temporal cortex, were modulated by the temporal complexity of the audiovisual signals. Our results, therefore, indicate specific processing roles for different subregions of the fronto-temporal cortex during audiovisual coherence detection.

Cerebellar atrophy in neurodegeneration-a meta-analysis.

INTRODUCTION: The cerebellum has strong cortical and subcortical connectivity, but is rarely taken into account for clinical diagnosis in many neurodegenerative conditions, particularly in the absence of clinical ataxia. The current meta-analysis aims to assess patterns of cerebellar grey matter atrophy in seven neurodegenerative conditions (Alzheimer's disease (AD), Parkinson's disease (PD) and Huntington's disease (HD), frontotemporal dementia (FTD), amyotrophic lateral sclerosis (ALS), multiple system atrophy (MSA), progressive supranuclear palsy (MSP)). METHODS: We carried out a systematic search in PubMed (any date: 14 July 2016) and a hand search of references from pertinent articles according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The authors were contacted to provide missing coordinate data. Peer-reviewed studies with direct comparison of patient and control groups, and availability of coordinate data of grey matter cerebellar atrophy in patients were included. These coordinates were used in an anatomical likelihood estimation meta-analysis. RESULTS: Across 54 studies, clusters of cerebellar atrophy were found for AD, ALS, FTD, MSA, and PSP. Atrophy patterns were largely disease-specific, with overlap in certain areas of the cerebellar hemisphere, which showed marked atrophy in AD, ALS, FTD and PSP (Crus I/II), and MSA and PSP (lobules I-IV), respectively. Atrophy colocated with cerebellar areas implicated for motor (PSP, MSA) or cognitive symptoms (FTD, ALS, PSP) in the diseases. DISCUSSION: Our findings suggest that cerebellar changes are largely disease-specific and correspond to cortical or subcortical changes in neurodegenerative conditions. High clinical variability in PD and HD samples may explain the absence of findings for consistent grey matter loss across studies. Our results have clinical implications for diagnosis and cerebellar neuroimaging referencing approaches.

Improving the Test-Retest Reliability of Resting State fMRI by Removing the Impact of Sleep.

Resting state functional magnetic resonance imaging (rs-fMRI) provides a powerful tool to examine large-scale neural networks in the human brain and their disturbances in neuropsychiatric disorders. Thanks to its low demand and high tolerance, resting state paradigms can be easily acquired from clinical population. However, due to the unconstrained nature, resting state paradigm is associated with excessive head movement and proneness to sleep. Consequently, the test-retest reliability of rs-fMRI measures is moderate at best, falling short of widespread use in the clinic. Here, we characterized the effect of sleep on the test-retest reliability of rs-fMRI. Using measures of heart rate variability (HRV) derived from simultaneous electrocardiogram (ECG) recording, we identified portions of fMRI data when subjects were more alert or sleepy, and examined their effects on the test-retest reliability of functional connectivity measures. When volumes of sleep were excluded, the reliability of rs-fMRI is significantly improved, and the improvement appears to be general across brain networks. The amount of improvement is robust with the removal of as much as 60% volumes of sleepiness. Therefore, test-retest reliability of rs-fMRI is affected by sleep and could be improved by excluding volumes of sleepiness as indexed by HRV. Our results suggest a novel and practical method to improve test-retest reliability of rs-fMRI measures.

Dominant hemisphere lateralization of cortical parasympathetic control as revealed by frontotemporal dementia.

The brain continuously influences and perceives the physiological condition of the body. Related cortical representations have been proposed to shape emotional experience and guide behavior. Although previous studies have identified brain regions recruited during autonomic processing, neurological lesion studies have yet to delineate the regions critical for maintaining autonomic outflow. Even greater controversy surrounds hemispheric lateralization along the parasympathetic-sympathetic axis. The behavioral variant of frontotemporal dementia (bvFTD), featuring progressive and often asymmetric degeneration that includes the frontoinsular and cingulate cortices, provides a unique lesion model for elucidating brain structures that control autonomic tone. Here, we show that bvFTD is associated with reduced baseline cardiac vagal tone and that this reduction correlates with left-lateralized functional and structural frontoinsular and cingulate cortex deficits and with reduced agreeableness. Our results suggest that networked brain regions in the dominant hemisphere are critical for maintaining an adaptive level of baseline parasympathetic outflow.

Network-selective vulnerability of the human cerebellum to Alzheimer's disease and frontotemporal dementia.

SEE SCHMAHMANN DOI101093/BRAIN/AWW064 FOR A SCIENTIFIC COMMENTARY ON THIS ARTICLE: Neurodegenerative diseases are associated with distinct and distributed patterns of atrophy in the cerebral cortex. Emerging evidence suggests that these atrophy patterns resemble intrinsic connectivity networks in the healthy brain, supporting the network-based degeneration framework where neuropathology spreads across connectivity networks. An intriguing yet untested possibility is that the cerebellar circuits, which share extensive connections with the cerebral cortex, could be selectively targeted by major neurodegenerative diseases. Here we examined the structural atrophy in the cerebellum across common types of neurodegenerative diseases, and characterized the functional connectivity patterns of these cerebellar atrophy regions. Our results showed that Alzheimer's disease and frontotemporal dementia are associated with distinct and circumscribed atrophy in the cerebellum. These cerebellar atrophied regions share robust and selective intrinsic connectivity with the atrophied regions in the cerebral cortex. These findings for the first time demonstrated the selective vulnerability of the cerebellum to common neurodegenerative disease, extending the network-based degeneration framework to the cerebellum. Our work also has direct implications on the cerebellar contribution to the cognitive and affective processes that are compromised in neurodegeneration as well as the practice of using the cerebellum as reference region for ligand neuroimaging studies.

Out-of-sync: disrupted neural activity in emotional circuitry during film viewing in melancholic depression.

While a rich body of research in controlled experiments has established changes in the neural circuitry of emotion in major depressive disorders, little is known as to how such alterations might translate into complex, naturalistic settings--namely involving dynamic multimodal stimuli with rich contexts, such as those provided by films. Neuroimaging paradigms employing dynamic natural stimuli alleviate the anxiety often associated with complex tasks and eschew the need for laboratory-style abstractions, hence providing an ecologically valid means of elucidating neural underpinnings of neuropsychiatric disorders. To probe the neurobiological signature of refined depression subtypes, we acquired functional neuroimaging data in patients with the melancholic subtype of major depressive disorder during free viewing of emotionally salient films. We found a marked disengagement of ventromedial prefrontal cortex during natural viewing of a film with negative emotional valence in patients with melancholia. This effect significantly correlated with depression severity. Such changes occurred on the background of diminished consistency of neural activity in visual and auditory sensory networks, as well as higher-order networks involved in emotion and attention, including bilateral intraparietal sulcus and right anterior insula. These findings may reflect a failure to re-allocate resources and diminished reactivity to external emotional stimuli in melancholia.

The anterior insula shows heightened interictal intrinsic connectivity in migraine without aura.

OBJECTIVE: We sought to explore whether patients with migraine show heightened interictal intrinsic connectivity within primary sensory networks, the salience network, and a network anchored by the dorsal pons, a region known to be active during migraine attacks. METHODS: Using task-free fMRI and a region-of-interest analysis, we compared intrinsic connectivity patterns in 15 migraineurs without aura to 15 age- and sex-matched healthy controls, focusing on networks anchored by the calcarine cortex, Heschl gyrus, right anterior insula, and dorsal pons, a region active during migraine attacks. We also examined the relationship between network connectivity, migraine frequency, and sensory sensitivity symptoms. RESULTS: Migraineurs showed increased connectivity between primary visual and auditory cortices and the right dorsal anterior insula, between the dorsal pons and the bilateral anterior insulae, and between the right and left ventral anterior insulae. Increased connectivity showed no clinical correlation with migraine frequency or sensory sensitivity. CONCLUSIONS: Patients with migraine display interictal changes in the topology of intrinsic connections, with greater connectivity between primary sensory cortices, the pons, and the anterior insula, a region involved in representing and coordinating responses to emotional salience.