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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.

Hexanucleotide repeat expansion in C9orf72 represents the most common genetic cause of familial and sporadic behavioural variant frontotemporal dementia. Previous studies show that some C9orf72 carriers with behavioural variant frontotemporal dementia exhibit distinctive atrophy patterns whereas others show mild or undetectable atrophy despite severe behavioural impairment. To explore this observation, we examined intrinsic connectivity network integrity in patients with or without the C9orf72 expansion. We studied 28 patients with behavioural variant frontotemporal dementia, including 14 C9orf72 mutation carriers (age 58.3 ± 7.7 years, four females) and 14 non-carriers (age 60.8 ± 6.9 years, four females), and 14 age- and sex-matched healthy controls. Both patient groups included five patients with comorbid motor neuron disease. Neuropsychological data, structural brain magnetic resonance imaging, and task-free functional magnetic resonance imaging were obtained. Voxel-based morphometry delineated atrophy patterns, and seed-based intrinsic connectivity analyses enabled group comparisons of the salience, sensorimotor, and default mode networks. Single-patient analyses were used to explore network imaging as a potential biomarker. Despite contrasting atrophy patterns in C9orf72 carriers versus non-carriers, patient groups showed topographically similar connectivity reductions in the salience and sensorimotor networks. Patients without C9orf72 expansions exhibited increases in default mode network connectivity compared to controls and mutation carriers. Across all patients, behavioural symptom severity correlated with diminished salience network connectivity and heightened default mode network connectivity. In C9orf72 carriers, salience network connectivity reduction correlated with atrophy in the left medial pulvinar thalamic nucleus, and this region further showed diminished connectivity with key salience network hubs. Single-patient analyses revealed salience network disruption and default mode network connectivity enhancement in C9orf72 carriers with early-stage or slowly progressive symptoms. The findings suggest that patients with behavioural variant frontotemporal dementia with or without the C9orf72 expansion show convergent large-scale network breakdowns despite distinctive atrophy patterns. Medial pulvinar degeneration may contribute to the behavioural variant frontotemporal dementia syndrome in C9orf72 carriers by disrupting salience network connectivity. Task-free functional magnetic resonance imaging shows promise in detecting early-stage disease in C9orf72 carriers and may provide a unifying biomarker across diverse anatomical variants.

Body-worn sensors have contributed to a rich and growing body of literature in public health and clinical research in the last decades. A major challenge in sensor research is the lack of consistency and standardization of the collection and reporting of the sensor data. The algorithms used to derive these activity counts can be vastly different between manufactures and not always transparent to the researchers. With Philips, one of the major research-grade wearable device manufacturers, discontinuing this product line, many researchers are left in need of alternative solutions and at the risk of not being able to relate their historical data using the Philips Actiwatch 2 devices to future findings with other devices. We herein provide a comparison analysis and conversion method that can be used to convert activity counts from Philips to those from ActiGraph, another major manufacturer who provide both raw acceleration data and count data based on their open-source algorithm to the research community. This work provides an approach to maximize the scientific value of historical actigraphy data collected by the Actiwatch devices to support research continuity in this community. The conversion, however, is not perfect and only offers an approximation, due to the intrinsic difference in the count algorithms between the two accelerometers, and the permanent information loss during data reduction. We encourage future research using body-worn sensors to retain the raw sensor data to ensure data consistency, comparability, and the ability to leverage future algorithm improvement. [ABSTRACT FROM AUTHOR]

The neural organization of semantic memory remains much debated. A 'distributed-only' view contends that semantic knowledge is represented within spatially distant, modality-selective primary and association cortices. Observations in semantic variant primary progressive aphasia have inspired an alternative model featuring the anterior temporal lobe as an amodal hub that supports semantic knowledge by linking distributed modality-selective regions. Direct evidence has been lacking, however, to support intrinsic functional interactions between an anterior temporal lobe hub and upstream sensory regions in humans. Here, we examined the neural networks supporting semantic knowledge by performing a multimodal brain imaging study in healthy subjects and patients with semantic variant primary progressive aphasia. In healthy subjects, the anterior temporal lobe showed intrinsic connectivity to an array of modality-selective primary and association cortices. Patients showed focal anterior temporal lobe degeneration but also reduced physiological integrity throughout distributed modality-selective regions connected with the anterior temporal lobe in healthy controls. Physiological deficits outside the anterior temporal lobe correlated with scores on semantic tasks and with anterior temporal subregion atrophy, following domain-specific and connectivity-based predictions. The findings provide a neurophysiological basis for the theory that semantic processing is orchestrated through interactions between a critical anterior temporal lobe hub and modality-selective processing nodes.

ObjectiveProgressive supranuclear palsy (PSP) has been conceptualized as a large-scale network disruption, but the specific network targeted has not been fully characterized. We sought to delineate the affected network in patients with clinical PSP.MethodsUsing task-free functional magnetic resonance imaging, we mapped intrinsic connectivity to the dorsal midbrain tegmentum (dMT), a region that shows focal atrophy in PSP. Two healthy control groups (1 young, 1 older) were used to define and replicate the normal connectivity pattern, and patients with PSP were compared to an independent matched healthy control group on measures of network connectivity.ResultsHealthy young and older subjects showed a convergent pattern of connectivity to the dMT, including brainstem, cerebellar, diencephalic, basal ganglia, and cortical regions involved in skeletomotor, oculomotor, and executive control. Patients with PSP showed significant connectivity disruptions within this network, particularly within corticosubcortical and cortico-brainstem interactions. Patients with more severe functional impairment showed lower mean dMT network connectivity scores.InterpretationThis study defines a PSP-related intrinsic connectivity network in the healthy brain and demonstrates the sensitivity of network-based imaging methods to PSP-related physiological and clinical changes.

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. [ABSTRACT FROM AUTHOR]

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 fromsimultaneous 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. [ABSTRACT FROM AUTHOR]

For learning to occur through trial and error, the nervous system must effectively detect and encode performance errors. To examine this process, we designed a set of oculomotor learning tasks with more than one visual object providing potential error cues, as would occur in a natural visual scene. A task-relevant visual target and a task-irrelevant visual background both influenced vestibulo-ocular reflex learning in rhesus monkeys. Thus, motor learning does not identify a single error cue based on behavioral relevance, but can be simultaneously influenced bymorethanonecue. Moreover, the relative weighting of the different cues could vary. If the speed of the visual target'smotiononthe retinawas low (≪1°/s), background motion dominated learning, but if target speed was high, the effects of the background were suppressed. The target and background motion had similar, nonlinear effects on the putative neural instructive signals carried by cerebellar climbing fibers, but with a stronger influence of the background on the climbing fibers than on learning. Incontrast, putative neural instructive signals carried by the simple spikes of Purkinje cells were influenced solely by the motion of the visual target. Because they are influenced by different cues during training, joint control of learning by the climbing fibers and Purkinje cells may expand the learning capacity of the cerebellar circuit. [ABSTRACT FROM AUTHOR]

Background: Smartphones and wearables are revolutionizing the assessment of cognitive and motor function in neurological disorders, allowing for objective, frequent, and remote data collection. However, these assessments typically provide a plethora of sensor-derived measures (SDMs), and selecting the most suitable measure for a given context of use is a challenging, often overlooked problem., Objective: This analysis aims to develop and apply an SDM selection framework, including automated data quality checks and the evaluation of statistical properties, to identify robust SDMs that describe the cognitive and motor function of people with multiple sclerosis (MS)., Methods: The proposed framework was applied to data from a cross-sectional study involving 85 people with MS and 68 healthy participants who underwent in-clinic supervised and remote unsupervised smartphone-based assessments. The assessment provided high-quality recordings from cognitive, manual dexterity, and mobility tests, from which 47 SDMs, based on established literature, were extracted using previously developed and publicly available algorithms. These SDMs were first separately and then jointly screened for bias and normality by 2 expert assessors. Selected SDMs were then analyzed to establish their reliability, using an intraclass correlation coefficient and minimal detectable change at 95% CI. The convergence of selected SDMs with in-clinic MS functional measures and patient-reported outcomes was also evaluated., Results: A total of 16 (34%) of the 47 SDMs passed the selection framework. All selected SDMs demonstrated moderate-to-good reliability in remote settings (intraclass correlation coefficient 0.5-0.85; minimal detectable change at 95% CI 19%-35%). Selected SDMs extracted from the smartphone-based cognitive test demonstrated good-to-excellent correlation (Spearman correlation coefficient, |ρ|>0.75) with the in-clinic Symbol Digit Modalities Test and fair correlation with Expanded Disability Status Scale (EDSS) scores (0.25≤|ρ|<0.5). SDMs extracted from the manual dexterity tests showed either fair correlation (0.25≤|ρ|<0.5) or were not correlated (|ρ|<0.25) with the in-clinic 9-hole peg test and EDSS scores. Most selected SDMs from mobility tests showed fair correlation with the in-clinic timed 25-foot walk test and fair to moderate-to-good correlation (0.5<|ρ|≤0.75) with EDSS scores. SDM correlations with relevant patient-reported outcomes varied by functional domain, ranging from not correlated (cognitive test SDMs) to good-to-excellent correlation (|ρ|>0.75) for mobility test SDMs. Overall, correlations were similar when smartphone-based tests were performed in a clinic or remotely., Conclusions: Reported results highlight that smartphone-based assessments are suitable tools to remotely obtain high-quality SDMs of cognitive and motor function in people with MS. The presented SDM selection framework promises to increase the interpretability and standardization of smartphone-based SDMs in people with MS, paving the way for their future use in interventional trials., (©Matthew Scaramozza, Aurélie Ruet, Patrizia A Chiesa, Laïtissia Ahamada, Emmanuel Bartholomé, Loïc Carment, Julie Charre-Morin, Gautier Cosne, Léa Diouf, Christine C Guo, Adrien Juraver, Christoph M Kanzler, Angelos Karatsidis, Claudia Mazzà, Joaquin Penalver-Andres, Marta Ruiz, Aurore Saubusse, Gabrielle Simoneau, Alf Scotland, Zhaonan Sun, Minao Tang, Johan van Beek, Lauren Zajac, Shibeshih Belachew, Bruno Brochet, Nolan Campbell. Originally published in JMIR Formative Research (https://formative.jmir.org), 08.11.2024.)

The increased use of sensor-based digital health technologies (DHTs) in clinical trials brought to light concerns about implementation practices that might introduce burden on trial participants, resulting in suboptimal compliance and become an additional complicating factor in clinical trial conduct. These concerns may contribute to the lower-than-anticipated uptake of DHT deployment and data use for regulatory decision-making, despite well-articulated benefits. The Electronic Clinical Outcome Assessment (eCOA) Consortium gathered collective experience on deploying sensor-based DHTs and supplemented this with relevant literature focusing on mechanisms that may enhance participant compliance. The process for DHT implementation starts with identifying a clinical concept of interest followed by a digital measure selection, defining active or passive data capture and their sources, the number of sensors with respective body location, plus the duration and frequency of use in the context of perceived participant burden. Roundtable discussions among patient groups, physicians, and technology providers prior to protocol development can be very impactful for optimizing trial design. While diversity and inclusion are essential for any clinical trial, patient populations should be considered carefully in the context of trial-specific aims, requirements, and anticipated patient burden. Minimizing site burden includes assessment of training, research engagement, and logistical burden which needs to be triaged differently for early and late-stage clinical trials. Additional considerations include sharing trial results with study participants and leveraging publicly available data for compliance modeling. To the best of our knowledge, this report provides holistic considerations for sensor-based DHT implementation that may optimize participant compliance., (© 2024 The Author(s). Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)