Epidemiological studies suggest that insulin resistance accelerates progression of age-based cognitive impairment, which neuroimaging has linked to brain glucose hypometabolism. As cellular inputs, ketones increase Gibbs free energy change for ATP by 27% compared to glucose. Here we test whether dietary changes are capable of modulating sustained functional communication between brain regions (network stability) by changing their predominant dietary fuel from glucose to ketones. We first established network stability as a biomarker for brain aging using two large-scale (n = 292, ages 20 to 85 y; n = 636, ages 18 to 88 y) 3 T functional MRI (fMRI) datasets. To determine whether diet can influence brain network stability, we additionally scanned 42 adults, age < 50 y, using ultrahigh-field (7 T) ultrafast (802 ms) fMRI optimized for single-participant-level detection sensitivity. One cohort was scanned under standard diet, overnight fasting, and ketogenic diet conditions. To isolate the impact of fuel type, an independent overnight fasted cohort was scanned before and after administration of a calorie-matched glucose and exogenous ketone ester (d-β-hydroxybutyrate) bolus. Across the life span, brain network destabilization correlated with decreased brain activity and cognitive acuity. Effects emerged at 47 y, with the most rapid degeneration occurring at 60 y. Networks were destabilized by glucose and stabilized by ketones, irrespective of whether ketosis was achieved with a ketogenic diet or exogenous ketone ester. Together, our results suggest that brain network destabilization may reflect early signs of hypometabolism, associated with dementia. Dietary interventions resulting in ketone utilization increase available energy and thus may show potential in protecting the aging brain.

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Diet modulates brain network stability, a biomarker for brain aging, in young adults

Electrophysiological methods, that is M/EEG, provide unique views into brain health. Yet, when building predictive models from brain data, it is often unclear how electrophysiology should be combined with other neuroimaging methods. Information can be redundant, useful common representations of multimodal data may not be obvious and multimodal data collection can be medically contraindicated, which reduces applicability. Here, we propose a multimodal model to robustly combine MEG, MRI and fMRI for prediction. We focus on age prediction as a surrogate biomarker in 674 subjects from the Cam-CAN dataset. Strikingly, MEG, fMRI and MRI showed additive effects supporting distinct brain-behavior associations. Moreover, the contribution of MEG was best explained by cortical power spectra between 8 and 30 Hz. Finally, we demonstrate that the model preserves benefits of stacking when some data is missing. The proposed framework, hence, enables multimodal learning for a wide range of biomarkers from diverse types of brain signals.

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Combining magnetoencephalography with magnetic resonance imaging enhances learning of surrogate-biomarkers.

The accurate diagnosis of Alzheimer’s disease (AD) plays an important role in patient treatment, especially at the disease’s early stages, because risk awareness allows the patients to undergo preventive measures even before the occurrence of irreversible brain damage. Although many recent studies have used computers to diagnose AD, most machine detection methods are limited by congenital observations. AD can be diagnosed-but not predicted-at its early stages, as prediction is only applicable before the disease manifests itself. Deep Learning (DL) has become a common technique for the early diagnosis of AD. Here, we briefly review some of the important literature on AD and explore how DL can help researchers diagnose the disease at its early stages.

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Alzheimer’s Diseases Detection by Using Deep Learning Algorithms: A Mini-Review

Within and between-person correlates of the temporal dynamics of resting EEG microstates

The dataset enables exploration of higher-order cognitive faculties, self-generated mental experience, and personality features in relation to the intrinsic functional architecture of the brain. We provide multimodal magnetic resonance imaging (MRI) data and a broad set of state and trait phenotypic assessments: mind-wandering, personality traits, and cognitive abilities. Specifically, 194 healthy participants (between 20 and 75 years of age) filled out 31 questionnaires, performed 7 tasks, and reported 4 probes of in-scanner mind-wandering. The scanning session included four 15.5-min resting-state functional MRI runs using a multiband EPI sequence and a hig h-resolution structural scan using a 3D MP2RAGE sequence. This dataset constitutes one part of the MPI-Leipzig Mind-Brain-Body database.

/pdf/a-functional-connectome-phenotyping-dataset-including-jiqkar2p76.pdf

A functional connectome phenotyping dataset including cognitive state and personality measures

We present a publicly available dataset of 227 healthy participants comprising a young (N=153, 25.1±3.1 years, range 20-35 years, 45 female) and an elderly group (N=74, 67.6±4.7 years, range 59-77 years, 37 female) acquired cross-sectionally in Leipzig, Germany, between 2013 and 2015 to study mind-body-emotion interactions. During a two-day assessment, participants completed MRI at 3 Tesla (resting-state fMRI, quantitative T1 (MP2RAGE), T2-weighted, FLAIR, SWI/QSM, DWI) and a 62-channel EEG experiment at rest. During task-free resting-state fMRI, cardiovascular measures (blood pressure, heart rate, pulse, respiration) were continuously acquired. Anthropometrics, blood samples, and urine drug tests were obtained. Psychiatric symptoms were identified with Standardized Clinical Interview for DSM IV (SCID-I), Hamilton Depression Scale, and Borderline Symptoms List. Psychological assessment comprised 6 cognitive tests as well as 21 questionnaires related to emotional behavior, personality traits and tendencies, eating behavior, and addictive behavior. We provide information on study design, methods, and details of the data. This dataset is part of the larger MPI Leipzig Mind-Brain-Body database.

/pdf/a-mind-brain-body-dataset-of-mri-eeg-cognition-emotion-and-2uw1266is6.pdf

A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults

Emotion regulation plays a central role in empathy. Only by successfully regulating our own emotions can we reliably use them in order to interpret the content and valence of others' emotions correctly. In an functional magnetic resonance imaging (fMRI)-based experiment, we show that regulating one's emotion via reappraisal modulated biased emotional intensity ratings following an empathy for pain manipulation. Task-based analysis revealed increased activity in the right inferior frontal gyrus (IFG) when painful emotions were regulated using reappraisal, whereas empathic feelings that were not regulated resulted in increased activity bilaterally in the precuneus, supramarginal gyrus and middle frontal gyrus (MFG), as well as the right parahippocampal gyrus. Functional connectivity analysis indicated that the right IFG plays a role in the regulation of empathy for pain, through its connections with regions in the empathy for pain network. Furthermore, these connections were further modulated as a function of the type of regulation used: in sum, our results suggest that accurate empathic judgment (i.e. empathy that is unbiased) relies on a complex interaction between neural regions involved in emotion regulation and regions associated with empathy for pain. Thus, demonstrating the importance of emotion regulation in the formulation of complex social systems and sheds light on the intricate network implicated in this complex process.

/pdf/the-neural-networks-underlying-reappraisal-of-empathy-for-50pwrnr53a.pdf

Christiane Rohr

Papers

A mind-brain-body dataset of MRI, EEG, cognition, emotion, and peripheral physiology in young and old adults

The neural networks underlying reappraisal of empathy for pain.

A novel method for detecting post-stroke functional tissue damage using resting-state fMRI