Gaussian Process-based prediction of memory performance and biomarker status in ageing and Alzheimer's disease-A systematic model evaluation

Neuroimaging markers based on Magnetic Resonance Imaging (MRI) combined with various other measures (such as genetic covariates, biomarkers, vascular risk factors, neuropsychological tests etc.) might provide useful predictions of clinical outcomes during the progression towards Alzheimer's disease (AD). The use of multiple features in predictive frameworks for clinical outcomes has become increasingly prevalent in AD research. However, many studies do not focus on systematically and accurately evaluating combinations of multiple input features. Hence, the aim of the present work is to explore and assess optimal combinations of various features for MR-based prediction of (1) cognitive status and (2) biomarker positivity with a multi-kernel learning Gaussian process framework. The explored features and parameters included (A) combinations of brain tissues, modulation, smoothing, and image resolution; (B) incorporating demographics & clinical covariates; (C) the impact of the size of the training data set; (D) the influence of dimensionality reduction and the choice of kernel types. The approach was tested in a large German cohort including 959 subjects from the multicentric longitudinal study of cognitive impairment and dementia (DELCODE). Our evaluation suggests the best prediction of memory performance was obtained for a combination of neuroimaging markers, demographics, genetic information (ApoE4) and CSF biomarkers explaining 57% of outcome variance in out-of-sample predictions. The highest performance for Aβ42/40 status classification was achieved for a combination of demographics, ApoE4, and a memory score while usage of structural MRI further improved the classification of individual patient's pTau status.

Correction: Altered activation and connectivity in a hippocampal-basal ganglia-midbrain circuit during salience processing in subjects at ultra high risk for psychosis

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Decoding oscillatory representations and mechanisms in memory

A fundamental goal in memory research is to understand how information is represented in distributed brain networks and what mechanisms enable its reactivation. It is evident that progress towards this goal will greatly benefit from multivariate pattern classification (MVPC) techniques that can decode representations in brain activity with high temporal resolution. Recently, progress along these lines has been achieved by applying MVPC to neural oscillations recorded with electroencephalography (EEG) and magnetoencephalography (MEG). We highlight two examples of methodological approaches for MVPC of EEG and MEG data that can be used to study memory function. The first example aims at understanding the dynamic neural mechanisms that enable reactivation of memory representations, i.e., memory replay; we discuss how MVPC can help uncover the physiological mechanisms underlying memory replay during working memory maintenance and episodic memory. The second example aims at understanding representational differences between various types of memory, such as perceptual priming and conscious recognition memory. We also highlight the conceptual and methodological differences between these two examples. Finally, we discuss potential future applications for MVPC of EEG/MEG data in studies of memory. We conclude that despite its infancy and existing methodological challenges, MVPC of EEG and MEG data is a powerful tool with which to assess mechanistic models of memory.

Novelty increases the mesolimbic functional connectivity of the substantia nigra/ventral tegmental area (SN/VTA) during reward anticipation: Evidence from high-resolution fMRI

Reward and novelty are potent learning signals that critically rely on dopaminergic midbrain responses. Recent findings suggest that although reward and novelty are likely to interact, both functions may be subserved by distinct neuronal clusters. We used high-resolution functional magnetic resonance imaging (fMRI) to isolate neural responses to reward and novelty within the human substantia nigra/ventral tegmental area (SN/VTA) complex to investigate the spatial delineation and integration of reward- and novelty-related activity clusters. We demonstrate that distinct clusters within the caudal portion of the medial SN/VTA and the lateral portion of the right SN are predominantly modulated by the anticipation of reward, while a more rostral part of the medial SN/VTA was exclusively modulated by novelty. In addition, the caudal medial SN/VTA cluster embodied an interaction between novelty and reward where novelty selectively increased reward-anticipation responses. This interaction, in turn, was paralleled by differences in the functional-connectivity patterns of these SN/VTA regions. Specifically, novel as compared to familiar reward-predictive stimuli increased the functional connectivity of the medial SN/VTA with mesolimbic regions, including the nucleus accumbens and the hippocampus, as well as with the primary visual cortex. This functional correlation may highlight how afferents of the medial SN/VTA provide integrative information about novelty and reward, or, alternatively, how medial SN/VTA activity may modulate memory processes for novel events associated with rewards.

Testing for nested oscillation

Nested oscillation occurs when the amplitude of a faster rhythm is coupled to the phase of a slower rhythm. It has been proposed to underlie the discrete nature of perception and the capacity of working memory and is a phenomenon observable in human brain imaging data. This paper compares three published methods for detecting nested oscillation and a fourth method proposed in this paper. These are: (i) the modulation index, (ii) the phase-locking value (PLV), (iii) the envelope-to-signal correlation (ESC) and (iv) a general linear model (GLM) measure derived from ESC. We applied the methods to electrocorticographic (ECoG) data recorded during a working-memory task and to data from a simulated hippocampal interneuron network. Further simulations were then made to address the dependence of each measure on signal to noise level, coupling phase, epoch length, sample rate, signal nonstationarity, and multi-phasic coupling. Our overall conclusion is that the GLM measure is the best all-round approach for detecting nested oscillation.