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AbstractDeposition of beta-amyloid (Aβ) deposits is one major histopathological hallmark of Alzheimer’s disease (AD). Here, we introduce volumetric multi-spectral optoacoustic tomography (vMSOT), which covers 10×10×10 mm3 field-of-view, capable of 3D whole mouse brain imaging. We show for the first time the optoacoustic properties of oxazine-derivative AOI987 probe, which binds to Aβ, and the application of vMSOT for the quantification of brain-wide Aβ deposition. Administration of AOI987 to two common transgenic mouse strains of AD amyloidosis led to a retention of the probe in Aβ-laden brain regions. Co-registered of vMSOT data to a brain atlas revealed strain-specific pattern of AOI987 uptake. A comparison with ex vivo light-sheet microscopy in cleared mouse brains showed a good correspondence in Aβ distribution. Lastly, we demonstrate the specificity of the AOI987 probe by immunohistochemistry. vMSOT with AOI987 facilitates preclinical brain region-specific studies of Aβ spread and accumulation, and the monitoring of putative treatments targeting Aβ.

AbstractLower family income during childhood is related to increased rates of adolescent depression, though the specific mechanisms are poorly understood. Evidence suggests that individuals with depression demonstrate hypoactivation in brain regions involved in reward learning and decision-making processes (e.g., portions of the prefrontal cortex). Separately, lower family income has been associated with neural alterations in similar regions. We examined associations between family income, depression, and brain activity during a reward learning and decision-making fMRI task in a sample of adolescents (full n=94; usable n=78; mean age=15.4 years). We identified neural regions representing 1) expected value (EV), the learned subjective value of an object, and 2) prediction error, the difference between EV and the actual outcome received. Regions of interest related to reward learning were examined in connection to childhood family income and parent-reported adolescent depressive symptoms. As hypothesized, lower activity in the subgenual anterior cingulate (sACC) for EV in response to approach stimuli was associated with lower childhood family income, as well as greater symptoms of depression measured one-year after the neuroimaging session. These results are consistent with the hypothesis that lower early family income leads to disruptions in reward and decision-making brain circuitry, which leads to adolescent depression.

AbstractCongenital amusia is a neurodevelopmental disorder characterized by difficulties in the perception and production of music, including the perception of consonance and dissonance, or the judgment of certain combinations of pitches as more pleasant than others. Two perceptual cues for dissonance are inharmonicity (the lack of a common fundamental frequency between components) and beating (amplitude fluctuations produced by close, interacting frequency components). In the presence of inharmonicities or beats, amusics have previously been reported to be insensitive to inharmonicity, but to exhibit normal sensitivity to beats. In the present study, we measured adaptive discrimination thresholds in amusic participants and found elevated thresholds for both cues. We recorded EEG and measured the mismatch negativity (MMN) in evoked potentials to consonance and dissonance deviants in an oddball paradigm. The amplitude of the MMN response was similar overall for amusics and controls, but while control participants showed a stronger MMN to harmonicity cues than to beating cues, amusic participants showed a stronger MMN to beating cues than to harmonicity cues. These findings suggest that initial encoding of consonance cues may be intact in amusia despite impaired behavioral performance, but that the relative weight of non-spectral cues may be increased for amusic individuals.

AbstractUnderstanding speech in noise (SiN) is a complex task that recruits multiple cortical subsystems. There is a variance in individuals’ ability to understand SiN that cannot be explained by simple hearing profiles, which suggests that central factors may underlie the variance in SiN ability. Here, we elucidated a few cortical functions involved during a SiN task and their contributions to individual variance using both within- and across-subject approaches. Through our within-subject analysis of source-localized electroencephalography, we investigated how acoustic signal-to-noise ratio (SNR) alters cortical evoked responses to a target word across the speech recognition areas, finding stronger responses in left supramarginal gyrus (SMG, BA40 the dorsal lexicon area) with quieter noise. Through an individual differences approach, we found that listeners show different neural sensitivity to the background noise and target speech, reflected in the amplitude ratio of earlier auditory-cortical responses to speech and noise, named as an internal SNR. Listeners with better internal SNR showed better SiN performance. Further, we found that the post-speech time SMG activity explains a further amount of variance in SiN performance that is not accounted for by internal SNR. This result demonstrates that at least two cortical processes contribute to SiN performance independently: pre-target time processing to attenuate neural representation of background noise and post-target time processing to extract information from speech sounds.

Abstract Fundamentals of Laparoscopic Surgery (FLS) is a prerequisite for board certification in general surgery in the USA. In FLS, the suturing task with intracorporeal knot tying is considered the most complex. Transcranial direct current stimulation (tDCS) of the dorsolateral prefrontal cortex (PFC) has been shown to facilitate FLS surgical skill acquisition where 2mA tDCS for 15min with the anode over F3 (10/10 EEG montage) and cathode over F4 has improved performance score in an open knot-tying task. Since PFC has a functional organization related to the hierarchy of cognitive control, we performed functional near-infrared spectroscopy (fNIRS) to investigate PFC activation during the more complex FLS suturing task with intracorporeal knot tying. We performed fNIRS-based analysis using AtlasViewer software on two expert surgeons and four novice medical students. We found an average cortical activation mainly at the left medial PFC across the experts, while the average cortical activation across the novices was primarily at the left pars opercularis of the inferior frontal gyrus and ventral premotor cortex, inferior parietal lobule, and supramarginal gyrus. Here, the average cortical activation across the novices included not only the cognitive control related brain regions but also motor control complexity related brain regions. Therefore, we investigated the feasibility to stimulate motor complexity related PFC regions using ROAST software and identified a 4x1 high-definition (HD) tDCS montage.Clinical Relevance—This shows the feasibility of fNIRS-guided tES to individualize electrode montage that may facilitate FLS surgical training in our future studies.

AbstractIndividuals differ in their ability to selectively attend to goal-relevant auditory stimuli. People with Attention-Deficit/Hyperactivity Disorder (ADHD) in particular tend to show cognitive deficits associated with distractibility and inefficiencies in inhibition and attention. We hypothesized that people with ADHD would exhibit poorer performance and weaker neural signatures of attentional control when undertaking a challenging auditory task that required strong top-down attention. Neurotypical (N = 20) and ADHD (N = 25) young adults with normal hearing listened to one of three concurrent, spatially separated speech streams and reported the order of the syllables presented while we recorded electroencephalography (EEG). We tested both the ability to sustain attentional focus on a single “target” stream and the ability to monitor the target but flexibly switch attention to an unpredictable “interrupter” stream from another direction if and when it appeared. Although both stimulus structure and task demands affected behavioral performance, ADHD status did not. In both groups, the interrupter evoked larger neural responses when it was to be attended compared to when it was irrelevant, including for the P3a “reorienting” response previously described as involuntary. This attentional modulation was weaker in ADHD listeners, even though their behavioral performance was no lower. Across the entire cohort, individual performance correlated with the degree of top-down modulation of neural responses. These results demonstrate that listeners differ in their ability to modulate neural representations of sound based on task goals. Adults with ADHD have weaker volitional control of attentional processes than their neurotypical counterparts.Significance StatementADHD and neurotypical listeners attended to one speech stream among distractors while neural responses were measured with electroencephalography. Behavioral performance varied with stimulus structure and task demands, but not with ADHD status. In both groups, top-down attention modulated stimulus-evoked neural responses: interrupting sounds elicited weaker responses when the sounds were ignored compared to when they were attended. This modulation affected a late “orienting” response (P3a) that has been previously described as automatic and not dependent on internal state. Importantly, ADHD subjects showed weaker attentional filtering than did neurotypical controls. At the individual level, performance correlated with neural metrics. Our results demonstrate that people vary widely in how flexibly they can use attention to modulate sensory responses based on task goals.

AbstractA diagnosis of schizophrenia is associated with a heterogeneous psychopathology including positive and negative symptoms. The disconnection hypothesis, an early pathophysiological framework conceptualizes the diversity of symptoms as a result from disconnections in neural networks. In line with this hypothesis, previous neuroimaging studies of patients with schizophrenia reported alterations within the default mode network (DMN), the most prominent network at rest.Aim of the present study was to investigate the functional connectivity during rest in patients with schizophrenia and healthy individuals and explore whether observed functional alterations are related to the psychopathology of patients. Therefore, functional magnetic resonance images at rest were recorded of 35 patients with schizophrenia and 41 healthy individuals. Independent component analysis (ICA) was used to extract resting state networks.Comparing ICA results between groups indicated alterations only within the network of the DMN. More explicitly, reduced connectivity in the precuneus was observed in patients with schizophrenia compared to healthy controls. Connectivity in this area was negatively correlated with the severity of negative symptoms, more specifically with the domain of apathy.Taken together, the current results provide further evidence for a role DMN alterations might play in schizophrenia and especially in negative symptom such as apathy.

Machine Learning (ML) approaches have been fruitfully applied to several classification problems of neurophysiological activity. Considering the relevance of emotion in human cognition and behaviour, ML found an important application field in emotion identification based on neurophysiological activity. Nonetheless, the literature results present a high variability depending on the neuronal activity measurement, the signal features and the classifier type. The present work aims to provide new methodological insight on ML applied to emotion identification based on electrophysiological brain activity. For this reason, we recorded EEG activity while emotional stimuli, high and low arousal (auditory and visual) were provided to a group of healthy participants. Our target signal to classify was the pre-stimulus onset brain activity. Classification performance of three different classifiers (LDA, SVM and kNN) was compared using both spectral and temporal features. Furthermore, we also contrasted the classifiers performance with static and dynamic (time evolving) features. The results show a clear increased in classification accuracy with temporal dynamic features. In particular, the SVM classifiers with temporal features showed the best accuracy (63.8 %) in classifying high vs. low arousal auditory stimuli.

Abstract Brain oscillations are known to modulate detection of visual stimuli, but it is unclear if this is due to increased guess rate or decreased precision of the mental representation. Here we estimated quality and guess rate as a function of electroencephalography (EEG) brain activity using an orientation perception task. Errors on each trial were quantified as the difference between the target orientation and the orientation reported by participants with a response stimulus. Response errors were fitted to standard mixed model by Zhang and Luck (2008) to quantify how participants’ guess rate and standard deviation parameters varied as a function of brain activity. Twenty-four participants were included in the analysis. Within subjects, the power and phase of delta and theta post-target oscillatory activity were found to vary along with performance on the orientation perception task in that greater power and phase coherence in the 2-5 Hz band range was measured in trials with more accurate responses. In addition, the phase of delta and theta correlated with the degree of response error while oscillatory power did not have a relationship with trial-by-trial response errors. Analysis of task-related alpha activity yielded no significant results implying that alpha oscillations do not play an important role in orientation perception at single trial level. Across participants, only the standard deviation parameter correlated with oscillatory power in the high alpha and low beta frequency ranges. These results indicate that post-target power is associated with the precision of mental representations rather than the guess rate, both across trials within subjects and across subjects.