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Abstract Delirium is risky and indicates poor outcomes for patients. Therefore, it is crucial to create an effective delirium detection method. However, the epigenetic pathophysiology of delirium remains largely unknown. We aimed to discover reliable and replicable epigenetic (DNA methylation: DNAm) markers that are associated with delirium including post-operative delirium (POD) in blood obtained from patients among four independent cohorts. Blood DNA from four independent cohorts (two inpatient cohorts and two surgery cohorts; 16 to 88 patients each) were analyzed using the Illumina EPIC array platform for genome-wide DNAm analysis. We examined DNAm differences in blood between patients with and without delirium including POD. When we compared top CpG sites previously identified from the initial inpatient cohort with three additional cohorts (one inpatient and two surgery cohorts), 11 of the top 13 CpG sites showed statistically significant differences in DNAm values between the delirium group and non-delirium group in the same directions as found in the initial cohort. This study demonstrated the potential value of epigenetic biomarkers as future diagnostic tools. Furthermore, our findings provide additional evidence of the potential role of epigenetics in the pathophysiology of delirium including POD.

Background: Transcranial magnetic stimulation (TMS) is believed to alter ongoing neural activity and cause circuit-level changes in brain function. While the electrophysiological effects of TMS have been extensively studied with scalp electroencephalography (EEG), this approach generally evaluates low-frequency neural activity at the cortical surface. However, TMS can be safely used in patients with intracranial electrodes (iEEG), allowing for direct assessment of deeper and more localized oscillatory responses across the frequency spectrum. Objective/Hypothesis: Our study used iEEG to understand the effects of TMS on human neural activity in the spectral domain. We asked (1) which brain regions respond to cortically-targeted TMS, and in what frequency bands, (2) whether deeper brain structures exhibit oscillatory responses, and (3) whether the neural responses to TMS reflect evoked versus induced oscillations. Methods: We recruited 17 neurosurgical patients with indwelling electrodes and recorded neural activity while patients underwent repeated trials of single-pulse TMS at either the dorsolateral prefrontal cortex (DLPFC) or parietal cortex. iEEG signals were analyzed using spectral methods to understand the oscillatory responses to TMS. Results: Stimulation to DLPFC drove widespread low-frequency increases (3–8 Hz) in frontolimbic cortices and high-frequency decreases (30–110 Hz) in frontotemporal areas, including the hippocampus. Stimulation to parietal cortex specifically provoked low-frequency responses in the medial temporal lobe. While most low-frequency activity was consistent with phase-locked evoked responses, anterior frontal regions exhibited induced theta oscillations following DLPFC stimulation. Conclusions: By combining TMS with intracranial EEG recordings, our results suggest that TMS is an effective means to perturb oscillatory neural activity in brain-wide networks, including deeper structures not directly accessed by stimulation itself.

BackgroundMind–body treatments can improve coping mechanisms to deal with pain, improve the quality of life of patients with fibromyalgia syndrome (FMS), and reduce perceived pain in some cases. However, responses to these treatments are highly variable, the mechanisms underpinning them remain unclear, and reliable predictors of treatment response are lacking. We employed resting-state blood oxygen level-dependent (rsBOLD) functional magnetic resonance imaging (fMRI) to examine changes in brain functional connectivity (FC) following mind–body treatment that may relate to and predict pain relief.MethodsWe recruited patients with FMS who underwent either mindfulness-based stress reduction (MBSR; n = 18) or a psychoeducational program (FibroQoL; n = 22) and a treatment-as-usual FMS group (TAU; n = 18). We collected rsBOLD data, alongside subjective pain, anxiety, depression, and catastrophizing measures prior to and following treatments. We examined behavioral changes and FC changes in the salience network (SN) and sensorimotor network (SMN) and performed regression analyses to identify predictors for treatment response.ResultsThe MBSR and FibroQoL groups experienced significant reductions in pain catastrophizing. After treatment, the FC of the sensorimotor cortex with the rest of the SMN became significantly reduced in the MBSR group compared to the TAU group. The FC between the SN and the SMN at baseline was negatively correlated with pain reductions following MBSR but positively correlated with pain reductions in the FibroQoL group. These results yielded large to very large effect sizes. Following MBSR, only for those patients with lower baseline SMN-SN FC, minutes of mindfulness practice were positively associated with clinical improvement (small to medium effect size).ConclusionsDifferent mind–body treatments are underpinned by discrete brain networks. Measures of the functional interplay between SN and SMN have the potential as predictors of mind–body treatment response in patients with FMS.

Abstract The value and uncertainty associated with choice alternatives constitute critical features relevant for decisions. However, the manner in which reward and risk representations are temporally organized in the brain remains elusive. Here we leverage the spatiotemporal precision of intracranial electroencephalography, along with a simple card game designed to elicit the unfolding computation of a set of reward and risk variables, to uncover this temporal organization. Reward outcome representations across wide-spread regions follow a sequential order along the anteroposterior axis of the brain. In contrast, expected value can be decoded from multiple regions at the same time, and error signals in both reward and risk domains reflect a mixture of sequential and parallel encoding. We further highlight the role of the anterior insula in generalizing between reward prediction error and risk prediction error codes. Together our results emphasize the importance of neural dynamics for understanding value-based decisions under uncertainty.

Telomeres are important to chromosomal stability, and changes in their length correlate with disease, potentially relevant to brain disorders. Assessing telomere length in human brain is invasive, but whether peripheral tissue telomere length correlates with that in brain is not known. Saliva, buccal, blood, and brain samples were collected at time points before, during, and after subjects undergoing neurosurgery (n = 35) for intractable epilepsy. DNA was isolated from samples and average telomere length assessed by qPCR. Correlations of telomere length between tissue samples were calculated across subjects. When data were stratified by sex, saliva telomere length correlated with brain telomere length in males only. Buccal telomere length correlated with brain telomere length when males and females were combined. These findings indicate that in living subjects, telomere length in peripheral tissues variably correlates with that in brain and may be dependent on sex. Peripheral tissue telomere length may provide insight into brain telomere length, relevant to assessment of brain disorder pathophysiology.

IntroductionCochlear implants (CIs) are the treatment of choice for severe to profound hearing loss. Variability in CI outcomes remains despite advances in technology and is attributed in part to differences in cortical processing. Studying these differences in CI users is technically challenging. Spectrally degraded stimuli presented to normal-hearing individuals approximate input to the central auditory system in CI users. This study used intracranial electroencephalography (iEEG) to investigate cortical processing of spectrally degraded speech.MethodsParticipants were adult neurosurgical epilepsy patients. Stimuli were utterances /aba/ and /ada/, spectrally degraded using a noise vocoder (1–4 bands) or presented without vocoding. The stimuli were presented in a two-alternative forced choice task. Cortical activity was recorded using depth and subdural iEEG electrodes. Electrode coverage included auditory core in posteromedial Heschl’s gyrus (HGPM), superior temporal gyrus (STG), ventral and dorsal auditory-related areas, and prefrontal and sensorimotor cortex. Analysis focused on high gamma (70–150 Hz) power augmentation and alpha (8–14 Hz) suppression.ResultsChance task performance occurred with 1–2 spectral bands and was near-ceiling for clear stimuli. Performance was variable with 3–4 bands, permitting identification of good and poor performers. There was no relationship between task performance and participants demographic, audiometric, neuropsychological, or clinical profiles. Several response patterns were identified based on magnitude and differences between stimulus conditions. HGPM responded strongly to all stimuli. A preference for clear speech emerged within non-core auditory cortex. Good performers typically had strong responses to all stimuli along the dorsal stream, including posterior STG, supramarginal, and precentral gyrus; a minority of sites in STG and supramarginal gyrus had a preference for vocoded stimuli. In poor performers, responses were typically restricted to clear speech. Alpha suppression was more pronounced in good performers. In contrast, poor performers exhibited a greater involvement of posterior middle temporal gyrus when listening to clear speech.DiscussionResponses to noise-vocoded speech provide insights into potential factors underlying CI outcome variability. The results emphasize differences in the balance of neural processing along the dorsal and ventral stream between good and poor performers, identify specific cortical regions that may have diagnostic and prognostic utility, and suggest potential targets for neuromodulation-based CI rehabilitation strategies.

Abstract Deep mutational scanning (DMS) measures the effects of thousands of genetic variants in a protein simultaneously. The small sample size renders classical statistical methods ineffective. For example, p-values cannot be correctly calibrated when treating variants independently. We propose Rosace, a Bayesian framework for analyzing growth-based DMS data. Rosace leverages amino acid position information to increase power and control the false discovery rate by sharing information across parameters via shrinkage. We also developed Rosette for simulating the distributional properties of DMS. We show that Rosace is robust to the violation of model assumptions and is more powerful than existing tools.

Abstract Parkinson’s disease (PD) is closely linked to α-synuclein (α-syn) misfolding and accumulation in Lewy bodies. The PDZ serine protease HTRA1 degrades fibrillar tau, which is associated with Alzheimer’s disease, and inactivating mutations to mitochondrial HTRA2 are implicated in PD. Here, we report that HTRA1 inhibits aggregation of α-syn as well as FUS and TDP-43, which are implicated in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia. The protease domain of HTRA1 is necessary and sufficient for inhibiting aggregation, yet this activity is proteolytically-independent. Further, HTRA1 disaggregates preformed α-syn fibrils, rendering them incapable of seeding aggregation of endogenous α-syn, while reducing HTRA1 expression promotes α-syn seeding. HTRA1 remodels α-syn fibrils by targeting the NAC domain, the key domain catalyzing α-syn amyloidogenesis. Finally, HTRA1 detoxifies α-syn fibrils and prevents formation of hyperphosphorylated α-syn accumulations in primary neurons. Our findings suggest that HTRA1 may be a therapeutic target for a range of neurodegenerative disorders.