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Commonly used for Parkinson’s disease (PD), deep brain stimulation (DBS) produces marked clinical benefits when optimized. However, assessing the large number of possible stimulation settings (i.e., programming) requires numerous clinic visits. Here, we examine whether functional magnetic resonance imaging (fMRI) can be used to predict optimal stimulation settings for individual patients. We analyze 3 T fMRI data prospectively acquired as part of an observational trial in 67 PD patients using optimal and non-optimal stimulation settings. Clinically optimal stimulation produces a characteristic fMRI brain response pattern marked by preferential engagement of the motor circuit. Then, we build a machine learning model predicting optimal vs. non-optimal settings using the fMRI patterns of 39 PD patients with a priori clinically optimized DBS (88% accuracy). The model predicts optimal stimulation settings in unseen datasets: a priori clinically optimized and stimulation-naïve PD patients. We propose that fMRI brain responses to DBS stimulation in PD patients could represent an objective biomarker of clinical response. Upon further validation with additional studies, these findings may open the door to functional imaging-assisted DBS programming. Deep brain stimulation programming for Parkinson’s disease entails the assessment of a large number of possible simulation settings, requiring numerous clinic visits after surgery. Here, the authors show that patterns of functional MRI can predict the optimal stimulation settings.

Machine-learning models for medical tasks can match or surpass the performance of clinical experts. However, in settings differing from those of the training dataset, the performance of a model can deteriorate substantially. Here we report a representation-learning strategy for machine-learning models applied to medical-imaging tasks that mitigates such ‘out of distribution’ performance problem and that improves model robustness and training efficiency. The strategy, which we named REMEDIS (for ‘Robust and Efficient Medical Imaging with Self-supervision’), combines large-scale supervised transfer learning on natural images and intermediate contrastive self-supervised learning on medical images and requires minimal task-specific customization. We show the utility of REMEDIS in a range of diagnostic-imaging tasks covering six imaging domains and 15 test datasets, and by simulating three realistic out-of-distribution scenarios. REMEDIS improved in-distribution diagnostic accuracies up to 11.5% with respect to strong supervised baseline models, and in out-of-distribution settings required only 1–33% of the data for retraining to match the performance of supervised models retrained using all available data. REMEDIS may accelerate the development lifecycle of machine-learning models for medical imaging. A representation-learning strategy for machine-learning models applied to medical-imaging tasks improves model robustness and training efficiency and mitigates suboptimal out-of-distribution performance.

Deep brain stimulation (DBS) to the fornix is an investigational treatment for patients with mild Alzheimer’s Disease. Outcomes from randomized clinical trials have shown that cognitive function improved in some patients but deteriorated in others. This could be explained by variance in electrode placement leading to differential engagement of neural circuits. To investigate this, we performed a post-hoc analysis on a multi-center cohort of 46 patients with DBS to the fornix (NCT00658125, NCT01608061). Using normative structural and functional connectivity data, we found that stimulation of the circuit of Papez and stria terminalis robustly associated with cognitive improvement ( R  = 0.53, p  < 0.001). On a local level, the optimal stimulation site resided at the direct interface between these structures ( R  = 0.48, p  < 0.001). Finally, modulating specific distributed brain networks related to memory accounted for optimal outcomes ( R  = 0.48, p  < 0.001). Findings were robust to multiple cross-validation designs and may define an optimal network target that could refine DBS surgery and programming. Deep brain stimulation has been investigated as a potential treatment for cognitive impairments in Alzheimer’s disease. Here the authors carry out post hoc analysis of multi-center cohorts to investigate the anatomical and functional correlates of effective deep brain stimulation, and find that stimulating circuit of Papez, fornix and bed nucleus of the stria terminalis, and a multi-region functional network, were associated with clinical improvement.

Deep brain stimulation (DBS) and non-invasive neuromodulation are currently being investigated for treating network dysfunction in Alzheimer’s Disease (AD). However, due to heterogeneity in techniques and targets, the cognitive outcome and brain network connectivity remain unknown. We performed a systematic review, meta-analysis, and normative functional connectivity to determine the cognitive outcome and brain networks of DBS and non-invasive neuromodulation in AD. PubMed, Embase, and Web of Science were searched using three concepts: dementia, brain connectome, and brain stimulation, with filters for English, human studies, and publication dates 1980–2021. Additional records from clinicaltrials.gov were added. Inclusion criteria were AD study with DBS or non-invasive neuromodulation and a cognitive outcome. Exclusion criteria were less than 3-months follow-up, severe dementia, and focused ultrasound intervention. Bias was assessed using Centre for Evidence-Based Medicine levels of evidence. We performed meta-analysis, with subgroup analysis based on type and age at neuromodulation. To determine the patterns of neuromodulation-induced brain network activation, we performed normative functional connectivity using rsfMRI of 1000 healthy subjects. Six studies, with 242 AD patients, met inclusion criteria. On fixed-effect meta-analysis, non-invasive neuromodulation favored baseline, with effect size −0.40(95% [CI], −0.73, −0.06, p  = 0.02), while that of DBS was 0.11(95% [CI] −0.34, 0.56, p  = 0.63), in favor of DBS. In patients ≥65 years old, DBS improved cognitive outcome, 0.95(95% [CI] 0.31, 1.58, p  = 0.004), whereas in patients <65 years old baseline was favored, −0.17(95% [CI] −0.93, 0.58, p  = 0.65). Functional connectivity regions were in the default mode (DMN), salience (SN), central executive (CEN) networks, and Papez circuit. The subgenual cingulate and anterior limb of internal capsule (ALIC) showed connectivity to all targets of neuromodulation. This meta-analysis provides level II evidence of a difference in response of AD patients to DBS, based on age at intervention. Brain stimulation in AD may modulate DMN, SN, CEN, and Papez circuit, with the subgenual cingulate and ALIC as potential targets.

Deep brain stimulation targeting the posterior hypothalamus (pHyp-DBS) is being investigated as a treatment for refractory aggressive behavior, but its mechanisms of action remain elusive. We conducted an integrated imaging analysis of a large multi-centre dataset, incorporating volume of activated tissue modeling, probabilistic mapping, normative connectomics, and atlas-derived transcriptomics. Ninety-one percent of the patients responded positively to treatment, with a more striking improvement recorded in the pediatric population. Probabilistic mapping revealed an optimized surgical target within the posterior-inferior-lateral region of the posterior hypothalamic area. Normative connectomic analyses identified fiber tracts and functionally connected with brain areas associated with sensorimotor function, emotional regulation, and monoamine production. Functional connectivity between the target, periaqueductal gray and key limbic areas – together with patient age – were highly predictive of treatment outcome. Transcriptomic analysis showed that genes involved in mechanisms of aggressive behavior, neuronal communication, plasticity and neuroinflammation might underlie this functional network.

In mesial temporal lobe epilepsy (mTLE), the correlation between disease duration, seizure laterality, and rostro-caudal location of hippocampal sclerosis has not been examined in the context of seizure severity and global cortical thinning. In this retrospective study, we analyzed structural 3 T MRI from 35 mTLE subjects. Regions of FLAIR hyperintensity (as an indicator of sclerosis)—based on 2D coronal FLAIR sequences—in the hippocampus were manually segmented, independently and in duplicate; degree of segmentation agreement was confirmed using the DICE index. Segmented lesions were used for separate analyses. First, the correlation of cortical thickness with disease duration and seizure focus laterality was explored using linear model regression. Then, the relationship between the rostro-caudal location of the FLAIR hyperintense signal and seizure severity, based on the Cleveland Clinic seizure freedom score (ccSFS), was explored using probabilistic voxel-wise mapping and functional connectivity analysis from normative data. The mean DICE Index was 0.71 (range 0.60–0.81). A significant correlation between duration of epilepsy and decreased mean whole brain cortical thickness was identified, regardless of seizure laterality ( p < 0.05). The slope of cortical volume loss over time, however, was greater in subjects with right seizure focus. Based on probabilistic voxel-wise mapping, FLAIR hyperintensity in the posterior hippocampus was significantly associated with lower ccSFS scores (greater seizure severity). Finally, the right hippocampus was found to have greater brain-wide connectivity, compared to the left side, based on normative connectomic data. We have demonstrated a significant correlation between duration of epilepsy and right-sided seizure focus with global cortical thinning, potentially due to greater brain-wide connectivity. Sclerosis along the posterior hippocampus was associated with greater seizure severity, potentially serving as an important biomarker of seizure outcome after surgery.

Background Gout treatment is not optimized globally, often due to therapeutic inertia by physicians or poor adherence to urate‐lowering medications by patients. A patient decision aid (PDA) to facilitate shared decision making (SDM) in gout treatment may overcome these physician‐patient barriers. Objective The study explored the views of physicians and patients on a novel locally designed gout treatment PDA prototype. Design Qualitative descriptive design was used to gather data from in‐depth‐interviews (IDI) and focus group discussions (FGD). Data analysis was via thematic analysis. Emergent themes shaped a revised version of the PDA. Setting and participants Adult Asian patients with recent acute gout exacerbations and local Primary Care Physicians (PCP) in Singapore were purposefully chosen. 15 patients with gout and 11 PCPs participated across three IDIs and six FGDs, with the investigators exploring their views of a prototype gout treatment PDA. Results Patients and physicians generally concurred with the content and design of the PDA prototype. However, while patients preferred fewer treatment details, the PCPs desired more information. Patients preferred the display of statistics, while PCPs felt that numbers were not relevant to patients. The latter were hesitant to include treatment options that were unavailable in primary care. Both stakeholders indicated that they would use the PDA during a consultation. PCPs would need further training in SDM, given a lack of understanding of it. Conclusion and patient contribution Patients will be the prime users of the PDA. While their views differed partially from the physicians, both have jointly developed the novel gout treatment PDA.

The giant barrel sponges (Xestospongia spp.) belong to a pan-global species complex with evidence suggesting they could encompass up to 9 cryptic species. In this study, we leveraged molecular and microbial techniques to investigate giant barrel sponges (X. testudinaria) from Singapore in relation to their placement within this species complex. Twenty-four giant barrel sponges from three sites were sequenced with mitochondrial (CO1) and nuclear (ATP6) DNA markers, identifying 6 distinct haplotypes belonging to 4 of the proposed barrel sponge species. Analysis of the X. testudinaria microbiomes was achieved with 16S rRNA gene amplicon sequencing. The microbiome composition of X. testudinaria did not differ by reef site, deviating from a pattern frequently observed in coral microbiomes across Singapore. However, there was significant differentiation in microbiome composition by host genetics consistent with the proposed species boundaries. General linear models identified 85 amplicon sequence variants (ASVs) as highly significant (P < 0.01) in differentiating among the four Species Groups, consisting of 12 Archaea and 73 Bacteria, with the largest representation from phylum Chloroflexi. We also identified 52 core ASVs present in all sponges representing 33.0% of the total sequence reads. Our results support previous findings of microbiome differentiation in co-occurring genetic haplotypes of barrel sponges from the Caribbean. Together these studies underline the potential for ecological partitioning based on genetic haplotype that could contribute to cryptic speciation within the giant barrel sponge species complex.

Corals host abundant microbial communities, or microbiomes, that play essential roles in the function of the coral holobiont. We examined the mucus microbiome in corals within the port of Singapore, where corals persist despite intense anthropogenic impacts. The coral mucus microbiomes of Pectinia paeonia and Platygyra sinensis at three reef sites were tracked by 16S rRNA gene amplicon sequencing from January 2019 to January 2020. Both coral species displayed spatial and temporal differences in microbiome composition, suggesting site specificity and seasonality in microbiome composition consistent with the monsoons. The temporal shifts in relative abundance of dominant taxa were different between the two coral species. Nonetheless, Proteobacteria was the most abundant phylum in both coral species and was reduced during the southwest (SW) monsoon, while Cyanobacteria and Crenarchaeota increased. The presumptive beneficial endosymbiont Endozoicomonas was only associated with corals at the reef site located the farthest from the Singapore mainland. The coral microbiomes reflected seasonal changes, while the seawater displayed distinct temporal microbial compositions and site-specific differentiation within all sampling dates. The persistence of coral reefs within the port of Singapore highlights the adaptive ability of corals to respond to stressful environments, and this study provides further evidence that a flexible microbiome could be an important part of the strategy employed by corals to remain resilient.