CT Perfusion Map Synthesis from CTP Dynamic Images Using a Learned LSTM Generative Adversarial Network for Acute Ischemic Stroke Assessment.

Computed tomography perfusion (CTP) is a dynamic 4-dimensional imaging technique (3-dimensional volumes captured over approximately 1 min) in which cerebral blood flow is quantified by tracking the passage of a bolus of intravenous contrast with serial imaging of the brain. To diagnose and assess acute ischemic stroke, the standard method relies on summarizing acquired CTPs over the time axis to create maps that show different hemodynamic parameters, such as the timing of the bolus arrival and passage (Tmax and MTT), cerebral blood flow (CBF), and cerebral blood volume (CBV). However, producing accurate CTP maps requires the selection of an arterial input function (AIF), i.e. a time-concentration curve in one of the large feeding arteries of the brain, which is a highly error-prone procedure. Moreover, during approximately one minute of CT scanning, the brain is exposed to ionizing radiation that can alter tissue composition, and create free radicals that increase the risk of cancer. This paper proposes a novel end-to-end deep neural network that synthesizes CTP images to generate CTP maps using a learned LSTM Generative Adversarial Network (LSTM-GAN). Our proposed method can improve the precision and generalizability of CTP map extraction by eliminating the error-prone and expert-dependent AIF selection step. Further, our LSTM-GAN does not require the entire CTP time series and can produce CTP maps with a reduced number of time points. By reducing the scanning sequence from about 40 to 9 time points, the proposed method has the potential to minimize scanning time thereby reducing patient exposure to CT radiation. Our evaluations using the ISLES 2018 challenge dataset consisting of 63 patients showed that our model can generate CTP maps by using only 9 snapshots, without AIF selection, with an accuracy of 84.37 % .

Social Isolation, Brain Food Cue Processing, Eating Behaviors, and Mental Health Symptoms.

Importance: Perceived social isolation is associated with negative health outcomes, including increased risk for altered eating behaviors, obesity, and psychological symptoms. However, the underlying neural mechanisms of these pathways are unknown. Objective: To investigate the association of perceived social isolation with brain reactivity to food cues, altered eating behaviors, obesity, and mental health symptoms. Design, Setting, and Participants: This cross-sectional, single-center study recruited healthy, premenopausal female participants from the Los Angeles, California, community from September 7, 2021, through February 27, 2023. Exposure: Participants underwent functional magnetic resonance imaging while performing a food cue viewing task. Main Outcomes and Measures: The main outcomes included brain reactivity to food cues, body composition, self-reported eating behaviors (food cravings, reward-based eating, food addiction, and maladaptive eating behaviors), and mental health symptoms (anxiety, depression, positive and negative affect, and psychological resilience). Results: The study included 93 participants (mean [SD] age, 25.38 [7.07] years). Participants with higher perceived social isolation reported higher fat mass percentage, lower diet quality, increased maladaptive eating behaviors (cravings, reward-based eating, uncontrolled eating, and food addiction), and poor mental health (anxiety, depression, and psychological resilience). In whole-brain comparisons, the higher social isolation group showed altered brain reactivity to food cues in regions of the default mode, executive control, and visual attention networks. Isolation-related neural changes in response to sweet foods correlated with various altered eating behaviors and psychological symptoms. These altered brain responses mediated the connection between social isolation and maladaptive eating behaviors (ß for indirect effect, 0.111; 95% CI, 0.013-0.210; P = .03), increased body fat composition (ß, -0.141; 95% CI, -0.260 to -0.021; P = .02), and diminished positive affect (ß, -0.089; 95% CI, -0.188 to 0.011; P = .09). Conclusions and Relevance: These findings suggest that social isolation is associated with altered neural reactivity to food cues within specific brain regions responsible for processing internal appetite-related states and compromised executive control and attentional bias and motivation toward external food cues. These neural responses toward specific foods were associated with an increased risk for higher body fat composition, worsened maladaptive eating behaviors, and compromised mental health. These findings underscore the need for holistic mind-body-directed interventions that may mitigate the adverse health consequences of social isolation.

The association between glymphatic system dysfunction and alterations in cerebral function and structure in patients with white matter hyperintensities.

The objective of this study is to explore the relationship between the glymphatic system and alterations in the structure and function of the brain in white matter hyperintensity (WMH) patients. MRI data were collected from 27 WMH patients and 23 healthy controls. We calculated the along perivascular space (ALPS) indices, the anterior corner distance of the lateral ventricle, and the width of the third ventricle for each subject. The DPABISurf tool was used to calculate the cortical thickness and cortical area. In addition, data processing assistant for resting-state fMRI was used to calculate regional homogeneity, degree centrality, amplitude low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), and voxel-mirrored homotopic connectivity (VMHC). In addition, each WMH patient was evaluated on the Fazekas scale. Finally, the correlation analysis of structural indicators and functional indicators with bilateral ALPS indices was investigated using Spearman correlation analysis. The ALPS indices of WMH patients were lower than those of healthy controls (left: t = -4.949, P < 0.001; right: t = -3.840, P < 0.001). This study found that ALFF, fALFF, regional homogeneity, degree centrality, and VMHC values in some brain regions of WMH patients were alternated (AlphaSim corrected, P < 0.005, cluster size > 26 voxel, rmm value = 5), and the cortical thickness and cortical area of WMH patients showed trend changes (P < 0.01, cluster size > 20 mm2, uncorrected). Interestingly, we found significantly positive correlations between the left ALPS indices and degree centrality values in the superior temporal gyrus (r = 0.494, P = 0.009, P × 5 < 0.05, Bonferroni correction). Our results suggest that glymphatic system impairment is related to the functional centrality of local connections in patients with WMH. This provides a new perspective for understanding the pathological mechanisms of cognitive impairment in the WMH population.

The Value of Intraoperative Ultrasound in Brain Surgery.

Favorable clinical outcomes in adult and pediatric neurosurgical oncology generally depend on the extent of tumor resection (EOR). Maximum safe resection remains the main aim of surgery in most intracranial tumors. Despite the accuracy of intraoperative magnetic resonance imaging (iMRI) in the detection of residual intraoperatively, it is not widely implemented worldwide owing to enormous cost and technical difficulties. Over the past years, intraoperative ultrasound (IOUS) has imposed itself as a valuable and reliable intraoperative tool guiding neurosurgeons to achieve gross total resection (GTR) of intracranial tumors.Being less expensive, feasible, doesn't need a high level of training, doesn't need a special workspace, and being real time with outstanding temporal and spatial resolution; all the aforementioned advantages give a superiority for IOUS in comparison to iMRI during resection of brain tumors.In this chapter, we spot the light on the technical nuances, advanced techniques, outcomes of resection, pearls, and pitfalls of the use of IOUS during the resection of brain tumors.

Functional and structural reorganization in brain tumors: a machine learning approach using desynchronized functional oscillations.

Neuroimaging studies have allowed for non-invasive mapping of brain networks in brain tumors. Although tumor core and edema are easily identifiable using standard MRI acquisitions, imaging studies often neglect signals, structures, and functions within their presence. Therefore, both functional and diffusion signals, as well as their relationship with global patterns of connectivity reorganization, are poorly understood. Here, we explore the functional activity and the structure of white matter fibers considering the contribution of the whole tumor in a surgical context. First, we find intertwined alterations in the frequency domain of local and spatially distributed resting-state functional signals, potentially arising within the tumor. Second, we propose a fiber tracking pipeline capable of using anatomical information while still reconstructing bundles in tumoral and peritumoral tissue. Finally, using machine learning and healthy anatomical information, we predict structural rearrangement after surgery given the preoperative brain network. The generative model also disentangles complex patterns of connectivity reorganization for different types of tumors. Overall, we show the importance of carefully designing studies including MR signals within damaged brain tissues, as they exhibit and relate to non-trivial patterns of both structural and functional (dis-)connections or activity.

Pathophysiology of Migraine.

OBJECTIVE: This article provides an overview of the current understanding of migraine pathophysiology through insights gained from the extended symptom spectrum of migraine, neuroanatomy, migraine neurochemistry, and therapeutics. LATEST DEVELOPMENTS: Recent advances in human migraine research, including human experimental migraine models and functional neuroimaging, have provided novel insights into migraine attack initiation, neurochemistry, neuroanatomy, and therapeutic substrates. It has become clear that migraine is a neural disorder, in which a wide range of brain areas and neurochemical systems are implicated, producing a heterogeneous clinical phenotype. Many of these neural pathways are monoaminergic and peptidergic, such as those involving calcitonin gene-related peptide and pituitary adenylate cyclase-activating polypeptide. We are currently witnessing an exciting era in which specific drugs targeting these pathways have shown promise in treating migraine, including some studies suggesting efficacy before headache has even started. ESSENTIAL POINTS: Migraine is a brain disorder involving both headache and altered sensory, limbic, and homeostatic processing. A complex interplay between neurotransmitter systems, physiologic systems, and pain processing likely occurs. Targeting various therapeutic substrates within these networks provides an exciting avenue for future migraine therapeutics.

Whole brain pattern of iron accumulation in REM sleep behavior disorder.

Isolated REM sleep behavior disorder (iRBD) is an early stage of synucleinopathy with most patients progressing to Parkinson's disease (PD) or related conditions. Quantitative susceptibility mapping (QSM) in PD has identified pathological iron accumulation in the substantia nigra (SN) and variably also in basal ganglia and cortex. Analyzing whole-brain QSM across iRBD, PD, and healthy controls (HC) may help to ascertain the extent of neurodegeneration in prodromal synucleinopathy. 70 de novo PD patients, 70 iRBD patients, and 60 HCs underwent 3 T MRI. T1 and susceptibility-weighted images were acquired and processed to space standardized QSM. Voxel-based analyses of grey matter magnetic susceptibility differences comparing all groups were performed on the whole brain and upper brainstem levels with the statistical threshold set at family-wise error-corrected p-values <.05. Whole-brain analysis showed increased susceptibility in the bilateral fronto-parietal cortex of iRBD patients compared to both PD and HC. This was not associated with cortical thinning according to the cortical thickness analysis. Compared to iRBD, PD patients had increased susceptibility in the left amygdala and hippocampal region. Upper brainstem analysis revealed increased susceptibility within the bilateral SN for both PD and iRBD compared to HC; changes were located predominantly in nigrosome 1 in the former and nigrosome 2 in the latter group. In the iRBD group, abnormal dopamine transporter SPECT was associated with increased susceptibility in nigrosome 1. iRBD patients display greater fronto-parietal cortex involvement than incidental early-stage PD cohort indicating more widespread subclinical neuropathology. Dopaminergic degeneration in the substantia nigra is paralleled by susceptibility increase, mainly in nigrosome 1.

VSSI-GGD: A Variation Sparse EEG Source Imaging Approach Based on Generalized Gaussian Distribution.

Electroencephalographic (EEG) source imaging (ESI) is a powerful method for studying brain functions and surgical resection of epileptic foci. However, accurately estimating the location and extent of brain sources remains challenging due to noise and background interference in EEG signals. To reconstruct extended brain sources, we propose a new ESI method called Variation Sparse Source Imaging based on Generalized Gaussian Distribution (VSSI-GGD). VSSI-GGD uses the generalized Gaussian prior as a sparse constraint on the spatial variation domain and embeds it into the Bayesian framework for source estimation. Using a variational technique, we approximate the intractable true posterior with a Gaussian density. Through convex analysis, the Bayesian inference problem is transformed entirely into a series of regularized L2p -norm ( ) optimization problems, which are efficiently solved with the ADMM algorithm. Imaging results of numerical simulations and human experimental dataset analysis reveal the superior performance of VSSI-GGD, which provides higher spatial resolution with clear boundaries compared to benchmark algorithms. VSSI-GGD can potentially serve as an effective and robust spatiotemporal EEG source imaging method. The source code of VSSI-GGD is available at https://github.com/Mashirops/VSSI-GGD.git.

Regional MRI volumetry using NeuroQuant versus visual rating scales in patients with cognitive impairment and dementia.

BACKGROUND AND PURPOSE: The aims were to compare the novel regional brain volumetric measures derived by the automatic software NeuroQuant (NQ) with clinically used visual rating scales of medial temporal lobe atrophy (MTA), global cortical atrophy-frontal (GCA-f), and posterior atrophy (PA) brain regions, assessing their diagnostic validity, and to explore if combining automatic and visual methods would increase diagnostic prediction accuracy. METHODS: Brain magnetic resonance imaging (MRI) examinations from 86 patients with subjective and mild cognitive impairment (i.e., non-dementia, n = 41) and dementia (n = 45) from the Memory Clinic at Oslo University Hospital were assessed using NQ volumetry and with visual rating scales. Correlations, receiver operating characteristic analyses calculating area under the curves (AUCs) for diagnostic accuracy, and logistic regression analyses were performed. RESULTS: The correlations between NQ volumetrics and visual ratings of corresponding regions were generally high between NQ hippocampi/temporal volumes and MTA (r = -0.72/-0.65) and between NQ frontal volume and GCA-f (r = -0.62) but lower between NQ parietal/occipital volumes and PA (r = -0.49/-0.37). AUCs of each region, separating non-dementia from dementia, were generally comparable between the two methods, except that NQ hippocampi volume did substantially better than visual MTA (AUC = 0.80 vs. 0.69). Combining both MRI methods increased only the explained variance of the diagnostic prediction substantially regarding the posterior brain region. CONCLUSIONS: The findings of this study encourage the use of regional automatic volumetry in locations lacking neuroradiologists with experience in the rating of atrophy typical of neurodegenerative diseases, and in primary care settings.

The association between cannabis use and neuroimaging measures in older adults: findings from the UK biobank.

BACKGROUND: Cannabis use has increased in recent years. However, the long-term implications of cannabis use on brain health remain unknown. We explored the associations of cannabis use with volumetric brain magnetic resonance imaging (MRI) measures in dementia-free older adults. METHODS: This cross-sectional and longitudinal study included dementia-free participants of the UK Biobank aged ≥60 years. Linear regression models were used to evaluate the association of cannabis use and patterns of use with volumetric brain MRI measures. The association between cannabis use and change in brain MRI measures over time was also tested. All models were adjusted for potential confounders. RESULTS: The sample included 19,932 participants (mean age 68 ± 5 years, 48% men), 3,800 (19%) reported lifetime use of cannabis. Cannabis use was associated with smaller total, white, grey and peripheral cortical grey matter volumes (B = -6,690 ± 1,157; P < 0.001, B = -4,396 ± 766; P < 0.001, B = -2,140 ± 690; P = 0.002 and B = -2,451 ± 606; P < 0.001, respectively). Among cannabis users, longer duration of use was associated with smaller total brain, grey and cortical grey matter volumes (B = -7,878 ± 2,396; P = 0.001, B = -5,411 ± 1,430; P < 0.001, B = -5,396 ± 1,254; P < 0.001, respectively), and with increased white matter hyperintensity volume (B = 0.09 ± 0.03; P = 0.008). Additionally, current vs. former users (B = -10,432 ± 4,395; P = 0.020) and frequent versus non-frequent users (B = -2,274 ± 1,125; P = 0.043) had smaller grey and cortical grey matter volumes, respectively. No significant associations were observed between cannabis use and change in brain MRI measures. DISCUSSION: Our findings suggest that cannabis use, particularly longer duration and frequent use, may be related to smaller grey and white matter volumes in older ages, but not to late-life changes in these measures over time.

Clavulanic Acid Decreases Cocaine Cue Reactivity in Addiction-Related Brain Areas, a Randomized fMRI Pilot Study.

Background: Preclinical studies show that clavulanic acid (CLAV) inhibits cocaine self-administration. This study investigates the effect of CLAV on regions of brain activation in response to cocaine cues during functional magnetic resonance imaging (fMRI) in participants with cocaine use disorder (CUD). Methods: A double-masked, placebo-controlled clinical trial with thirteen individuals with severe CUD who were randomized to treatment with CLAV (N = 10, 9 completers) 500 mg/day or matched placebo (PBO) (N = 3) for 3 days. fMRI was used to assess brain reactivity to 18 alternating six-second video clips of cocaine or neutral scenes. In this paradigm, participants were exposed to three different stimulus conditions: NEUTRAL, WATCH (passive watching), and DOWN (actively inhibiting craving while watching). Results: Participants who received CLAV demonstrated a significant reduction in brain activity in the anterior cingulate gyrus (p = 0.009) and the caudate (p = 0.018) in response to DOWN cocaine cues. There was a trend toward lessened cue reactivity in other regions implicated in CUD. Conclusion: CLAV reduced the response of the brain regions associated with motivation and emotional response during the DOWN condition compared to PBO, suggesting CLAV may strengthen voluntary efforts to avoid cocaine use. This pilot data supports the use of CLAV for CUD. (Trial registered in ClinicalTrials.gov NCT04411914).

Cerebral rScO2 Measured by Near-Infrared Spectroscopy (NIRS) During Therapeutic Hypothermia in Neonates with Hypoxic-Ischemic Encephalopathy: A Systematic Review.

INTRODUCTION: Perinatal asphyxia, a leading cause of neonatal mortality and neurological sequelae, necessitates early detection of pathophysiological neurologic changes during hypoxic-ischaemic encephalopathy (HIE). This study aimed to review published data on rScO2 monitoring during hypothermia treatment in neonates with perinatal asphyxia to predict short- and long-term neurological injury. METHODS: A systematic review was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Study identification was performed through a search between November and December 2021 in the electronic databases PubMed, Embase, Lilacs, Scopus, Web of Science, and Cochrane Central Register of Controlled Trials (CENTRAL). The main outcome was short-term (Changes in brain magnetic resonating imaging) and long-term (In neurodevelopment) neurological injury. The study protocol was registered in PROSPERO (International Prospective Register of Systematic Reviews) with CRD42023395438. RESULTS: 380 articles were collected from databases in the initial search. Finally, 15 articles were selected for extraction and analysis of the information. An increase in rScO2 measured by NIRS (Near-infrared spectroscopy) at different moments of treatment predicts neurological injury. However, there exists a wide variability in the methods and outcomes of the studies. CONCLUSION: High rScO2 values were found to predict negative outcomes, with substantial discord among studies. NIRS is proposed as a real-time bedside tool for predicting brain injury in neonates with moderate to severe HIE.

Distinct brain network organizations between club players and novices under different difficulty levels.

SIGNIFICANT: Chunk memory is one of the essential cognitive functions for high-expertise (HE) player to make efficient decisions. However, it remains unknown how the neural mechanisms of chunk memory processes mediate or alter chess players' performance when facing different opponents. AIM: This study aimed at inspecting the significant brain networks associated with chunk memory, which would vary between club players and novices. APPROACH: Functional networks and topological features of 20 club players (HE) and 20 novice players (LE) were compared at different levels of difficulty by means of functional near-infrared spectroscopy. RESULTS: Behavioral performance indicated that the club player group was unaffected by differences in difficulty. Furthermore, the club player group demonstrated functional connectivity among the dorsolateral prefrontal cortex, the frontopolar cortex, the supramarginal gyrus, and the subcentral gyrus, as well as higher clustering coefficients and lower path lengths in the high-difficulty task. CONCLUSIONS: The club player group illustrated significant frontal-parietal functional connectivity patterns and topological characteristics, suggesting enhanced chunking processes for improved chess performance.

Whole brain morphologic features improve the predictive accuracy of IDH status and VEGF expression levels in gliomas.

Glioma is a systemic disease that can induce micro and macro alternations of whole brain. Isocitrate dehydrogenase and vascular endothelial growth factor are proven prognostic markers and antiangiogenic therapy targets in glioma. The aim of this study was to determine the ability of whole brain morphologic features and radiomics to predict isocitrate dehydrogenase status and vascular endothelial growth factor expression levels. This study recruited 80 glioma patients with isocitrate dehydrogenase wildtype and high vascular endothelial growth factor expression levels, and 102 patients with isocitrate dehydrogenase mutation and low vascular endothelial growth factor expression levels. Virtual brain grafting, combined with Freesurfer, was used to compute morphologic features including cortical thickness, LGI, and subcortical volume in glioma patient. Radiomics features were extracted from multiregional tumor. Pycaret was used to construct the machine learning pipeline. Among the radiomics models, the whole tumor model achieved the best performance (accuracy 0.80, Area Under the Curve 0.86), while, after incorporating whole brain morphologic features, the model had a superior predictive performance (accuracy 0.82, Area Under the Curve 0.88). The features contributed most in predicting model including the right caudate volume, left middle temporal cortical thickness, first-order statistics, shape, and gray-level cooccurrence matrix. Pycaret, based on morphologic features, combined with radiomics, yielded highest accuracy in predicting isocitrate dehydrogenase mutation and vascular endothelial growth factor levels, indicating that morphologic abnormalities induced by glioma were associated with tumor biology.

A large normative connectome for exploring the tractographic correlates of focal brain interventions.

Diffusion-weighted MRI (dMRI) is a widely used neuroimaging modality that permits the in vivo exploration of white matter connections in the human brain. Normative structural connectomics - the application of large-scale, group-derived dMRI datasets to out-of-sample cohorts - have increasingly been leveraged to study the network correlates of focal brain interventions, insults, and other regions-of-interest (ROIs). Here, we provide a normative, whole-brain connectome in MNI space that enables researchers to interrogate fiber streamlines that are likely perturbed by given ROIs, even in the absence of subject-specific dMRI data. Assembled from multi-shell dMRI data of 985 healthy Human Connectome Project subjects using generalized Q-sampling imaging and multispectral normalization techniques, this connectome comprises ~12 million unique streamlines, the largest to date. It has already been utilized in at least 18 peer-reviewed publications, most frequently in the context of neuromodulatory interventions like deep brain stimulation and focused ultrasound. Now publicly available, this connectome will constitute a useful tool for understanding the wider impact of focal brain perturbations on white matter architecture going forward.

Computational limits to the legibility of the imaged human brain.

Our knowledge of the organisation of the human brain at the population-level is yet to translate into power to predict functional differences at the individual-level, limiting clinical applications and casting doubt on the generalisability of inferred mechanisms. It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal. Here we comprehensively investigate the resolvability of such patterns with data and compute at unprecedented scale. Across 23 810 unique participants from UK Biobank, we systematically evaluate the predictability of 25 individual biological characteristics, from all available combinations of structural and functional neuroimaging data. Over 4526 GPU*hours of computation, we train, optimize, and evaluate out-of-sample 700 individual predictive models, including fully-connected feed-forward neural networks of demographic, psychological, serological, chronic disease, and functional connectivity characteristics, and both uni- and multi-modal 3D convolutional neural network models of macro- and micro-structural brain imaging. We find a marked discrepancy between the high predictability of sex (balanced accuracy 99.7%), age (mean absolute error 2.048 years, R2 0.859), and weight (mean absolute error 2.609Kg, R2 0.625), for which we set new state-of-the-art performance, and the surprisingly low predictability of other characteristics. Neither structural nor functional imaging predicted an individual's psychology better than the coincidence of common chronic disease (p < 0.05). Serology predicted chronic disease (p < 0.05) and was best predicted by it (p < 0.001), followed by structural neuroimaging (p < 0.05). Our findings suggest either more informative imaging or more powerful models will be needed to decipher individual level characteristics from the human brain. We make our models and code openly available.

MR Imaging-based Biomarker Development in Hemorrhagic Stroke Patients Including Brain Iron Quantification, Diffusion Tensor Imaging, and Phenomenon of Ultra-early Erythrolysis.

This review article discusses the role of MR imaging-based biomarkers in understanding and managing hemorrhagic strokes, focusing on intracerebral hemorrhage (ICH) and aneurysmal subarachnoid hemorrhage. ICH is a severe type of stroke with high mortality and morbidity rates, primarily caused by the rupture of small blood vessels in the brain, resulting in hematoma formation. MR imaging-based biomarkers, including brain iron quantification, ultra-early erythrolysis detection, and diffusion tensor imaging, offer valuable insights for hemorrhagic stroke management. These biomarkers could improve early diagnosis, risk stratification, treatment monitoring, and patient outcomes in the future, revolutionizing our approach to hemorrhagic strokes.

Miniaturized Implantable Fluorescence Probes Integrated with Metal-Organic Frameworks for Deep Brain Dopamine Sensing.

Continuously monitoring neurotransmitter dynamics can offer profound insights into neural mechanisms and the etiology of neurological diseases. Here, we present a miniaturized implantable fluorescence probe integrated with metal-organic frameworks (MOFs) for deep brain dopamine sensing. The probe is assembled from physically thinned light-emitting diodes (LEDs) and phototransistors, along with functional surface coatings, resulting in a total thickness of 120 µm. A fluorescent MOF that specifically binds dopamine is introduced, enabling a highly sensitive dopamine measurement with a detection limit of 79.9 nM. A compact wireless circuit weighing only 0.85 g is also developed and interfaced with the probe, which was later applied to continuously monitor real-time dopamine levels during deep brain stimulation in rats, providing critical information on neurotransmitter dynamics. Cytotoxicity tests and immunofluorescence analysis further suggest a favorable biocompatibility of the probe for implantable applications. This work presents fundamental principles and techniques for integrating fluorescent MOFs and flexible electronics for brain-computer interfaces and may provide more customized platforms for applications in neuroscience, disease tracing, and smart diagnostics.