Non-invasive Technology Research Articles

Extracorporeal shockwave therapy (ESWT) in an outpatient wound care clinic: case series analysis of a non-invasive technology in the management of chronic wounds for wound bed preparation.

Extracorporeal shockwave therapy (ESWT) has been shown to reduce wound dimensions and healing time in chronic wounds and should be considered a valuable tool in the healing of chronic complex lower extremity wounds. The aim of this small case series was to evaluate the effect of ESWT on complex chronic wounds in patients with multiple comorbidities in a medically underserved outpatient wound care clinic setting. All patients had baseline wound measurements taken. Pictures of the wounds were also taken at the time of the initial visit. Patients selected for ESWT received weekly treatments for a maximum recorded duration of 12 weeks in the form of focused electro-hydraulic acoustic pulses. Wound beds were cleansed according to standard of care. Thirteen patients were followed with a total of 18 wounds treated. After retrospectively analyzing the data, 3 subjects and a total of 5 wounds were excluded, leaving 10 total subjects and 13 wounds. Out of these wounds, 12 healed completely by or before week 12 of ESWT. All wounds demonstrated significant wound dimension reduction during the first 12 weeks of treatment. ESWT could offer accessible, fast, safe, and cost-effective management of some complex chronic wounds. Further research is needed to validate these findings.

КОРРЕКЦИЯ КИСЛОРОДНОГО ГОЛОДАНИЯ В ЭКСПЕРИМЕНТАХ НА ЖИВОТНЫХ ПРИ МОДЕЛИРОВАНИИ ЭФФЕКТОВ, ВЫЗЫВАЕМЫХ ОСТРОЙ ДЫХАТЕЛЬНОЙ НЕДОСТАТОЧНОСТЬЮ, С ПОМОЩЬЮ МНОГОКОМПОНЕНТНОЙ ГАЗОВОЙ СМЕСИ НА ОСНОВЕ КОМБИНАЦИИ ИНЕРТНЫХ ГАЗОВ

Acute respiratory insufficiency (ARI) accompanied with hypoxemia and hypercapnia requires oxygen therapy. The helium-oxygen therapy secures from the undesirable effects of oxygen therapy. Earlier we showed that introduction of argon in a breathing mixture (BM) reduces the effect of hypoxia. Purpose of the work is to test the potentiality of BM containing a combination of inert gases to counteract oxygen starvation in acute ARI-provoking experiments with animals. Fourteen Wistar rats with a body mass of 276 ± 14.6 grams were divided into two groups. An integral noninvasive technology was used for real-time assessment of oxygen transport and consumption in each animal. Both the experiment and control animals breathed a mixture with a low O2 and high CO2 concentration. The control animals received a routine therapy by a hyperoxic mixture containing nitrogen and carbon dioxide. The experimental animals were treated with a hyperoxic mixture containing helium and argon, and carbon dioxide to induce hypercapnia. The results demonstrated reliably that the hyperoxic heluim-argon mixture reduces burden on the external respiration and favors oxygen utilization in tissues; however, this happens at the expense of a greater stress to the cardiovascular system. In contrast, the hyperoxic mixture used in the control stressed the external respiration rather than the cardiac muscle.

Feasibility and acceptability of an ultra-long-term at-home EEG monitoring system (EEG@HOME) for people with epilepsy

BackgroundRecent technological advancements offer new ways to monitor and manage epilepsy. The adoption of these devices in routine clinical practice will strongly depend on patient acceptability and usability, with their perspectives being crucial. Previous studies provided feedback from patients, but few explored the experience of them using independently multiple devices independently at home. PurposeThe study, assessed through a mixed methods design, the direct experiences of people with epilepsy independently using a non-invasive monitoring system (EEG@HOME) for an extended duration of 6 months, at home. We aimed to investigate factors affecting engagement, gather qualitative insights, and provide recommendations for future home epilepsy monitoring systems. Materials and methodsAdults with epilepsy independently were trained to use a wearable dry EEG system, a wrist-worn device, and a smartphone app for seizure tracking and behaviour monitoring for 6 months at home. Monthly acceptability questionnaires (PSSUQ, SUS) and semi-structured interviews were conducted to explore participant experience. Adherence with the procedure, acceptability scores and systematic thematic analysis of the interviews, focusing on the experience with the procedure, motivation and benefits and opinion about the procedure were assessed. ResultsTwelve people with epilepsy took part into the study for an average of 193.8 days (range 61 to 312) with a likelihood of using the system at six months of 83 %. The e-diary and the smartwatch were highly acceptable and preferred to a wearable EEG system (PSSUQ score of 1.9, 1.9, 2.4). Participants showed an acceptable level of adherence with all solutions (Average usage of 63 %, 66 %, 92 %) reporting more difficulties using the EEG twice a day and remembering to complete the daily behavioural questionnaires. Clear information and training, continuous remote support, perceived direct and indirect benefits and the possibility to have a flexible, tailored to daily routine monitoring were defined as key factors to ensure compliance with long-term monitoring systems. ConclusionsEEG@HOME study demonstrated people with epilepsy' interest and ability in active health monitoring using new technologies. Remote training and support enable independent home use of new non-invasive technologies, but to ensure long term acceptability and usability systems will require to be integrated into patients' routines, include healthcare providers, and offer continuous support and personalized feedback.

Protective effect of remote ischemic preconditioning on blood pressure control in magnetic resonance-guided focused ultrasound operations

Backrgound. Magnetic resonance-guided focused ultrasound (MR-FUS) is a new non-invasive technology for the surgical treatment of extrapyramidal movement disorders for such pathologies as essential tremor, Parkinson’s disease, etc. In these cases, the brain is injured and a zone of ischemia is formed. Arterial hypertension (AH) is one of the most common pathologies, which is often accompanied by neurological diseases. Objective. To investigate the effect of the remote ischemic preconditioning (RIPC) on blood pressure (BP) during MR-FUS surgery. Design – blinded, randomized, controlled, 2-group study with sham preconditioning. Materials and Methods. Patients were randomized into the RIPC group (cuff systolic BP (SBP) >50 mmHg, n=42) and the group with sham RIPC (cuff diastolic BP (DBP), n=39) before surgery in regime 3 cycles each 5 min with 5 min rest between cycles. Results. While there was a decrease in SBP and DBP before and after the operation in the RIPC group, there was an increase (p<0.001) in the sham RIPC group. Using the Difference-in-Difference equation for SBP, SBP was 8.9 (5.9–11.9) mm Hg higher in the sham RIPC group than in the RIPC group (p<0.01). Conclusion. As a result, MR-FUS operations showed a significant decrease in BP during RIPC compared to the imitation, which confirms the angioprotective effect of preconditioning.

Non-Contact Thermal and Acoustic Sensors with Embedded Artificial Intelligence for Point-of-Care Diagnostics.

This work involves exploring non-invasive sensor technologies for data collection and preprocessing, specifically focusing on novel thermal calibration methods and assessing low-cost infrared radiation sensors for facial temperature analysis. Additionally, it investigates innovative approaches to analyzing acoustic signals for quantifying coughing episodes. The research integrates diverse data capture technologies to analyze them collectively, considering their temporal evolution and physical attributes, aiming to extract statistically significant relationships among various variables for valuable insights. The study delineates two distinct aspects: cough detection employing a microphone and a neural network, and thermal sensors employing a calibration curve to refine their output values, reducing errors within a specified temperature range. Regarding control units, the initial implementation with an ESP32 transitioned to a Raspberry Pi model 3B+ due to neural network integration issues. A comprehensive testing is conducted for both fever and cough detection, ensuring robustness and accuracy in each scenario. The subsequent work involves practical experimentation and interoperability tests, validating the proof of concept for each system component. Furthermore, this work assesses the technical specifications of the prototype developed in the preceding tasks. Real-time testing is performed for each symptom to evaluate the system's effectiveness. This research contributes to the advancement of non-invasive sensor technologies, with implications for healthcare applications such as remote health monitoring and early disease detection.

Brain-Computer Interfaces Can Significantly Improve The Quality of Life for Aged People - A Review

As the global population ages, numerous programs have emerged to enhance the quality of life for seniors, addressing factors such as mental and physical well-being, mental illnesses, concurrent health issues, loneliness, and social interactions. Cognitive decline in aging poses a significant public health concern, impacting families and communities emotionally and financially. This review explores the effectiveness of healthcare surveillance combined with Brain-Computer Interfaces (BCIs) in improving memory and focus among a group of healthy seniors. Maintaining a high quality of life for the elderly is crucial, fostering resilience and perseverance. The review data suggests the potential of non-invasive BCIs, integrated with IoT-based Smart Home Technology (SHT), to enhance the Quality of Life (BCI-QOL) for seniors. The review focuses on advancing non-invasive BCI technology to support the health and well-being of older individuals, addressing dementia and physical impairments. It also discusses supportive BCI technologies for smart homes catering to seniors with motor control deficits. This comprehensive review aims to assess the immediate and long-term impact of regional features on the well-being and quality of life of seniors. The BCI-QOL-based system holds promise in enhancing cognition and focus, aiming for security, intuitiveness, and appropriateness for older users.

NIR-hyperspectral imaging and machine learning for non-invasive chemotype classification in Cannabis sativa L

The current public acceptance rate towards medical cannabis feasibility has led to a worldwide increase in this plant species production. Nevertheless, the currently transforming legal framework does not prevent the originally unlawful knowledge around cannabis breeding, which lacks quality control regulations or standards for correct manufacturing processes, a fact that could subsequently lead to uncontrolled and even harmful crop products. In this line, the objective of this work was to develop a non-invasive methodology for cannabis chemotype classification in different cultivars during the plant cultivation process, in order to keep undoubtful production control over cannabis crops. Hence, hyperspectral imaging (HSI), coupled with various multivariate data analysis approaches, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), enabled the non-invasive in-situ analysis of the plants. Hence, two PLS-DA classification models were trained with the plant spectral data for three chemotypes, based on the cannabinoid content of the plant inflorescences, with the difference between both approaches being the regard of the stem part of the plant as a bias. Thus, obtained sensitivity and specificity values in the inflorescences were 0.845/0.845 for Chemotype I, 0.954/0.920 for Chemotype II, and 0.888/0.925 for Chemotype III. At last, a hierarchical PLS-DA, which considered the stem as a bias, presented an overall 94.7 % trueness in the external validation of 57 different plant individuals, divided as 92.3 % trueness for chemotype I, 100.0 % trueness for chemotype II and 88.9 % trueness for chemotype III. Based on these results, the proof of concept for comprehensive agricultural control of cannabis crops through a non-invasive analytical technique was demonstrated, a previously unproven fact. Therefore, this work could further pave the way for non-invasive technology development for horticultural quality control in medical cannabis productions, as this emerging industry will require strict control over the cannabis chemotypes, with the strong advantage of avoiding destructive and time-consuming analytical techniques such as chromatography.

Understanding the anatomy of dermoscopy of melanocytic skin tumours: Correlation invivo with line-field optical coherence tomography.

Early melanoma detection is the main factor affecting prognosis and survival. For that reason, non-invasive technologies have been developed to provide a more accurate diagnosis. Recently, line-field confocal optical coherence tomography (LC-OCT) was developed to provide an invivo, imaging device, with deep penetration and cellular resolution in three dimensions. Combining the advantages of conventional OCT and reflectance confocal microscopy, this tool seems to be particularly suitable for melanocytic lesions. The objective of this study was to identify and describe the correlation between specific dermoscopic criteria and LC-OCT features in three dimensions associated with melanocytic lesions. Dermoscopic and LC-OCT images of 126 melanocytic lesions were acquired in three different centres. The following dermoscopic criteria have been considered: reticular pattern, dots and globules, structureless areas, blue-whitish veil, regression structures, negative network, homogeneous pattern, streaks and blotches. 69 (55%) benign and 57 (45%) malignant lesions were analysed. A regular reticular pattern was found associated in the 75% of the cases with the presence of elongated rete ridges with pigmented cells along the basal layer, while atypical reticular pattern showed an irregular organization of rete ridges with melanocytic hyperplasia, broadened and fused ridges and elongated nests. Both typical and atypical dots and globules were found associated with melanocytic nests in the dermis or at the dermoepidermal junction (DEJ), as well as with keratin cysts/pseudocysts. Grey globules corresponded to the presence of melanin-containing dermal inflammatory cells (melanophages) within the papillae. Structureless brown/black areas correlated with alterations of the DEJ. We observed the same DEJ alterations, but with the presence of dermal melanophages, in 36% of the cases of blue/white/grey structureless areas. A description of each LC-OCT/dermoscopy correlation was made. LC-OCT permitted for the first time to perform an invivo, 3D correlation between dermoscopic criteria and pathological-like features of melanocytic lesions.

Patterning of Protein-Sequestered Liquid-Crystal Droplets Using Acoustic Wave Trapping.

Development of spatially organized structures and understanding their role in controlling kinetics of multistep chemical reactions are essential for the successful design of efficient systems and devices. While studies that showcase different types of methodologies for the spatial organization of various colloidal systems are known, design and development of well-defined hierarchical assemblies of liquid-crystal (LC) droplets and subsequent demonstration of biological reactions using such assemblies still remain elusive. Here, we show reversible and reconfigurable one-dimensional (1D) assemblies of protein-bioconjugate-sequestered monodisperse LC droplets by combining microfluidics with noninvasive acoustic wave trapping technology. Tunable spatial geometries and lattice dimensions can be achieved in an aqueous medium comprising ≈19 or 62 μm LC droplets. Different assemblies of a mixed population of larger and smaller droplets sequestered with glucose oxidase (GOx) and horseradish peroxidase (HRP), respectively, exhibit spatially localized enzyme kinetics with higher initial rates of reaction compared with GOx/HRP cascades implemented in the absence of an acoustic field. This can be attributed to the direct substrate transfer/channeling between the two complementary enzymes in close proximity. Therefore, our study provides an initial step toward the fabrication of LC-based devices for biosensing applications.

EFFECTS OF IMPLEMENTING AGETECH TO SUPPORT DEMENTIA CARE IN A RURAL AND NORTHERN SETTING

Abstract Older adults wishing to age in northern and rural communities deserve equitable access to technologies that support optimal health, well-being, and quality of life. A new dementia care home opened in a rural and northern community in 2022 with consciously embedded multiple AgeTech solutions to enhance residents quality of life and care. The AgeTech solutions including hydroponic gardening, circadian lighting, and virtual exercise programming, were implementated into the 10-bed facility through a partnership between the Center for Technology Adoption for Aging in the North (CTAAN), health systems leaders, and community partners. The purposeful implementation of AgeTech within the facility through this partnership aimed to enhance the life-skills and living model of care to maximize independence, function, and resident well-being. We conducted a process evaluation assessing data from interviews with facility staff, health systems leaders, and secondary analysis of existing documentation to objectively measure the implementation process and inform a framework for ongoing assessment of AgeTech. Results highlight areas for ongoing technology innovation and inform policy decisions for future initiatives. Informants described the benefits in use of non-invasive circadian lighting technology to support resident well-being by enhancing sleep, rest/activity, reducing behaviours associated with dementia. Technology enhanced environmental well-being and expanded opportunities for residents to engage in meaningful activities. Participants also described the physical and social benefits for residents using individual and social multiplayer modes of exercise technologies. Providing equitable access to services across dispersed and sparse populations is challenging, yet with implementation of AgeTech quality of care can be enhanced.

Added value of 3D echocardiography in the diagnosis and prognostication of patients with right ventricular dysfunction.

Recent inroads into percutaneous-based options for the treatment of tricuspid valve disease has brought to light how little we know about the behavior of the right ventricle in both health and disease and how incomplete our assessment of right ventricular (RV) physiology and function is using current non-invasive technology, in particular echocardiography. The purpose of this review is to provide an overview of what three-dimensional echocardiography (3DE) can offer currently to enhance RV evaluation and what the future may hold if we continue to improve the 3D evaluation of the right heart.

DepressionGraph: A Two-Channel Graph Neural Network for the Diagnosis of Major Depressive Disorders Using rs-fMRI

Major depressive disorder (MDD) is a prevalent psychiatric condition with a complex and unknown pathological mechanism. Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a valuable non-invasive technology for MDD diagnosis. By utilizing rs-fMRI data, a dynamic brain functional connection network (FCN) can be constructed to represent the complex interacting relationships of multiple brain sub-regions. Graph neural network (GNN) models have been widely employed to extract disease-associated information. The simple averaging or summation graph readout functions of GNNs may lead to a loss of critical information. This study introduces a two-channel graph neural network (DepressionGraph) that effectively aggregates more comprehensive graph information from the two channels based on the node feature number and node number. Our proposed DepressionGraph model leverages the transformer–encoder architecture to extract the relevant information from the time-series FCN. The rs-fMRI data were obtained from a cohort of 533 subjects, and the experimental data show that DepressionGraph outperforms both traditional GNNs and simple graph readout functions for the MDD diagnosis task. The introduced DepressionGraph framework demonstrates efficacy in extracting complex patterns from rs-fMRI data and exhibits promising capabilities for the precise diagnosis of complex neurological disorders. The current study acknowledges a potential gender bias due to an imbalanced gender distribution in the dataset. Future research should prioritize the development and utilization of gender-balanced datasets to mitigate this limitation and enhance the generalizability of the findings.

Wireless capsule endoscopy multiclass classification using three-dimensional deep convolutional neural network model

BackgroundWireless capsule endoscopy (WCE) is a patient-friendly and non-invasive technology that scans the whole of the gastrointestinal tract, including difficult-to-access regions like the small bowel. Major drawback of this technology is that the visual inspection of a large number of video frames produced during each examination makes the physician diagnosis process tedious and prone to error. Several computer-aided diagnosis (CAD) systems, such as deep network models, have been developed for the automatic recognition of abnormalities in WCE frames. Nevertheless, most of these studies have only focused on spatial information within individual WCE frames, missing the crucial temporal data within consecutive frames.MethodsIn this article, an automatic multiclass classification system based on a three-dimensional deep convolutional neural network (3D-CNN) is proposed, which utilizes the spatiotemporal information to facilitate the WCE diagnosis process. The 3D-CNN model fed with a series of sequential WCE frames in contrast to the two-dimensional (2D) model, which exploits frames as independent ones. Moreover, the proposed 3D deep model is compared with some pre-trained networks. The proposed models are trained and evaluated with 29 subject WCE videos (14,691 frames before augmentation). The performance advantages of 3D-CNN over 2D-CNN and pre-trained networks are verified in terms of sensitivity, specificity, and accuracy.Results3D-CNN outperforms the 2D technique in all evaluation metrics (sensitivity: 98.92 vs. 98.05, specificity: 99.50 vs. 86.94, accuracy: 99.20 vs. 92.60). In conclusion, a novel 3D-CNN model for lesion detection in WCE frames is proposed in this study.ConclusionThe results indicate the performance of 3D-CNN over 2D-CNN and some well-known pre-trained classifier networks. The proposed 3D-CNN model uses the rich temporal information in adjacent frames as well as spatial data to develop an accurate and efficient model.

Advanced Electrode Technologies for Noninvasive Brain-Computer Interfaces.

Brain-computer interfaces (BCIs) have garnered significant attention in recent years due to their potential applications in medical, assistive, and communication technologies. Building on this, noninvasive BCIs stand out as they provide a safe and user-friendly method for interacting with the human brain. In this work, we provide a comprehensive overview of the latest developments and advancements in material, design, and application of noninvasive BCIs electrode technology. We also explore the challenges and limitations currently faced by noninvasive BCI electrode technology and sketch out the technological roadmap from three dimensions: Materials and Design; Performances; Mode and Function. We aim to unite research efforts within the field of noninvasive BCI electrode technology, focusing on the consolidation of shared goals and fostering integrated development strategies among a diverse array of multidisciplinary researchers.

Photoacoustic image reconstruction with anobjective function using TGV and ESTGV asaregularization functional.

Photoacoustic tomographic imaging is a non-invasive medical diagnostic technology for visualizing biological tissue. However, the inverse problem and noise in photoacoustic signals often cause blurred images. Existing regularization methods struggle with staircasing artifacts and edge preservation. To overcome this, an objective function incorporating total generalized variation (TGV) is proposed. However, it failed with high-density Gaussian noise. To address this, an extended version called edge-guided second-order TGV (ESTGV) is introduced. For sparsification, wavelet transform and discrete cosine transform are introduced, while the fast-composite-splitting algorithm is employed for the inverse problem solution. Experimental validation demonstrates the potential of these approaches.

Local post-hoc interpretable machine learning model for prediction of dementia in young adults

<p>Dementia is still the prevailing brain disease with late diagnosis. There is a large increase in dementia disease among young adults. The major reason is over indulgence of young adults on social media resulting in denial of disease and delayed clinical diagnosis. Dementia is preventable and curable if diagnosed at an early stage, however, no attempts are being made to miti gate dementia in young adults. Today artificial intelligence (AI) based advanced technology with real-life consultations in clinical or remote setups are proved beneficial and is used to detect dementia. Most AI-based test is dependent on computer-aided di agnosis (CAD) tools and uses non-invasive imaging technology such as magnetic resonance imaging (MRI) data for disease diagnosis. In this paper, a local post-hoc interpretable machine learning (LPIML) model for prediction of dementia in young adults is proposed. The performance parameters are computed and compared based on accuracy, specificity, precision, F1 score and recall. The proposed work yields 98.87% training accuracy on original images and 99.31% training accuracy on morphologically enhanced images. The performance results are intrinsic and intuitive in learning the prediction results of individual case. The adoption of the proposed work will accelerate the diagnosis process in the era of digital healthcare.</p>

Chinese expert consensus statement on the clinical application of AFP/AFP-L3%/DCP using GALAD and GALAD-like algorithm in HCC.

Primary hepatocellular carcinoma (HCC) is one of the most prevalent world-wide malignancies. Half of the newly developed HCC occurs in China. Optimizing the strategies for high-risk surveillance and early diagnosis are pivotal for improving 5-year survival. Constructing the scientific non-invasive detection technologies feasible for medical and healthcare institutions is among the key routes for elevating the efficacies of HCC identification and follow-up. Based on the Chinese and international guidelines, expert consensus statements, literatures and evidence-based clinical practice experiences, this consensus statement puts forward the clinical implications, application subjects, detection techniques and results interpretations of the triple-biomarker (AFP, AFP-L3%, DCP) based GALAD, GALAD like models for liver cancer. The compile of this consensus statement aims to address and push the reasonable application of the triple-biomarker (AFP, AFP-L3%, DCP) detections thus to maximize the clinical benefits and help improving the high risk surveillance, early diagnosis and prognosis of HCC.

Techno-Paranoia, Techno-Fear and Narcissism as Determinants of Consumer Trust in Wearables

ABSTRACT In the age of digital transformation and the transformation of traditional forms of communication between consumers and brands, wearables have successively become the next channel of contact after smartphones. On the basis of the literature review and an overview of the available devices of this type on the market, three categories of wearables have been identified: non-interfering (e.g. smart watches), indirect (protruding devices) applied to the body (e.g. smart tattoo, smart earrings) and interfering with the consumer’s body (e.g. subcutaneous chips, implants on organs inside the body or brain). The purpose of this paper was to assess the level of trust in wearables technology and its determinants, such as the respondent’s level of narcissism and fear of technology, using the constructs techno-paranoia and techno-fear among young respondents as examples. It turned out that out of the three tested predictors, only two of them turned out to be significant (techno-fear and techno-paranoia significantly (negatively) determine the level of trust). Techno-paranoia was significant for each of the three types of wearables technology, while techno-fear was only significant for non-invasive technologies.

Ethyl vinyl ketone activates oxidative and calcium burst and CML8-ACA8 participates in calcium recovery in Arabidopsis leaves

Plants produce ethyl vinyl ketone (evk) in response to biotic stress, but the evk's identification and downstream defense response remain unclear. In this paper, it is predicted by docking for the first time that evk can be recognized by RBOH protein and assist the electron transfer of RBOHD/RBOHF by binding to its FAD or NADPH binding site. Surface plasmon resonance (SPR) binding assay shows that evk indeed bind to RBOHD. Here, we show that evk treatment increased H2O2 and intracellular calcium concentrations in Arabidopsis thaliana mesophyll cells, as observed by confocal laser scanning microscopy and non-invasive micro-test technology, and that H2O2 signaling functioned upstream of Ca2+ signaling. Yeast two-hybrid, firefly luciferase complementation imaging, and in vitro pull-down assays demonstrated that the ACA8 (AUTOINHIBITED Ca2+-ATPASE, ISOFORM 8)–CML8 (CALMODULIN-LIKE 8) interaction promoted Ca2+ efflux to return Ca2+ levels to the resting state.

A tale of two targets: examining the differential effects of posterior cingulate cortex- and amygdala-targeted fMRI-neurofeedback in a PTSD pilot study

IntroductionReal-time fMRI-based neurofeedback (rt-fMRI-NFB) is a non-invasive technology that enables individuals to self-regulate brain activity linked to neuropsychiatric symptoms, including those associated with post-traumatic stress disorder (PTSD). Selecting the target brain region for neurofeedback-mediated regulation is primarily informed by the neurobiological characteristics of the participant population. There is a strong link between PTSD symptoms and multiple functional disruptions in the brain, including hyperactivity within both the amygdala and posterior cingulate cortex (PCC) during trauma-related processing. As such, previous rt-fMRI-NFB studies have focused on these two target regions when training individuals with PTSD to regulate neural activity. However, the differential effects of neurofeedback target selection on PTSD-related neural activity and clinical outcomes have not previously been investigated.MethodsHere, we compared whole-brain activation and changes in PTSD symptoms between PTSD participants (n = 28) that trained to downregulate activity within either the amygdala (n = 14) or the PCC (n = 14) while viewing personalized trauma words.ResultsFor the PCC as compared to the amygdala group, we observed decreased neural activity in several regions implicated in PTSD psychopathology – namely, the bilateral cuneus/precuneus/primary visual cortex, the left superior parietal lobule, the left occipital pole, and the right superior temporal gyrus/temporoparietal junction (TPJ) – during target region downregulation using rt-fMRI-NFB. Conversely, for the amygdala as compared to the PCC group, there were no unique (i.e., over and above that of the PCC group) decreases in neural activity. Importantly, amygdala downregulation was not associated with significantly improved PTSD symptoms, whereas PCC downregulation was associated with reduced reliving and distress symptoms over the course of this single training session. In this pilot analysis, we did not detect significant between-group differences in state PTSD symptoms during neurofeedback. As a critical control, the PCC and amygdala groups did not differ in their ability to downregulate activity within their respective target brain regions. This indicates that subsequent whole-brain neural activation results can be attributed to the effects of the neurofeedback target region selection in terms of neurophysiological function, rather than as a result of group differences in regulatory success.ConclusionIn this study, neurofeedback-mediated downregulation of the PCC was differentially associated with reduced state PTSD symptoms and simultaneous decreases in PTSD-associated brain activity during a single training session. This novel analysis may guide researchers in choosing a neurofeedback target region in future rt-fMRI-NFB studies and help to establish the clinical efficacy of specific neurofeedback targets for PTSD. A future multi-session clinical trial of rt-fMRI-NFB that directly compares between PCC and amygdala target regions is warranted.