Functional Brain Imaging Research Articles

Impact of an online guided physical activity training on cognition and gut-brain axis interactions in older adults: protocol of a randomized controlled trial.

By 2050, the worldwide percentage of people 65 years and older is assumed to have doubled compared to current numbers. Therefore, finding ways of promoting healthy (cognitive) aging is crucial. Physical activity is considered an effective approach to counteract not only physical but also cognitive decline. However, the underlying mechanisms that drive the benefits of regular physical activity on cognitive function are not fully understood. This randomized controlled trial aims to analyze the effect of an eight-week standardized physical activity training program in older humans on cognitive, brain, and gut-barrier function as well as the relationship between the resulting changes. One-hundred healthy participants aged 60 to 75 years will be recruited. First, participants will undergo an extensive baseline assessment consisting of neurocognitive tests, functional and structural brain imaging, physical fitness tests, and gut-microbiome profiling. Next, participants will be randomized into either a multi-component physical activity group (experimental condition) or a relaxation group (active control condition), with each training lasting 8 weeks and including an equal number and duration of exercises. The whole intervention will be online-based, i.e., participants will find their intervention schedule and all materials needed on the study website. After the intervention phase, participants will have their post-intervention assessment, which consists of the same measures and tests as the baseline assessment. The primary outcome of this study is the change in the cognitive parameter of visual processing speed from baseline to post-measurement, which will on average take place 10 weeks after the randomization. Secondary outcomes related to cognitive, brain, and microbiome data will be analyzed exploratory. Clinical trial registration: https://drks.de/search/de/trial/DRKS00028022.

Alexithymia characteristics are associated with salience network activity in healthy participants: an arterial spin labeling study

BackgroundAlexithymia, a personality trait characterized by difficulties in identifying and expressing their emotions despite having a range of emotional experiences, can impact individuals’ stress coping mechanisms. While many studies have investigated brain functions associated with specific tasks in relation to emotion processing, research focusing on resting-state brain functions has been limited. Thus, the aim of this study was to investigate the relationship between alexithymia and brain function by analyzing arterial spin labeling (ASL) data obtained during the resting state.MethodsA brain structural and functional imaging study was conducted on 42 healthy adult men and women using ASL and the 20-item Toronto Alexithymia Scale (TAS-20) questionnaire survey. Cerebral blood flow and functional connectivity values were calculated for regions of interest in the default mode network, saliency network, and central executive network from the ASL data. Correlation analysis was performed with TAS20 scores, and partial correlation analysis was conducted to control for anxiety and depression.ResultsThe functional connectivity analysis revealed a negative correlation between the functional connectivity of the right insular cortex and left anterior cingulate cortex and the total score of TAS, as well as difficulty identifying feelings and difficulty describing feeling subscores, indicating that the higher the scores, the weaker the functional connectivity between these regions (T = -3.830, p = 0.0013, R = -0.5180). This correlation remained significant even after controlling for anxiety and depression using partial correlation analysis.ConclusionThe present study revealed differences in the activity of the Saliency Network at rest as measured by ASL, which were independent of anxiety and depression, and varied depending on the severity of alexithymia. This functional change may underlie the neural basis of decreased emotional processing observed in alexithymia. These findings may contribute to the elucidation of the neural mechanisms of alexithymia, which can lead to social impairments, and suggest the usefulness of ASL measurement as a biomarker of alexithymia.

Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder.

Generalized anxiety disorder (GAD) and depressive disorder (DD) are distinct mental disorders, which are characterized by complex and unique neuroelectrophysiological mechanisms in psychiatric neurosciences. The understanding of the brain functional differences between GAD and DD is crucial for the accurate diagnosis and clinical efficacy evaluation. The aim of this study was to reveal the differences in functional brain imaging between GAD and DD based on multidimensional electroencephalogram (EEG) characteristics. To this end, 10 min resting-state EEG signals were recorded from 38 GAD and 34 DD individuals. Multidimensional EEG features were subsequently extracted, which include power spectrum density (PSD), fuzzy entropy (FE), and phase lag index (PLI). Then, a direct statistical analysis (i.e., ANOVA) and three ensemble learning models (i.e., Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost)) were used on these EEG features for the differential recognitions. Our results showed that DD has significantly higher PSD values in the alpha1 and beta band, and a higher FE in the beta band, in comparison with GAD, along with the aberrant functional connections in all four bands between GAD and DD. Moreover, machine learning analysis further revealed that the distinct features predominantly occurred in the beta band and functional connections. Here, we show that DD has higher power and more complex brain activity patterns in the beta band and reorganized brain functional network structures in all bands compared to GAD. In sum, these findings move towards the practical identification of brain functional differences between GAD and DD.

Two neurosubtypes of ADHD different from the clinical phenotypes

Clinical and etiological variability of attention deficit hyperactivity disorder (ADHD) presents an obstacle to understand the disorder. The aim of this study was to disentangle the heterogeneity of ADHD using neuroimaging and a semi-supervised machine learning algorithm. We collected brain structural and functional magnetic resonance imaging (MRI) data and clinical profiles of 183 children with ADHD and 396 neurotypical controls from 7 independent sites. We also used an external validation set with 750 subjects. We adopted a semi-supervised clustering method to subtype ADHD by regional volumetric measures of gray matter, white matter, and fractional amplitude of low frequency fluctuation (fALFF). In addition, split sample test, leave-one-site-out test and external validation were applied to evaluate the reproducibility and stability of ADHD subtypes. Two stable and reproducible neurosubtypes of ADHD were disclosed, which were proved by the split-sample test and leave-one-site-out validation. The structural and functional patterns of ADHD subtypes were also stable in the external validation set. The current two neurosubtypes differed in clinical manifestations and volumetric gray matter, white matter volume and fALFF patterns. The current neurosubtypes of ADHD which were different from clinical phenotypes could facilitate understanding the underlying neuropathological and neurobiological mechanism of the disorder.

Functional magnetic particle imaging (fMPI) of cerebrovascular changes in the rat brain during hypercapnia

Objective. Non-invasive functional brain imaging modalities are limited in number, each with its own complex trade-offs between sensitivity, spatial and temporal resolution, and the directness with which the measured signals reflect neuronal activation. Magnetic particle imaging (MPI) directly maps the cerebral blood volume (CBV), and its high sensitivity derives from the nonlinear magnetization of the superparamagnetic iron oxide nanoparticle (SPION) tracer confined to the blood pool. Our work evaluates functional MPI (fMPI) as a new hemodynamic functional imaging modality by mapping the CBV response in a rodent model where CBV is modulated by hypercapnic breathing manipulation. Approach. The rodent fMPI time-series data were acquired with a mechanically rotating field-free line MPI scanner capable of 5 s temporal resolution and 3 mm spatial resolution. The rat’s CBV was modulated for 30 min with alternating 5 min hyper-/hypocapnic states, and processed using conventional fMRI tools. We compare our results to fMRI responses undergoing similar hypercapnia protocols found in the literature, and reinforce this comparison in a study of one rat with 9.4T BOLD fMRI using the identical protocol. Main results. The initial image in the time-series showed mean resting brain voxel SNR values, averaged across rats, of 99.9 following the first 10 mg kg−1 SPION injection and 134 following the second. The time-series fit a conventional General Linear Model with a 15%–40% CBV change and a peak pixel CNR between 12 and 29, 2–6× higher than found in fMRI. Significance. This work introduces a functional modality with high sensitivity, although currently limited spatial and temporal resolution. With future clinical-scale development, a large increase in sensitivity could supplement other modalities and help transition functional brain imaging from a neuroscience tool focusing on population averages to a clinically relevant modality capable of detecting differences in individual patients.

Extrastriate activity reflects the absence of local retinal input

The physiological blind spot corresponds to the optic disc where the retina contains no light-detecting photoreceptor cells. Our perception seemingly fills in this gap in input. Here we suggest that rather than an active process, such perceptual filling-in could instead be a consequence of the integration of visual inputs at higher stages of processing discounting the local absence of retinal input. Using functional brain imaging, we resolved the retinotopic representation of the physiological blind spot in early human visual cortex and measured responses while participants perceived filling-in. Responses in early visual areas simply reflected the absence of visual input. In contrast, higher extrastriate regions responded more to stimuli in the eye containing the blind spot than the fellow eye. However, this signature was independent of filling-in. We argue that these findings agree with philosophical accounts that posit that the concept of filling-in of absent retinal input is unnecessary.

Robust gradient boosting for generalized additive models for location, scale and shape

AbstractDue to the increasing complexity and dimensionality of data sources, it is favorable that methodological approaches yield robust results so that corrupted observations do not jeopardize overall conclusions. We propose a modelling approach which is robust towards outliers in the response variable for generalized additive models for location, scale and shape (GAMLSS). We extend a recently proposed robustification of the log-likelihood to gradient boosting for GAMLSS, which is based on trimming low log-likelihood values via a log-logistic function to a boundary depending on a robustness constant. We recommend a data-driven choice for the involved robustness constant based on a quantile of the unconditioned response variable and investigate the choice in a simulation study for low- and high-dimensional data situations. The versatile application possibilities of robust gradient boosting for GAMLSS are illustrated via three biomedical examples—including the modelling of thyroid hormone levels, spatial effects for functional magnetic resonance brain imaging and a high-dimensional application with gene expression levels for cancer cell lines.

Synchronous measurements of prefrontal activity and pulse rate variability during online video game playing with functional near-infrared spectroscopy

Interactions between the central nervous system (CNS) and autonomic nervous system (ANS) play a crucial role in modulating perception, cognition, and emotion production. Previous studies on CNS–ANS interactions, or heart–brain coupling, have often used heart rate variability (HRV) metrics derived from electrocardiography (ECG) recordings as empirical measurements of sympathetic and parasympathetic activities. Functional near-infrared spectroscopy (fNIRS) is a functional brain imaging modality that is increasingly used in brain and cognition studies. The fNIRS signals contain frequency bands representing both neural activity oscillations and heartbeat rhythms. Therefore, fNIRS data acquired in neuroimaging studies can potentially provide a single-modality approach to measure task-induced responses in the brain and ANS synchronously, allowing analysis of CNS–ANS interactions. In this proof-of-concept study, fNIRS was used to record hemodynamic changes from the foreheads of 20 university students as they each played a round of multiplayer online battle arena (MOBA) game. From the fNIRS recordings, neural and heartbeat frequency bands were extracted to assess prefrontal activities and short-term pulse rate variability (PRV), an approximation for short-term HRV, respectively. Under the experimental conditions used, fNIRS-derived PRV metrics showed good correlations with ECG-derived HRV golden standards, in terms of absolute measurements and video game playing (VGP)-related changes. It was also observed that, similar to previous studies on physical activity and exercise, the PRV metrics closely related to parasympathetic activities recovered slower than the PRV indicators of sympathetic activities after VGP. It is concluded that it is feasible to use fNIRS to monitor concurrent brain and ANS activations during online VGP, facilitating the understanding of VGP-related heart–brain coupling.

Review of imaging test phantoms.

Photoacoustic tomography has emerged as a prominent medical imaging technique that leverages its hybrid nature to provide deep penetration, high resolution, and exceptional optical contrast with notable applications in early cancer detection, functional brain imaging, drug delivery monitoring, and guiding interventional procedures. Test phantoms are pivotal in accelerating technology development and commercialization, specifically in photoacoustic (PA) imaging, and can be optimized to achieve significant advancements in PA imaging capabilities. The analysis of material properties, structural characteristics, and manufacturing methodologies of test phantoms from existing imaging technologies provides valuable insights into their applicability to PA imaging. This investigation enables a deeper understanding of how phantoms can be effectively employed in the context of PA imaging. Three primary categories of test phantoms (simple, intermediate, and advanced) have been developed to differentiate complexity and manufacturing requirements. In addition, four sub-categories (tube/channel, block, test target, and naturally occurring phantoms) have been identified to encompass the structural variations within these categories, resulting in a comprehensive classification system for test phantoms. Based on a thorough examination of literature and studies on phantoms in various imaging modalities, proposals have been put forth for the development of multiple PA-capable phantoms, encompassing considerations related to the material composition, structural design, and specific applications within each sub-category. The advancement of novel and sophisticated test phantoms within each sub-category is poised to foster substantial progress in both the commercialization and development of PA imaging. Moreover, the continued refinement of test phantoms will enable the exploration of new applications and use cases for PA imaging.

Protocol for the development of joint attention-based subclassification of autism spectrum disorder and validation using multi-modal data

BackgroundHeterogeneity in clinical manifestation and underlying neuro-biological mechanisms are major obstacles to providing personalized interventions for individuals with autism spectrum disorder (ASD). Despite various efforts to unify disparate data modalities and machine learning techniques for subclassification, replicable ASD clusters remain elusive. Our study aims to introduce a novel method, utilizing the objective behavioral biomarker of gaze patterns during joint attention, to subclassify ASD. We will assess whether behavior-based subgrouping yields clinically, genetically, and neurologically distinct ASD groups.MethodsWe propose a study involving 60 individuals with ASD recruited from a specialized psychiatric clinic to perform joint attention tasks. Through the examination of gaze patterns in social contexts, we will conduct a semi-supervised clustering analysis, yielding two primary clusters: good gaze response group and poor gaze response group. Subsequent comparison will occur across these clusters, scrutinizing neuroanatomical structure and connectivity using structural as well as functional brain imaging studies, genetic predisposition through single nucleotide polymorphism data, and assorted socio-demographic and clinical information.ConclusionsThe aim of the study is to investigate the discriminative properties and the validity of the joint attention-based subclassification of ASD using multi-modality data.Trial registrationClinical trial, KCT0008530, Registered 16 June 2023, https://cris.nih.go.kr/cris/index/index.do.

Blunted Neurobehavioral Loss Anticipation Predicts Relapse to Stimulant Drug Use

BackgroundPatients with stimulant use disorder experience high rates of relapse. While neurobehavioral mechanisms involved in initiating drug use have been studied extensively, less research has focused on relapse. MethodsTo assess motivational processes involved in relapse and diagnosis, we acquired functional magnetic resonance imaging responses to nondrug (monetary) gains and losses in detoxified patients with stimulant use disorder (n = 68) and community control participants (n = 42). In a prospective multimodal design, we combined imaging of brain function, brain structure, and behavior to longitudinally track subsequent risk for relapse. ResultsAt the 6-month follow-up assessment, 27 patients remained abstinent, but 33 had relapsed. Patients with blunted anterior insula (AIns) activity during loss anticipation were more likely to relapse, an association that remained robust after controlling for potential confounds (i.e., craving, negative mood, years of use, age, and gender). Lower AIns activity during loss anticipation was associated with lower self-reported negative arousal to loss cues and slower behavioral responses to avoid losses, which also independently predicted relapse. Furthermore, AIns activity during loss anticipation was associated with the structural coherence of a tract connecting the AIns and the nucleus accumbens, as was functional connectivity between the AIns and nucleus accumbens during loss processing. However, these neurobehavioral responses did not differ between patients and control participants. ConclusionsTaken together, the results of the current study show that neurobehavioral markers predicted relapse above and beyond conventional self-report measures, with a cross-validated accuracy of 72.7%. These findings offer convergent multimodal evidence that implicates blunted avoidance motivation in relapse to stimulant use and may therefore guide interventions targeting individuals who are most vulnerable to relapse.

Atypical cognitive training-induced learning and brain plasticity and their relation to insistence on sameness in children with autism.

Children with autism spectrum disorders (ASDs) often display atypical learning styles; however, little is known regarding learning-related brain plasticity and its relation to clinical phenotypic features. Here, we investigate cognitive learning and neural plasticity using functional brain imaging and a novel numerical problem-solving training protocol. Children with ASD showed comparable learning relative to typically developing children but were less likely to shift from rule-based to memory-based strategy. While learning gains in typically developing children were associated with greater plasticity of neural representations in the medial temporal lobe and intraparietal sulcus, learning in children with ASD was associated with more stable neural representations. Crucially, the relation between learning and plasticity of neural representations was moderated by insistence on sameness, a core phenotypic feature of ASD. Our study uncovers atypical cognitive and neural mechanisms underlying learning in children with ASD, and informs pedagogical strategies for nurturing cognitive abilities in childhood autism.

Cardiorespiratory fitness is associated with hippocampal resting state connectivity in women newly diagnosed with breast cancer.

Breast cancer and its treatment are associated with aberrant patterns of resting state functional connectivity (rsFC) between the hippocampus and several areas of the brain, which may account for poorer cognitive outcomes in patients. Higher cardiorespiratory fitness (CRF) has been associated with enhanced rsFC and cognitive performance; however, these associations have not been well studied in breast cancer. We examined the relationship between CRF, rsFC of the hippocampus, and cognitive performance among women newly diagnosed with breast cancer. Thirty-four postmenopausal women newly diagnosed with Stage 0-IIIa breast cancer (Mage = 63.59 ± 5.73) were enrolled in a 6-month randomized controlled trial of aerobic exercise vs. usual care. During baseline assessments, participants completed functional brain imaging, a submaximal CRF test, and cognitive testing. Whole-brain, seed-based analyses were used to examine the relationship between CRF and hippocampal rsFC, with age, years of education, and framewise displacement included as covariates. Cognition was measured with a battery of validated neurocognitive measures, reduced to seven composite factors. Higher CRF was positively associated with greater rsFC of the hippocampus to a cluster within the dorsomedial and dorsolateral frontal cortex (z-max = 4.37, p = 0.003, cluster extent = 1,020 voxels). Connectivity within cluster peaks was not significantly related to cognitive factors (all ps > 0.05). CRF was positively associated with hippocampal rsFC to frontal cortex structures, comprising a network of regions commonly suppressed in breast cancer. Future longitudinal research is needed to explore whether baseline rsFC predicts long-term cognitive resilience in breast cancer.

Brain-wide mapping of resting-state networks in mice using high-frame rate functional ultrasound

Functional ultrasound (fUS) imaging is a method for visualizing deep brain activity based on cerebral blood volume changes coupled with neural activity, while functional MRI (fMRI) relies on the blood-oxygenation-level-dependent signal coupled with neural activity. Low-frequency fluctuations (LFF) of fMRI signals during resting-state can be measured by resting-state fMRI (rsfMRI), which allows functional imaging of the whole brain, and the distributions of resting-state network (RSN) can then be estimated from these fluctuations using independent component analysis (ICA). This procedure provides an important method for studying cognitive and psychophysiological diseases affecting specific brain networks. The distributions of RSNs in the brain-wide area has been reported primarily by rsfMRI. RSNs using rsfMRI are generally computed from the time-course of fMRI signals for more than 5 min. However, a recent dynamic functional connectivity study revealed that RSNs are still not perfectly stable even after 10 min. Importantly, fUS has a higher temporal resolution and stronger correlation with neural activity compared with fMRI. Therefore, we hypothesized that fUS applied during the resting-state for a shorter than 5 min would provide similar RSNs compared to fMRI. High temporal resolution rsfUS data were acquired at 10 Hz in awake mice. The quality of the default mode network (DMN), a well-known RSN, was evaluated using signal-noise separation (SNS) applied to different measurement durations of rsfUS. The results showed that the SNS did not change when the measurement duration was increased to more than 210 s. Next, we measured short-duration rsfUS multi-slice measurements in the brain-wide area. The results showed that rsfUS with the short duration succeeded in detecting RSNs distributed in the brain-wide area consistent with RSNs detected by 11.7-T MRI under awake conditions (medial prefrontal cortex and cingulate cortex in the anterior DMN, retrosplenial cortex and visual cortex in the posterior DMN, somatosensory and motor cortexes in the lateral cortical network, thalamus, dorsal hippocampus, and medial cerebellum), confirming the reliability of the RSNs detected by rsfUS. However, bilateral RSNs located in the secondary somatosensory cortex, ventral hippocampus, auditory cortex, and lateral cerebellum extracted from rsfUS were different from the unilateral RSNs extracted from rsfMRI. These findings indicate the potential of rsfUS as a method for analyzing functional brain networks and should encourage future research to elucidate functional brain networks and their relationships with disease model mice.

Specific neural coding of fMRI spiking neural network based on time coding

Specific neural coding is the key to achieving advanced cognitive function in a bio-brain, which can form an identifying coding pattern for external stimulation. The performance of specific neural coding depends extremely on brain-like models. However, the bio-interpretability of the topology of a brain-like model is still insufficient. The purpose of this paper is to investigate a more biological interpretative brain-like model verified by the performance of specific neural coding. In this study, we used the topology constrained by human brain functional magnetic resonance imaging (fMRI) to construct a new spiking neural network (SNN) as a brain-like model called fMRI-SNN. In the fMRI-SNN, the nodes are Izhikevich neuron models, and the edges are synaptic plasticity models with time-delay. Then, we investigated the specific neural coding of fMRI-SNN, and discussed its mechanism. Our results indicated that: (i) fMRI-SNN has obvious specific neural coding based on time coding for different external stimulations. Furthermore, our discussion on relevance analysis implies that the intrinsic element of specific neural coding is synaptic plasticity. (ii) The specific neural coding of fMRI-SNN outperforms that of scale-free SNN and small-world SNN. Furthermore, our discussion on dynamic topological characteristics implies that the network topology is an element that impacts the performance level of the specific neural coding. (iii) Taking a speech recognition task as a case study, the performance of fMRI-SNN outperforms that of scale-free SNN and small-world SNN in terms of speech recognition accuracy.

Effect of auricular acupuncture on neuroplasticity of stroke patients with motor dysfunction: A fNIRS study

Cerebral Stroke is an acute cerebrovascular disease, a disease of brain tissue damage caused by the sudden rupture or blockage of blood vessels in the brain that prevents blood flow to the brain. Acupuncture has become a popular treatment for stroke, with auricular acupuncture providing a new idea for stroke treatment. However, the neuromodulatory mechanism of auricular acupuncture in the brain is still unclear. The aim of this study was to investigate the effect of auricular acupuncture in the treatment of upper limb dysfunction and the activation of specific brain regions in stroke patients. Forty patients with stroke hemiplegia who met the nerf criteria were included in the experiment and randomly assigned into two groups (20 patients in each group): the auricular acupuncture group and the control group. Fugl-Meyer score (FMA) assessment of upper limb motor function, motor evoked potential (MEP) measurement, and functional near-infrared brain function imaging (fNIRS) data acquisition in the primary motor M1 area of the brain at rest were performed before and after treatment, respectively. It was found that: 1) after auricular acupuncture treatment, the patients in the auricular acupuncture group showed significantly greater peak MEP and significantly higher oxyhemoglobin content in the M1 region of the brain compared with the control group, with a significant activation effect (MEP: P-value = 0.032, t = -2.22; HbO2; f = 4.225, p = 0.046); 2) in the clinical efficacy assessment, the FMA score in the auricular acupuncture group after treatment (p = 0.0122, t = 2.769). The results suggest that auricular acupuncture has an ameliorative effect on upper limb motor deficits after stroke and that activation of the M1 region of the brain may be a key node in auricular acupuncture for treating upper limb dysfunction in stroke patients, a finding that emphasizes the potential for clinical application of auricular acupuncture therapy for stroke patients with potential mechanisms influencing the outcome.

Measurements of the lateral cerebellar hemispheres using near-infrared spectroscopy through comparison between autism spectrum disorder and typical development

The cerebellum plays a vital role in cognition, communication with the cerebral cortex, and fine motor coordination. Near-infrared spectroscopy (NIRS) is a portable, less restrictive, and noninvasive functional brain imaging method that can capture brain activity during movements by measuring the relative oxyhemoglobin (oxy-Hb) concentrations in the blood. However, the feasibility of using NIRS to measure cerebellar activity requires discussion. We compared NIRS responses between areas assumed to be the cerebellum and the occipital lobe during a fine motor task (tying a bow knot) and a visual task. Our results showed that the oxy-Hb concentration increased more in the occipital lobe than in the cerebellum during the visual task (p =.034). In contrast, during the fine motor task, the oxy-Hb concentration decreased in the occipital lobe but increased significantly in the cerebellum, indicating a notable difference (p =.015). These findings suggest that we successfully captured cerebellar activity associated with processing, particularly fine motor coordination. Moreover, the observed responses did not differ between individuals with autism spectrum disorder and those with typical development. Our study demonstrates the meaningful utility of NIRS as a method for measuring cerebellar activity during movements.

Micro- and macro-changes in early-stage type 2 diabetes mellitus without cognitive impairment: a diffusion tensor imaging (DTI) and surface-based morphometry (SBM) study.

Brain structure and function changes are considered major brain damages in type 2 diabetes mellitus (T2DM), which likely has a close relationship with cognitive impairment. Many previous studies have shown by using brain structural and functional magnetic resonance imaging (MRI) methods that brain white and gray matter are damaged in T2DM, leading to cognitive impairment. Researches neglected patients of T2DM without cognitive dysfunction might also have brain changes. In this study, subjects with early stage T2DM with no cognitive dysfunction were enrolled to detect brain damages using the tract-based spatial statistics analysis (TBSS) method to demonstrate white matter (WM) micro changes and surface-based morphometry (SBM) method to assess cerebral cortex macro changes. The whole-brain TBSS analysis revealed that there were no statistically significant changes in fractional anisotropy (FA) and mean diffusivity (MD), but the FA declined in some area of cerebral WM (p < 0.1). The SBM results showed no changes in cortical thickness (CT), cortical volume (CV), surface area (SA), and cortical sulcal curve (CSC) between these two groups, but pial local gyration index (LGI) was decreased in the precuneus (-log10, p = -3.327). In conclusion, early stage T2DM patients without cognitive impairment had brain micro and macro structural damages, suggesting the potential use of MRI as an imaging marker to detect brain changes in early stage T2DM, which could not be observed and assessed clinically.

Experimental demonstration of a new near-infrared spectroscopy technique based on optical dual-comb: DC-NIRS

We present a novel near-infrared spectroscopy technique based on Dual-Comb optical interrogation (DC-NIRS) applied to dispersive media. The technique recovers the frequency response of the medium under investigation by sampling its spectral response in amplitude and phase. The DC-NIRS reference and sample signals are generated using electro-optic modulation which offers a cost-effective, integrable solution while providing high adaptability to the interrogated medium. A careful choice of both line spacing and optical span of the frequency comb ensures that the retrieved information enables the reconstruction of the temporal impulse response of the medium, known as the diffuse-time-of-flight (DTOF), to obtain its optical properties with a 70 µs temporal resolution and 32 ps photon propagation delay resolution. Furthermore, the DC-NIRS technique also offers enhanced penetration due to noiseless optical amplification (interferometric detection). The presented technique was demonstrated on a static bio-mimetic phantom of known optical properties reproducing a typical brain’s optical response. The DTOF and optical properties of the phantom were measured, showing the capabilities of this new technique on the estimation of absolute optical properties with a deviation under 3%. Compared to current technologies, our DC-NIRS technique provides enhanced temporal resolution, spatial location capabilities, and penetration depth, with an integrable and configurable cost-effective architecture, paving the way to next-generation, non-invasive and portable systems for functional brain imaging, and brain-computer interfaces, among other. The system is patent pending PCT/ES2022/070176.

Dynamic Modelling of Mental Resilience in Young Adults: Protocol for a Longitudinal Observational Study (DynaM-OBS)

Background Stress-related mental disorders are highly prevalent and pose a substantial burden on individuals and society. Improving strategies for the prevention and treatment of mental disorders requires a better understanding of their risk and resilience factors. This multicenter study aims to contribute to this endeavor by investigating psychological resilience in healthy but susceptible young adults over 9 months. Resilience is conceptualized in this study as the maintenance of mental health or quick recovery from mental health perturbations upon exposure to stressors, assessed longitudinally via frequent monitoring of stressors and mental health. Objective This study aims to investigate the factors predicting mental resilience and adaptive processes and mechanisms contributing to mental resilience and to provide a methodological and evidence-based framework for later intervention studies. Methods In a multicenter setting, across 5 research sites, a sample with a total target size of 250 young male and female adults was assessed longitudinally over 9 months. Participants were included if they reported at least 3 past stressful life events and an elevated level of (internalizing) mental health problems but were not presently affected by any mental disorder other than mild depression. At baseline, sociodemographic, psychological, neuropsychological, structural, and functional brain imaging; salivary cortisol and α-amylase levels; and cardiovascular data were acquired. In a 6-month longitudinal phase 1, stressor exposure, mental health problems, and perceived positive appraisal were monitored biweekly in a web-based environment, while ecological momentary assessments and ecological physiological assessments took place once per month for 1 week, using mobile phones and wristbands. In a subsequent 3-month longitudinal phase 2, web-based monitoring was reduced to once a month, and psychological resilience and risk factors were assessed again at the end of the 9-month period. In addition, samples for genetic, epigenetic, and microbiome analyses were collected at baseline and at months 3 and 6. As an approximation of resilience, an individual stressor reactivity score will be calculated. Using regularized regression methods, network modeling, ordinary differential equations, landmarking methods, and neural net–based methods for imputation and dimension reduction, we will identify the predictors and mechanisms of stressor reactivity and thus be able to identify resilience factors and mechanisms that facilitate adaptation to stressors. Results Participant inclusion began in October 2020, and data acquisition was completed in June 2022. A total of 249 participants were assessed at baseline, 209 finished longitudinal phase 1, and 153 finished longitudinal phase 2. Conclusions The Dynamic Modelling of Resilience–Observational Study provides a methodological framework and data set to identify predictors and mechanisms of mental resilience, which are intended to serve as an empirical foundation for future intervention studies. International Registered Report Identifier (IRRID) DERR1-10.2196/39817