Neuroimaging Modalities Research Articles

Machine Learning-based Filtering system for fNIRS Signals’ Analysis Purpose

This paper presents a preliminary study delving into the application of machine learning-based methods for optimising parameter selection in filtering techniques. The authors focus on exploring the efficacy of two prominent filtering methods: smoothing and cascade filters, known for their profound impact on enhancing the quality of brain signals. The study specifically examines signals acquired through functional near-infrared spectroscopy (fNIRS), a non-invasive neuroimaging modality offering valuable insights into brain activity. Through meticulous analysis, the research underscores the potential of machine learning approaches in discerning optimal parameters for filtering, thereby leading to a significant enhancement in the quality and reliability of fNIRS-derived signals. The results demonstrate the effectiveness of machine learning-based methods in optimizing parameter selection for filtering techniques, particularly in the context of fNIRS signals. By leveraging these approaches, the study achieves notable improvements in the quality and reliability of brain signal data. This work sheds light on promising avenues for refining neuroimaging methodologies and advancing the field of signal processing in neuroscience. The successful application of machine learning-based techniques highlights their potential for optimizing neuroimaging data processing, ultimately contributing to a deeper understanding of brain function.

Maternal Immune Activation and Child Brain Development: A Longitudinal Population-based Multimodal Neuroimaging study

Maternal Immune Activation (MIA) has been hypothesized to have an adverse effect on child neurodevelopment, but only a few neuroimaging studies have been performed to date, mostly in neonates. In this population-based cohort study, we investigated the association between MIA and multiple neuroimaging modalities depicting brain development from childhood to adolescence. We used data of mother-child pairs from the Generation R Study. To define our exposure, we measured IL-1β, IL-6, IL-17a, IL-23 and IFN-γ, and CRP at two time points during pregnancy. Given that levels of these 5 cytokines were highly correlated, we were able to compute a Cytokine index. We used multiple brain imaging modalities as outcomes, encompassing global and regional measures of brain morphology (structural MRI, volume, n=3,295), white matter microstructure (diffusion MRI, FA and MD, n=3,267), and functional connectivity (functional MRI, graph theory measures and network-level connectivity, n=2,914) at child mean ages 10 and 14 years. We performed mixed-effects models using the child's age as continuous time variable. We found no significant association or time interaction between MIA and any neuroimaging outcomes in children over time. These associations were similar for the Cytokine index, CRP, and individual cytokines. We observed no evidence for differential effects of timing of prenatal MIA or child sex after multiple testing correction. This longitudinal population-based study reports no evidence for an association between MIA and child brain development in the general population. Our findings differ from prior research in neonates showing structural and functional brain abnormalities after MIA.

A Thorough Review of Deep Learning in Autism Spectrum Disorder Detection: From Data to Diagnosis

Background: Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition with significant heterogeneity in its clinical presentation. Timely and precise identification of ASD is crucial for effective intervention and assistance. Recent advances in deep learning techniques have shown promise in enhancing the accuracy of ASD detection. Objective: This comprehensive review aims to provide an overview of various deep learning methods employed in detecting ASD, utilizing diverse neuroimaging modalities. We analyze a range of studies that use resting-state functional Magnetic Resonance Imaging (rsfMRI), structural MRI (sMRI), task-based fMRI (tfMRI), and electroencephalography (EEG). This paper aims to assess the effectiveness of these techniques based on criteria such as accuracy, sensitivity, specificity, and computational efficiency. Methods: We systematically review studies investigating ASD detection using deep learning across different neuroimaging modalities. These studies utilize various preprocessing tools, atlases, feature extraction techniques, and classification algorithms. The performance metrics of interest include accuracy, sensitivity, specificity, precision, F1-score, recall, and area under the curve (AUC). Results: The review covers a wide range of studies, each with its own dataset and methodology. Notable findings include a study employing rsfMRI data from ABIDE that achieved an accuracy of 80% using LeNet. Another study using rsfMRI data from ABIDE-II achieved an impressive accuracy of 95.4% with the ASGCN deep learning model. Studies utilizing different modalities, such as EEG and sMRI, also reported high accuracies ranging from 74% to 95%. Conclusion: Deep learning-based approaches for ASD detection have demonstrated significant potential across multiple neuroimaging modalities. These methods offer a more objective and data-driven approach to diagnosis, potentially reducing the subjectivity associated with clinical evaluations. However, challenges remain, including the need for larger and more diverse datasets, model interpretability, and clinical validation. The field of deep learning in ASD diagnosis continues to evolve, holding promise for early and accurate identification of individuals with ASD, which is crucial for timely intervention and support.

Advancements in Alzheimer's disease classification using deep learning frameworks for multimodal neuroimaging: A comprehensive review

Over the past years, Alzheimer's disease has emerged as a serious concern for people's health. Researchers are facing challenges in effectively categorizing and diagnosing the different stages of Alzheimer's disease (AD). Current promising studies have shown that multimodal Neuroimaging has the potential to offer vital information about the structural and functional alterations associated with Alzheimer's. Using advanced computational techniques, Machine Learning calculations have been demonstrated to be highly precise in deciphering patterns and connections within the multimodal Neuroimaging data, eventually aiding in the arrangement of Alzheimer's illness stages. This research aimed to survey the adequacy of Machine Learning techniques in correctly categorizing stages of Alzheimer's disease by working on multiple neuroimaging modalities. In this review, a detailed analysis was carried out on the classification algorithms included. The study specifically examines publications published between 2016 and 2024. From the review, it was found that deep learning frameworks are more robust in Alzheimer's disease classification.

Functional and connectivity correlates associated with Parkinson's disease psychosis: a systematic review.

Neural underpinnings of Parkinson's disease psychosis remain unclear to this day with relatively few studies and reviews available. Using a systematic review approach, here, we aimed to qualitatively synthesize evidence from studies investigating Parkinson's psychosis-specific alterations in brain structure, function or chemistry using different neuroimaging modalities. PubMed, Web of Science and Embase databases were searched for functional MRI (task-based and resting state), diffusion tensor imaging, PET and single-photon emission computed tomography studies comparing Parkinson's disease psychosis patients with Parkinson's patients without psychosis. We report findings from 29 studies (514 Parkinson's psychosis patients, mean age ± SD = 67.92 ± 4.37 years; 51.36% males; 853 Parkinson's patients, mean age ± SD = 66.75 ± 4.19 years; 55.81% males). Qualitative synthesis revealed widespread patterns of altered brain function across task-based and resting-state functional MRI studies in Parkinson's psychosis patients compared with Parkinson's patients without psychosis. Similarly, white matter abnormalities were reported in parietal, temporal and occipital regions. Hypo-metabolism and reduced dopamine transporter binding were also reported whole brain and in sub-cortical areas. This suggests extensive alterations affecting regions involved in high-order visual processing and attentional networks.

Compensatory mechanisms amidst demyelinating disorders: insights into cognitive preservation.

Demyelination disrupts the transmission of electrical signals in the brain and affects neurodevelopment in children with disorders such as multiple sclerosis and myelin oligodendrocyte glycoprotein-associated disorders. Although cognitive impairments are prevalent in these conditions, some children maintain cognitive function despite substantial structural injury. These findings raise an important question: in addition to the degenerative process, do compensatory neural mechanisms exist to mitigate the effects of myelin loss? We propose that a multi-dimensional approach integrating multiple neuroimaging modalities, including diffusion tensor imaging, magnetoencephalography and eye-tracking, is key to investigating this question. We examine the structural and functional connectivity of the default mode and executive control networks due to their significant roles in supporting higher-order cognitive processes. As cognitive proxies, we examine saccade reaction times and direction errors during an interleaved pro- (eye movement towards a target) and anti-saccade (eye movement away from a target) task. 28 typically developing children, 18 children with multiple sclerosis and 14 children with myelin oligodendrocyte glycoprotein-associated disorders between 5 and 18.9 years old were scanned at the Hospital for Sick Children. Tractography of diffusion MRI data examined structural connectivity. Intracellular and extracellular microstructural parameters were extracted using a white matter tract integrity model to provide specific inferences on myelin and axon structure. Magnetoencephalography scanning was conducted to examine functional connectivity. Within groups, participants had longer saccade reaction times and greater direction errors on the anti- versus pro-saccade task; there were no group differences on either task. Despite similar behavioural performance, children with demyelinating disorders had significant structural compromise and lower bilateral high gamma, higher left-hemisphere theta and higher right-hemisphere alpha synchrony relative to typically developing children. Children diagnosed with multiple sclerosis had greater structural compromise relative to children with myelin oligodendrocyte glycoprotein-associated disorders; there were no group differences in neural synchrony. For both patient groups, increased disease disability predicted greater structural compromise, which predicted longer saccade reaction times and greater direction errors on both tasks. Structural compromise also predicted increased functional connectivity, highlighting potential adaptive functional reorganisation in response to structural compromise. In turn, increased functional connectivity predicted faster saccade reaction times and fewer direction errors. These findings suggest that increased functional connectivity, indicated by increased alpha and theta synchrony, may be necessary to compensate for structural compromise and preserve cognitive abilities. Further understanding these compensatory neural mechanisms could pave the way for the development of targeted therapeutic interventions aimed at enhancing these mechanisms, ultimately improving cognitive outcomes for affected individuals.

From Sound to Movement: Mapping the Neural Mechanisms of Auditory–Motor Entrainment and Synchronization

Background: Humans exhibit a remarkable ability to synchronize their actions with external auditory stimuli through a process called auditory–motor or rhythmic entrainment. Positive effects of rhythmic entrainment have been demonstrated in adults with neurological movement disorders, yet the neural substrates supporting the transformation of auditory input into timed rhythmic motor outputs are not fully understood. We aimed to systematically map and synthesize the research on the neural correlates of auditory–motor entrainment and synchronization. Methods: Following the PRISMA-ScR guidelines for scoping reviews, a systematic search was conducted across four databases (MEDLINE, Embase, PsycInfo, and Scopus) for articles published between 2013 and 2023. Results: From an initial return of 1430 records, 22 studies met the inclusion criteria and were synthesized based on the neuroimaging modality. There is converging evidence that auditory–motor synchronization engages bilateral cortical and subcortical networks, including the supplementary motor area, premotor cortex, ventrolateral prefrontal cortex, basal ganglia, and cerebellum. Specifically, the supplementary motor area and the basal ganglia are essential for beat-based timing and internally guided rhythmic movements, while the cerebellum plays an important role in tracking and processing complex rhythmic patterns and synchronizing to the external beat. Self-paced tapping is associated with additional activations in the prefrontal cortex and the basal ganglia, suggesting that tapping in the absence of auditory cues requires more neural resources. Lastly, existing studies indicate that movement rate and the type of music further modulate the EEG power in the alpha and beta frequency bands. Conclusions: These findings are discussed in the context of clinical implications and rhythm-based therapies.

Ultra-Low-Field Portable MRI and Extracorporeal Membrane Oxygenation: Preclinical Safety Testing.

Conventional MRI is incompatible with extracorporeal membrane oxygenation (ECMO) cannulas and pumps. Ultra-low-field portable MRI (ULF-pMRI) with 0.064 Tesla may provide a solution, but its safety and compatibility is unknown. ULF-pMRI does not cause significant displacement and heating of ECMO cannulas and does not affect ECMO pump function. ECMO cannulas in various sizes were tested ex vivo using phantom models to assess displacement force and heating according to the American Society for Testing and Materials criteria. ECMO pump function was assessed by pump flow and power consumption. In vivo studies involved five female domestic pigs (20-42 kg) undergoing different ECMO configurations (peripheral and central cannulation) and types of cannulas with an imaging protocol consisting of T2-weighted, T1-weighted, FLuid-Attenuated Inversion Recovery, and diffusion-weighted imaging sequences. Phantom models demonstrated that ECMO cannulas, both single lumen with various sizes (15-24-Fr) and double lumen cannula, had average displacement force less than gravitational force within 5 gauss safety line of ULF-pMRI and temperature changes less than 1°C over 15 minutes of scanning and ECMO pump maintained stable flow and power consumption immediately outside of the 5 gauss line. All pig models showed no visible motion due to displacement force or heating of the cannulas. ECMO flow and the animals' hemodynamic status maintained stability, with no changes greater than 10%, respectively. ULF-pMRI is safe and feasible for use with standard ECMO configurations, supporting its clinical application as a neuroimaging modality in ECMO patients.

Multimodal investigation of dynamic brain network alterations in autism spectrum disorder: Linking connectivity dynamics to symptoms and developmental trajectories

BackgroundAutism spectrum disorder (ASD) has been associated with disrupted brain connectivity, yet a comprehensive understanding of the dynamic neural underpinnings remains lacking. This study employed concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) techniques to investigate dynamic functional connectivity (dFC) patterns and neurovascular characteristics in children with ASD. We also explored associations between neurovascular characteristics and the developmental trajectory of adaptive behavior in individuals with ASD. MethodsResting-state EEG and fNIRS data were simultaneously recorded from 58 ASD and 63 TD children. We implemented a k-means clustering approach to extract the dFC states for each modality. In addition, a multimodal covariance network (MCN) was constructed from the EEG and fNIRS dFC features to capture the neurovascular characteristics linked to ASD. ResultsEEG analyses revealed atypical properties of dFC states in the beta and gamma bands in children with ASD compared to TD children. For fNIRS, the ASD group exhibited atypical properties of dFC states such as duration and transitions relative to the TD group. The MCN analysis revealed significantly suppressed functional covariance between right superior temporal and left Broca's areas, alongside enhanced right dorsolateral prefrontal-left Broca covariance in ASD. Notably, we found that early neurovascular characteristics can predict the developmental progress of adaptive functioning in ASD. ConclusionThe multimodal investigation revealed distinct dFC patterns and neurovascular characteristics associated with ASD, elucidating potential neural mechanisms underlying core symptoms and their developmental trajectories. Our study highlights that integrating complementary neuroimaging modalities may aid in unraveling the complex neurobiology of ASD.

Heterogeneity and convergence across seven neuroimaging modalities: a review of the autism spectrum disorder literature.

A growing body of literature classifies autism spectrum disorder (ASD) as a heterogeneous, complex neurodevelopmental disorder that often is identified prior to three years of age. We aim to provide a narrative review of key structural and functional properties that differentiate the neuroimaging profile of autistic youth from their typically developing (TD) peers across different neuroimaging modalities. Relevant studies were identified by searching for key terms in PubMed, with the most recent search conducted on September 1, 2023. Original research papers were included if they applied at least one of seven neuroimaging modalities (structural MRI, functional MRI, DTI, MRS, fNIRS, MEG, EEG) to compare autistic children or those with a family history of ASD to TD youth or those without ASD family history; included only participants <18 years; and were published from 2013 to 2023. In total, 172 papers were considered for qualitative synthesis. When comparing ASD to TD groups, structural MRI-based papers (n = 26) indicated larger subcortical gray matter volume in ASD groups. DTI-based papers (n = 14) reported higher mean and radial diffusivity in ASD participants. Functional MRI-based papers (n = 41) reported a substantial number of between-network functional connectivity findings in both directions. MRS-based papers (n = 19) demonstrated higher metabolite markers of excitatory neurotransmission and lower inhibitory markers in ASD groups. fNIRS-based papers (n = 20) reported lower oxygenated hemoglobin signals in ASD. Converging findings in MEG- (n = 20) and EEG-based (n = 32) papers indicated lower event-related potential and field amplitudes in ASD groups. Findings in the anterior cingulate cortex, insula, prefrontal cortex, amygdala, thalamus, cerebellum, corpus callosum, and default mode network appeared numerous times across modalities and provided opportunities for multimodal qualitative analysis. Comparing across neuroimaging modalities, we found significant differences between the ASD and TD neuroimaging profile in addition to substantial heterogeneity. Inconsistent results are frequently seen within imaging modalities, comparable study populations and research designs. Still, converging patterns across imaging modalities support various existing theories on ASD.

T1- and T2-weighted MRI signal and histology findings in suboptimally fixed human brains

Neuroscientific research that requires brain tissue depends on brain banks that provide very small tissue samples fixed by immersion in neutral-buffered formalin (NBF), while anatomy laboratories could provide full brain specimens. However, these brains are generally fixed by perfusion of the full body with solutions other than NBF generally used by brain banks, such as an alcohol-formaldehyde solution (AFS) that is typically used for dissection and teaching. Therefore, fixation quality of these brains needs to be assessed to determine their usefulness in post-mortem investigations through magnetic resonance imaging (MRI) and histology, two common neuroimaging modalities. Here, we report the characteristics of five brains fixed by full body perfusion of an AFS from our Anatomy Laboratory suspected of being poorly fixed, given the altered signal seen on T1w MRI scans in situ. We describe 1- the characteristics of the donors; 2- the fixation procedures applied for each case; 3- the tissue contrast characteristics of the T1w and T2w images; 4- the macroscopic tissue quality after extraction of the brains; 5- the macroscopic arterial characteristics and presence or absence of blood clots; and 6- four histological stains of the areas that we suspected were poorly fixed. We conclude that multiple factors can affect the fixation quality of the brain. Nevertheless, cases in which brain fixation is suboptimal, consequently altering the T1w signal, still have T2w of adequate gray-matter to white-matter contrast and may also be used for histology stains with sufficient quality.

Neurophysiological Approach for Psychological Safety: Enhancing Mental Health in Human–Robot Collaboration in Smart Manufacturing Setups Using Neuroimaging

Human–robot collaboration (HRC) has become increasingly prevalent due to innovative advancements in the automation industry, especially in manufacturing setups. Although HRC increases productivity and efficacy, it exposes human workers to psychological stress while interfacing with collaborative robotic systems as robots may not provide visual or auditory cues. It is crucial to comprehend how HRC impacts mental stress in order to enhance occupational safety and well-being. Though academics and industrial interest in HRC is expanding, safety and mental stress problems are still not adequately studied. In particular, human coworkers’ cognitive strain during HRC has not been explored well, although being fundamental to sustaining a secure and constructive workplace environment. This study, therefore, aims to monitor the mental stress of factory workers during HRC using behavioural, physiological and subjective measures. Physiological measures, being objective and more authentic, have the potential to replace conventional measures i.e., behavioural and subjective measures, if they demonstrate a good correlation with traditional measures. Two neuroimaging modalities including electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have been used as physiological measures to track neuronal and hemodynamic activity of the brain, respectively. Here, the correlation between physiological data and behavioural and subjective measurements has been ascertained through the implementation of seven different machine learning algorithms. The results imply that the EEG and fNIRS features combined produced the best results for most of the targets. For subjective measures being the target, linear regression has outperformed all other models, whereas tree and ensemble performed the best for predicting the behavioural measures. The outcomes indicate that physiological measures have the potential to be more informative and often substitute other skewed metrics.

Enhancing EEG data quality and precision for cloud-based clinical applications: an evaluation of the SLOG framework

Automation is revamping our preprocessing pipelines, and accelerating the delivery of personalized digital medicine. It improves efficiency, reduces costs, and allows clinicians to treat patients without significant delays. However, the influx of multimodal data highlights the need to protect sensitive information, such as clinical data, and safeguard data fidelity. One of the neuroimaging modalities that produces large amounts of time-series data is Electroencephalography (EEG). It captures the neural dynamics in a task or resting brain state with high temporal resolution. EEG electrodes placed on the scalp acquire electrical activity from the brain. These electrical potentials attenuate as they cross multiple layers of brain tissue and fluid yielding relatively weaker signals than noise-low signal-to-noise ratio. EEG signals are further distorted by internal physiological artifacts, such as eye movements (EOG) or heartbeat (ECG), and external noise, such as line noise (50 Hz). EOG artifacts, due to their proximity to the frontal brain regions, are particularly challenging to eliminate. Therefore, a widely used EOG rejection method, independent component analysis (ICA), demands manual inspection of the marked EOG components before they are rejected from the EEG data. We underscore the inaccuracy of automatized ICA rejection and provide an auxiliary algorithm-Second Layer Inspection for EOG (SLOG) in the clinical environment. SLOG based on spatial and temporal patterns of eye movements, re-examines the already marked EOG artifacts and confirms no EEG-related activity is mistakenly eliminated in this artifact rejection step. SLOG achieved a 99% precision rate on the simulated dataset while 85% precision on the real EEG dataset. One of the primary considerations for cloud-based applications is operational costs, including computing power. Algorithms like SLOG allow us to maintain data fidelity and precision without overloading the cloud platforms and maxing out our budgets.

OTHR-17 MANAGEMENT AND OUTCOMES OF SELLAR, SUPRASELLAR, AND PARASELLAR MASSES IN LOW- AND MIDDLE-INCOME COUNTRIES: A SCOPING REVIEW AND PROPORTIONAL META-ANALYSIS OF 6584 MASSES

Abstract OBJECTIVE To aggregate data on treatment modalities, management approaches, and clinical outcomes of sellar, suprasellar, and parasellar masses in LMICs. METHODS We conducted a scoping review as per the Arksey and O’Malley framework. MEDLINE, Embase, Global Index Medicus, and African Journals Online were searched and included articles between 2000 and 2021 screened against predefined eligibility criteria. Pooled statistics were calculated using measures of central tendency and spread. A proportional meta-analysis was conducted to pool the mortality rates. RESULTS Of the 3526 articles generated by the search strategy, 173 articles were included. The mean age was 35.5 ± 15 years, and most were females (56.47%). Sellar masses predominated (85%; 95% CI = 93.16–118.58), and the most commonly used neuroimaging modality to diagnose these masses was an MRI (65.31%). Surgical resection was mostly utilised with endoscopic surgery (78%; 95% CI = 17.29–154.96) predominating over microsurgery (22%; 95% CI = 11.51–47.73). The transsphenoidal approach was mostly used for both endoscopic surgery and microsurgery (92.13% vs 93.21%), and the extent of resection was gross total resection in most cases (n = 3611). Non-surgical management included hormonal therapies (n = 2080), chemotherapy (n = 96 patients), and radiosurgery (n = 357). New onset diabetes insipidus (34.27%), followed by postoperative infection (27.95%), were the most commonly reported postoperative complications. The pooled overall mortality rate was 8.1% (95% CI: 0.031–0.146). CONCLUSION Although MRIs are considered to be the gold standard means of diagnosis for these masses, their use in LMICs is less reported. Despite new minimally invasive treatment options, endoscopic surgery is commonly used in LMICs. These translates to the fact that more effort is required in the effective care of patients with these masses in LMICs.

Effects of high-definition tDCS targeting individual motor hotspot with EMG-driven robotic hand training on upper extremity motor function: a pilot randomized controlled trial

BackgroundDelivering HD-tDCS on individual motor hotspot with optimal electric fields could overcome challenges of stroke heterogeneity, potentially facilitating neural activation and improving motor function for stroke survivors. However, the intervention effect of this personalized HD-tDCS has not been explored on post-stroke motor recovery. In this study, we aim to evaluate whether targeting individual motor hotspot with HD-tDCS followed by EMG-driven robotic hand training could further facilitate the upper extremity motor function for chronic stroke survivors.MethodsIn this pilot randomized controlled trial, eighteen chronic stroke survivors were randomly allocated into two groups. The HDtDCS-group (n = 8) received personalized HD-tDCS using task-based fMRI to guide the stimulation on individual motor hotspot. The Sham-group (n = 10) received only sham stimulation. Both groups underwent 20 sessions of training, each session began with 20 min of HD-tDCS and was then followed by 60 min of robotic hand training. Clinical scales (Fugl-meyer Upper Extremity scale, FMAUE; Modified Ashworth Scale, MAS), and neuroimaging modalities (fMRI and EEG-EMG) were conducted before, after intervention, and at 6-month follow-up. Two-way repeated measures analysis of variance was used to compare the training effect between HDtDCS- and Sham-group.ResultsHDtDCS-group demonstrated significantly better motor improvement than the Sham-group in terms of greater changes of FMAUE scores (F = 6.5, P = 0.004) and MASf (F = 3.6, P = 0.038) immediately and 6 months after the 20-session intervention. The task-based fMRI activation significantly shifted to the ipsilesional motor area in the HDtDCS-group, and this activation pattern increasingly concentrated on the motor hotspot being stimulated 6 months after training within the HDtDCS-group, whereas the increased activation is not sustainable in the Sham-group. The neuroimaging results indicate that neural plastic changes of the HDtDCS-group were guided specifically and sustained as an add-on effect of the stimulation.ConclusionsStimulating the individual motor hotspot before robotic hand training could further enhance brain activation in motor-related regions that promote better motor recovery for chronic stroke.Trial registrationThis study was retrospectively registered in ClinicalTrials.gov (ID NCT05638464).

Convergent Multimodal Imaging Abnormalities in the Dorsal Precuneus in Subjective Cognitive Decline.

A range of imaging modalities have reported Alzheimer's disease-related abnormalities in individuals experiencing subjective cognitive decline (SCD). However, there has been no consistent local abnormality identified across multiple neuroimaging modalities for SCD. We aimed to investigate the convergent local alterations in amyloid-β (Aβ) deposition, glucose metabolism, and resting-state functional MRI (RS-fMRI) metrics in SCD. Fifty SCD patients (66.4±5.7 years old, 19 men [38%]) and 15 normal controls (NC) (66.3±4.4 years old, 5 men [33.3%]) were scanned with both [18F]-florbetapir PET and [18F]-fluorodeoxyglucose PET, as well as simultaneous RS-fMRI from February 2018 to November 2018. Voxel-wise metrics were retrospectively analyzed, including Aβ deposition, glucose metabolism, amplitude of low frequency fluctuation (ALFF), regional homogeneity (ReHo), and degree centrality(DC). The SCD group showed increased Aβ deposition and glucose metabolism (p < 0.05, corrected), as well as decreased ALFF, ReHo, and DC (p < 0.05, uncorrected) in the left dorsal precuneus (dPCu). Furthermore, the dPCu illustrated negative resting-state functional connectivity with the default mode network. Regarding global Aβ deposition positivity, the Aβ deposition in the left dPCu showed a gradient change, i.e., Aβ positive SCD > Aβ negative SCD > Aβ negative NC. Additionally, both Aβ positive SCD and Aβ negative SCD showed increased glucose metabolism and decreased RS-fMRI metrics in the dPCu. The dorsal precuneus, an area implicated in early AD, shows convergent neuroimaging alterations in SCD, and might be more related to other cognitive functions (e.g., unfocused attention) than episodic memory.

Constructing the "Family Personality": Can Family Functioning Be Linked to Parent-Child Interpersonal Neural Synchronization?

Early child development occurs within an interactive environment, initially dominated by parents or caregivers, and is heavily influenced by the dynamics of this social context. The current study probed the neurobiology of "family personality", or family functioning, in the context of parent-child dyadic interaction using a two-person neuroimaging modality. One hundred and five parent-child dyads (child mean age 5 years 4 months) were recruited. Functional near-infrared spectroscopy (fNIRS) hyperscanning was employed to measure neural synchrony while dyads completed a mildly stressful interactive task. Family functioning was measured through the Family Adaptability and Cohesion Scale IV (FACES-IV). Synchrony during stress was significantly greater than synchrony during both baseline and recovery conditions for all dyads. A significant interaction between neural synchrony in each task condition and familial balanced flexibility was found, such that higher levels of balanced flexibility were associated with greater changes in frontal cortex neural synchrony as dyads progressed through the three task conditions. Parent-child dyads from families who display heightened levels of balanced flexibility are also more flexible in their engagement of neural synchrony when shifting between social conditions. This is one of the first studies to utilize a two-person imaging modality to explore the links between family functioning and interbrain synchrony between parents and their children.

Neuroimaging Biomarkers in Addiction.

As a neurobiological process, addiction involves pathological patterns of engagement with substances and a range of behaviors with a chronic and relapsing course. Neuroimaging technologies assess brain activity, structure, physiology, and metabolism at scales ranging from neurotransmitter receptors to large-scale brain networks, providing unique windows into the core neural processes implicated in substance use disorders. Identified aberrations in the neural substrates of reward and salience processing, response inhibition, interoception, and executive functions with neuroimaging can inform the development of pharmacological, neuromodulatory, and psychotherapeutic interventions to modulate the disordered neurobiology. Based on our systematic search, 409 protocols registered on ClinicalTrials.gov include the use of one or more neuroimaging paradigms as an outcome measure in addiction, with the majority (N=268) employing functional magnetic resonance imaging (fMRI), followed by positron emission tomography (PET) (N=71), electroencephalography (EEG) (N=50), structural magnetic resonance imaging (MRI) (N=35) and magnetic resonance spectroscopy (MRS) (N=35). Furthermore, in a PubMed systematic review, we identified 61 meta-analyses including 30 fMRI, 22 structural MRI, 8 EEG, 7 PET, and 3 MRS meta-analyses suggesting potential biomarkers in addictions. These studies can facilitate the development of a range of biomarkers that may prove useful in the arsenal of addiction treatments in the coming years. There is evidence that these markers of large-scale brain structure and activity may indicate vulnerability or separate disease subtypes, predict response to treatment, or provide objective measures of treatment response or recovery. Neuroimaging biomarkers can also suggest novel targets for interventions. Closed or open loop interventions can integrate these biomarkers with neuromodulation in real-time or offline to personalize stimulation parameters and deliver the precise intervention. This review provides an overview of neuroimaging modalities in addiction, potential neuroimaging biomarkers, and their physiologic and clinical relevance. Future directions and challenges in bringing these putative biomarkers from the bench to the bedside are also discussed.

Atypical Parkinsonian Syndromes: Structural, Functional, and Molecular Imaging Features.

Atypical parkinsonian syndromes, also known as Parkinson-plus syndromes, are a heterogeneous group of movement disorders, including dementia with Lewy bodies (DLB), progressive supranuclear palsy (PSP), multisystem atrophy (MSA), and corticobasal degeneration (CBD). This review highlights the characteristic structural, functional, and molecular imaging features of these complex disorders. DLB typically demonstrates parieto-occipital hypometabolism with involvement of the cuneus on FDG-PET, whereas dopaminergic imaging, such as [123I]-FP-CIT SPECT (DaTscan) or fluorodopa (FDOPA)-PET, can be utilized as an adjunct for diagnosis. PSP typically shows midbrain atrophy on structural imaging, whereas FDG-PET may be useful to depict frontal lobe hypometabolism and tau-PET confirms underlying tauopathy. MSA typically demonstrates putaminal or cerebellar atrophy, whereas FDG-PET highlights characteristic nigrostriatal or olivopontocerebellar hypometabolism, respectively. Finally, CBD typically shows asymmetric atrophy in the superior parietal lobules and corpus callosum, whereas FDG and tau-PET demonstrate asymmetric hemispheric and subcortical involvement contralateral to the side of clinical deficits. Additional advanced neuroimaging modalities and techniques described may assist in the diagnostic work-up or are promising areas of emerging research.

Mapping neural correlates of biological motion perception in autistic children using high-density diffuse optical tomography

BackgroundAutism spectrum disorder (ASD), a neurodevelopmental disorder defined by social communication deficits plus repetitive behaviors and restricted interests, currently affects 1/36 children in the general population. Recent advances in functional brain imaging show promise to provide useful biomarkers of ASD diagnostic likelihood, behavioral trait severity, and even response to therapeutic intervention. However, current gold-standard neuroimaging methods (e.g., functional magnetic resonance imaging, fMRI) are limited in naturalistic studies of brain function underlying ASD-associated behaviors due to the constrained imaging environment. Compared to fMRI, high-density diffuse optical tomography (HD-DOT), a non-invasive and minimally constraining optical neuroimaging modality, can overcome these limitations. Herein, we aimed to establish HD-DOT to evaluate brain function in autistic and non-autistic school-age children as they performed a biological motion perception task previously shown to yield results related to both ASD diagnosis and behavioral traits.MethodsWe used HD-DOT to image brain function in 46 ASD school-age participants and 49 non-autistic individuals (NAI) as they viewed dynamic point-light displays of coherent biological and scrambled motion. We assessed group-level cortical brain function with statistical parametric mapping. Additionally, we tested for brain-behavior associations with dimensional metrics of autism traits, as measured with the Social Responsiveness Scale-2, with hierarchical regression models.ResultsWe found that NAI participants presented stronger brain activity contrast (coherent > scrambled) than ASD children in cortical regions related to visual, motor, and social processing. Additionally, regression models revealed multiple cortical regions in autistic participants where brain function is significantly associated with dimensional measures of ASD traits.LimitationsOptical imaging methods are limited in depth sensitivity and so cannot measure brain activity within deep subcortical regions. However, the field of view of this HD-DOT system includes multiple brain regions previously implicated in both task-based and task-free studies on autism.ConclusionsThis study demonstrates that HD-DOT is sensitive to brain function that both differentiates between NAI and ASD groups and correlates with dimensional measures of ASD traits. These findings establish HD-DOT as an effective tool for investigating brain function in autistic and non-autistic children. Moreover, this study established neural correlates related to biological motion perception and its association with dimensional measures of ASD traits.