Somatosensory Network Research Articles

Disrupted resting-sate brain network dynamics in children born extremely preterm.

The developing brain has to adapt to environmental and intrinsic insults after extremely preterm (EPT) birth. Ongoing maturational processes maximize their fit to the environment and this can provide a substrate for neurodevelopmental failures. Resting-state functional magnetic resonance imaging was used to scan 33 children born EPT, at < 27weeks of gestational age, and 26 full-term controls at 10years of age. We studied the capability of a brain area to propagate neural information (intrinsic ignition) and its variability across time (node-metastability). This framework was computed for the dorsal attention network (DAN), frontoparietal, default-mode network (DMN), and the salience, limbic, visual, and somatosensory networks. The EPT group showed reduced intrinsic ignition in the DMN and DAN, compared with the controls, and reduced node-metastability in the DMN, DAN, and salience networks. Intrinsic ignition and node-metastability values correlated with cognitive performance at 12years of age in both groups, but only survived in the term group after adjustment. Preterm birth disturbed the signatures of functional brain organization at rest in 3 core high-order networks: DMN, salience, and DAN. Identifying vulnerable resting-state networks after EPT birth may lead to interventions that aim to rebalance brain function.

Convergence of Brain Transcriptomic and Neuroimaging Patterns in Schizophrenia, Bipolar Disorder, Autism Spectrum Disorder, and Major Depressive Disorder

BackgroundPsychiatric conditions show overlap in their symptoms, genetics, and involvement in brain areas and circuits. Structural alterations in the brain have been found to run in parallel with expression profiles of risk genes at the level of the brain transcriptome, which may point toward a potential transdiagnostic vulnerability of the brain to disease processes. MethodsWe characterized the transcriptomic vulnerability of the cortex across 4 major psychiatric disorders based on collated data from patients with psychiatric disorders (n = 390) and matched control participants (n = 293). We compared normative expression profiles of risk genes linked to schizophrenia, bipolar disorder, autism spectrum disorder, and major depressive disorder to examine cross-disorder overlap in spatial expression profiles across the cortex and their concordance with a magnetic resonance imaging–derived cross-disorder profile of structural brain alterations. ResultsWe showed high expression of psychiatric risk genes converging on multimodal cortical regions of the limbic, ventral attention, and default mode networks versus primary somatosensory networks. Risk genes were found to be enriched among genes associated with the magnetic resonance imaging cross-disorder profile, suggestive of a common link between brain anatomy and the transcriptome in psychiatric conditions. Characterization of this cross-disorder structural alteration map further shows enrichment for gene markers of astrocytes, microglia, and supragranular cortical layers. ConclusionsOur findings suggest that normative expression profiles of disorder risk genes confer a shared and spatially patterned vulnerability of the cortex across multiple psychiatric conditions. Transdiagnostic overlap in transcriptomic risk suggests a common pathway to brain dysfunction across psychiatric disorders.

Aberrant intrinsic functional brain topology in methamphetamine-dependent individuals after six-months of abstinence.

Our aim was to explore the aberrant intrinsic functional topology in methamphetamine-dependent individuals after six months of abstinence using resting-state functional magnetic imaging (rs-fMRI). Eleven methamphetamines (MA) abstainers who have abstained for six months and eleven healthy controls (HC) were recruited for rs-fMRI examination. The graph theory and functional connectivity (FC) analysis were employed to investigate the aberrant intrinsic functional brain topology between the two groups at multiple levels. Compared with the HC group, the characteristic shortest path length ($ {L}_{p} $) showed a significant decrease at the global level, while the global efficiency ($ {E}_{glob} $) and local efficiency ($ {E}_{loc} $) showed an increase considerably. After FDR correction, we found significant group differences in nodal degree and nodal efficiency at the regional level in the ventral attentional network (VAN), dorsal attentional network (DAN), somatosensory network (SMN), visual network (VN) and default mode network (DMN). In addition, the NBS method presented the aberrations in edge-based FC, including frontoparietal network (FPN), subcortical network (SCN), VAN, DAN, SMN, VN and DMN. Moreover, the FC of large-scale functional brain networks revealed a decrease within the VN and SCN and between the networks. These findings suggest that some functions, e.g., visual processing skills, object recognition and memory, may not fully recover after six months of withdrawal. This leads to the possibility of relapse behavior when confronted with MA-related cues, which may contribute to explaining the relapse mechanism. We also provide an imaging basis for revealing the neural mechanism of MA-dependency after six months of abstinence.

Brain effective connectivity analysis facilitates the treatment outcome expectation of sound therapy in patients with tinnitus.

Tinnitus is associated with abnormal functional connectivity of multiple regions of the brain. However, previous analytic methods have disregarded information on the direction of functional connectivity, leading to only a moderate efficacy of pretreatment planning. We hypothesized that the pattern of directional functional connectivity can provide key information on treatment outcomes. Sixty-four participants were enrolled in this study: eighteen patients with tinnitus were categorized into the effective group, twenty-two patients into the ineffective group, and twenty-four healthy participants into the healthy control group. We acquired resting-state functional magnetic resonance images prior to sound therapy and constructed an effective connectivity network of the three groups using an artificial bee colony algorithm and transfer entropy. The key feature of patients with tinnitus was the significantly increased signal output of the sensory network, including the auditory, visual, and somatosensory networks, and parts of the motor network. This provided critical insights into the gain theory of tinnitus development. The altered pattern of functional information orchestration, represented by a higher degree of hypervigilance-driven attention and enhanced multisensory integration, may explain poor clinical outcomes. The activated gating function of the thalamus is one of the key factors for a good prognosis in tinnitus treatment. We developed a novel method for analyzing effective connectivity, facilitating an understanding of the tinnitus mechanism and treatment outcome expectation based on the direction of information flow.

Neural correlates of mindful disengagement from worry.

Uncontrollable worry is a hallmark of generalized anxiety disorder and a transdiagnostic feature of psychopathology. Mindfulness-based strategies show promise for treating worry, but it is unknown which specific strategies are most beneficial, and how these skills might operate on a neurobiological level. We recruited 40 participants with clinically significant worry to undergo functional magnetic resonance imaging while engaging in real-time, idiographic worry and instructed disengagement using two mindfulness strategies (focused attention, acceptance) and one comparison strategy (suppression). Hypotheses were preregistered and partially supported. All disengagement strategies downregulated default mode and upregulated frontoparietal and salience networks, suggesting some shared mechanisms. Focused attention was most effective for promoting disengagement and elicited decreased activity in cognitive control and sensorimotor regions. Successful disengagement was associated with increased activity in rostrolateral prefrontal cortex and functional connectivity between posterior cingulate and primary somatosensory cortex. Findings support the role of cognitive control and somatosensory networks in disengagement from worry and suggest common and distinct mechanisms of disengagement, with focused attention a particularly promising strategy. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Retracted: Real-Time Detection of Body Nutrition in Sports Training Based on Cloud Computing and Somatosensory Network.

[This retracts the article DOI: 10.1155/2022/9911905.].

Sensing and acting: Full circle in fat

White adipose tissue (WAT) communicates with the CNS bidirectionally. In recent studies, aided by three-dimensional imaging, Wang etal. and Frei etal. have provided the organ-wide view of the afferent somatosensory network in the inguinal WAT (iWAT) and have further delineated its critical involvement in energy balance.

A multisensory-feedback tactile glove with dense coverage of sensing arrays for object recognition

Somatosensory networks that provide sophisticated sensory feedback and enable the dexterous manipulation of the human grasp remain difficult to replicate in robots, which is attributed to the grand challenge of densely covering the hand with tactile arrays. Here, a multisensory tactile glove is reported that is capable of object recognition with dense coverage of pressure and temperature sensing arrays. The synergistic effect of the multimodal configuration allows the tactile arrays to perceive contact pressure and thermal conductivity of an object involved in grasping motion, thus enhancing the accuracy via the combination of the mechanical features with thermal properties. By leveraging the multiple scanning technology and wireless transmission system, the tactile glove achieves a recognition accuracy of 94.2% in differentiating 20 types of objects with a modified deep learning algorithm. The large-area sensing arrays with high spatiotemporal resolution and multimodal sensing capabilities, which paves the way for the development of robot grasping tools, human–machine interfacing, and advanced prosthetics.

Six month lower-leg mechanical tactile sensory stimulation alters functional network connectivity associated with improved gait in older adults with peripheral neuropathy - A pilot study.

Foot sole somatosensory impairment associated with peripheral neuropathy (PN) is prevalent and a strong independent risk factor for gait disturbance and falls in older adults. Walkasins, a lower-limb sensory prosthesis, has been shown to improve gait and mobility in people with PN by providing afferent input related to foot sole pressure distributions via lower-leg mechanical tactile stimulation. Given that gait and mobility are regulated by sensorimotor and cognitive brain networks, it is plausible improvements in gait and mobility from wearing the Walkasins may be associated with elicited neuroplastic changes in the brain. As such, this study aimed to examine changes in brain network connectivity after 26 weeks of daily use of the prosthesis among individuals with diagnosed PN and balance problems. In this exploratory investigation, assessments of participant characteristics, Functional Gait Assessment (FGA), and resting-state functional magnetic resonance imaging were completed at study baseline and 26 weeks follow-up. We found that among those who have completed the study (N = 8; mean age 73.7 years) we observed a five-point improvement in FGA performance as well as significant changes in network connectivity over the 26 weeks that were correlated with improved FGA performance. Specifically, greater improvement in FGA score over 26 weeks was associated with increased connectivity within the Default Mode Network (DMN; p < 0.01), the Somatosensory Network (SMN; p < 0.01), and the Frontoparietal Network (FPN; p < 0.01). FGA improvement was also correlated with increased connectivity between the DMN and the FPN (p < 0.01), and decreased connectivity between the SMN and both the FPN (p < 0.01) and cerebellum (p < 0.01). These findings suggest that 26 weeks of daily use of the Walkasins device may provide beneficial neural modulatory changes in brain network connectivity via the sensory replacement stimulation that are relevant to gait improvements among older adults with PN.

Objective Sleep Quality and the Underlying Functional Neural Correlates Among Older Adults with Possible Mild Cognitive Impairment.

Poor sleep quality is common among older individuals with mild cognitive impairment (MCI) and may be a consequence of functional alterations in the brain; yet few studies have investigated the underlying neural correlates of actigraphy-measured sleep quality in this cohort. The objective of this study was to examine the relationship between brain networks and sleep quality measured by actigraphy. In this cross-sectional analysis, sleep efficiency and sleep fragmentation were estimated using Motionwatch8 (MW8) over a period of 14 days in 36 community-dwelling older adults with possible MCI aged 65-85 years. All 36 participants underwent resting-state functional magnetic resonance imaging (fMRI) scanning. Independent associations between network connectivity and MW8 measures of sleep quality were determined using general linear modeling via FSL. Networks examined included the somatosensory network (SMN), frontoparietal network (FPN), and default mode network (DMN). Across the 36 participants (mean age 71.8 years; SD = 5.2 years), mean Montreal Cognitive Assessment score was 22.5 (SD = 2.7) and Mini-Mental State Examination score was 28.3 (SD = 1.5). Mean sleep efficiency and fragmentation index was 80.1% (SD = 10.0) and 31.8 (SD = 10.4) respectively. Higher sleep fragmentation was significantly correlated with increased connectivity between the SMN and insula, the SMN and posterior cingulate, as well as FPN and primary motor area (FDR-corrected, p &lt; 0.004). Functional connectivity between brain regions involved in attentional and somatosensory processes may be associated with disrupted sleep in older adults with MCI.

Aberrant intrinsic functional brain networks in patients with functional constipation.

Patients with functional constipation (FCon) often suffer from mental and psychological problems. To explore the possible neurological interaction, we used resting-state functional magnetic imaging (RS-fMRI) to compare the alterations in intrinsic brain functional networks at multiple levels between patients with FCon and healthy controls (HC). Twenty-eight patients with FCon and twenty-nine HC were recruited for a series of examinations and RS-fMRI. Both graph theory analysis and functional connectivity (FC) analysis were used to investigate brain functional alterations between the two groups. Correlation analyses were performed among neuropsychological scores, clinical indexes, and neuroimaging data. Compared with the HC, the assortativity showed significantly increased in global level in patients with FCon. In regional level, we found obviously increased nodal degree and nodal efficiency in somatosensory network (SMN), decreased nodal degree, and increased nodal efficiency in default mode network (DMN) in the FCon group. Furthermore, FC analysis demonstrated several functional alterations within and between the networks, particularly including the SMN and visual network (VN) in sub-network and large-scale network analysis. Moreover, correlation analysis indicated that nodal metrics and aberrant FC among functional brain networks were associated with emotion and scores of constipation in patients with FCon. All these findings reflect the differences in intrinsic brain functional networks between FCon and HC. Our study highlighted SMN, DMN, and VN as critical network and may be involved in the neurophysiology of FCon, which may contribute to improve personalized treatment in patients with FCon.

Brain state transition analysis using ultra-fast fMRI differentiates MCI from cognitively normal controls.

PurposeConventional resting-state fMRI studies indicate that many cortical and subcortical regions have altered function in Alzheimer’s disease (AD) but the nature of this alteration has remained unclear. Ultrafast fMRIs with sub-second acquisition times have the potential to improve signal contrast and enable advanced analyses to understand temporal interactions between brain regions as opposed to spatial interactions. In this work, we leverage such fast fMRI acquisitions from Alzheimer’s disease Neuroimaging Initiative to understand temporal differences in the interactions between resting-state networks in 55 older adults with mild cognitive impairment (MCI) and 50 cognitively normal healthy controls.MethodsWe used a sliding window approach followed by k-means clustering. At each window, we computed connectivity i.e., correlations within and across the regions of the default mode, salience, dorsal attention, and frontoparietal network. Visual and somatosensory networks were excluded due to their lack of association with AD. Using the Davies–Bouldin index, we identified clusters of windows with distinct connectivity patterns, also referred to as brain states. The fMRI time courses were converted into time courses depicting brain state transition. From these state time course, we calculated the dwell time for each state i.e., how long a participant spent in each state. We determined how likely a participant transitioned between brain states. Both metrics were compared between MCI participants and controls using a false discovery rate correction of multiple comparisons at a threshold of. 0.05.ResultsWe identified 8 distinct brain states representing connectivity within and between the resting state networks. We identified three transitions that were different between controls and MCI, all involving transitions in connectivity between frontoparietal, dorsal attention, and default mode networks (p<0.04).ConclusionWe show that ultra-fast fMRI paired with dynamic functional connectivity analysis allows us to capture temporal transitions between brain states. Most changes were associated with transitions between the frontoparietal and dorsal attention networks connectivity and their interaction with the default mode network. Although future work needs to validate these findings, the brain networks identified in our work are known to interact with each other and play an important role in cognitive function and memory impairment in AD.

Ayu-Characterization of healthy aging from neuroimaging data with deep learning and rsfMRI.

Estimating brain age and establishing functional biomarkers that are prescient of cognitive declines resulting from aging and different neurological diseases are still open research problems. Functional measures such as functional connectivity are gaining interest as potentially more subtle markers of neurodegeneration. However, brain functions are also affected by “normal” brain aging. More information is needed on how functional connectivity relates to aging, particularly in the absence of neurodegenerative disorders. Resting-state fMRI enables us to investigate functional brain networks and can potentially help us understand the processes of development as well as aging in terms of how functional connectivity (FC) matures during the early years and declines during the late years. We propose models for estimation of the chronological age of a healthy person from the resting state brain activation (rsfMRI). In this work, we utilized a dataset (N = 638, age-range 20–88) comprising rsfMRI images from the Cambridge Centre for Aging and Neuroscience (Cam-CAN) repository of a healthy population. We propose an age prediction pipeline Ayu which consists of data preprocessing, feature selection, and an attention-based model for deep learning architecture for brain age assessment. We extracted features from the static functional connectivity (sFC) to predict the subject's age and classified them into different age groups (young, middle, middle, and old ages). To the best of our knowledge, a classification accuracy of 72.619 % and a mean absolute error of 6.797, and an r2 of 0.754 reported by our Ayu pipeline establish competitive benchmark results as compared to the state-of-the-art-approach. Furthermore, it is vital to identify how different functional regions of the brain are correlated. We also analyzed how functional regions contribute differently across ages by applying attention-based networks and integrated gradients. We obtained well-known resting-state networks using the attention model, which maps to within the default mode network, visual network, ventral attention network, limbic network, frontoparietal network, and somatosensory network connected to aging. Our analysis of fMRI data in healthy elderly Age groups revealed that dynamic FC tends to slow down and becomes less complex and more random with increasing age.

Metabolic connectivity of resting-state networks in alpha synucleinopathies, from prodromal to dementia phase.

Previous evidence suggests that the derangement of large-scale brain networks reflects structural, molecular, and functional mechanisms underlying neurodegenerative diseases. Although the alterations of multiple large-scale brain networks in Parkinson’s disease (PD) and Dementia with Lewy Bodies (DLB) are reported, a comprehensive study on connectivity reconfiguration starting from the preclinical phase is still lacking. We aimed to investigate shared and disease-specific changes in the large-scale networks across the Lewy Bodies (LB) disorders spectrum using a brain metabolic connectivity approach. We included 30 patients with isolated REM sleep behavior disorder (iRBD), 28 with stable PD, 30 with DLB, and 30 healthy controls for comparison. We applied seed-based interregional correlation analyses (IRCA) to evaluate the metabolic connectivity in the large-scale resting-state networks, as assessed by [18F]FDG-PET, in each clinical group compared to controls. We assessed metabolic connectivity changes by applying the IRCA and specific connectivity metrics, such as the weighted and unweighted Dice similarity coefficients (DC), for the topographical similarities. All the investigated large-scale brain resting-state networks showed metabolic connectivity alterations, supporting the widespread involvement of brain connectivity within the alpha-synuclein spectrum. Connectivity alterations were already evident in iRBD, severely affecting the posterior default mode, attentive and limbic networks. Strong similarities emerged in iRBD and DLB that showed comparable connectivity alterations in most large-scale networks, particularly in the posterior default mode and attentive networks. Contrarily, PD showed the main connectivity alterations limited to motor and somatosensory networks. The present findings reveal that metabolic connectivity alterations in the large-scale networks are already present in the early iRBD phase, resembling the DLB metabolic connectivity changes. This suggests and confirms iRBD as a risk condition for progression to the severe LB disease phenotype. Of note, the neurobiology of stable PD supports its more benign phenotype.

Acetyl-dl-leucine in cerebellar ataxia ([18F]-FDG-PET study): how does a cerebellar disorder influence cortical sensorimotor networks?

ObjectiveThe aim of the study was to deepen our insights into central compensatory processes of brain networks in patients with cerebellar ataxia (CA) before and with treatment with acetyl-dl-leucine (AL) by means of resting-state [18F]-FDG-PET brain imaging.MethodsRetrospective analyses of [18F]-FDG-PET data in 22 patients with CA (with vestibular and ocular motor disturbances) of different etiologies who were scanned before (PET A) and on AL treatment (PET B). Group subtraction analyses, e.g., for responders and non-responders, comparisons with healthy controls and correlation analyses of regional cerebral glucose metabolism (rCGM) with symptom duration, ataxia (SARA) and quality of life (QoL) scores were calculated.ResultsPrior to treatment rCGM was consistently downregulated at the cerebellar level and increased in multisensory cortical areas, e.g., somatosensory, primary and secondary visual (including V5, precuneus), secondary vestibular (temporal gyrus, anterior insula), and premotor/supplementary motor areas. With AL (PET B vs. A) cerebellar hypometabolism was deepened and sensorimotor hypermetabolism increased only in responders with clinical benefit, but not for the non-responders and the whole CA group. A positive correlation of ataxia improvement with rCGM was found in visual and vestibular cortices, a negative correlation in cerebellar and brainstem areas. QoL showed a positive correlation with rCGM in the cerebellum and symptom duration in premotor and somatosensory areas.ConclusionsCentral compensatory processes in CA mainly involve multisensory visual, vestibular, and somatosensory networks as well as premotor/primary motor areas at the cortical level. The enhanced divergence of cortical sensorimotor up- and cerebellar downregulation with AL in responders could reflect amplification of inhibitory cerebellar mechanisms.

Piezoionic mechanoreceptors: Force-induced current generation in hydrogels.

The human somatosensory network relies on ionic currents to sense, transmit, and process tactile information. We investigate hydrogels that similarly transduce pressure into ionic currents, forming a piezoionic skin. As in rapid- and slow-adapting mechanoreceptors, piezoionic currents can vary widely in duration, from milliseconds to hundreds of seconds. These currents are shown to elicit direct neuromodulation and muscle excitation, suggesting a path toward bionic sensory interfaces. The signal magnitude and duration depend on cationic and anionic mobility differences. Patterned hydrogel films with gradients of fixed charge provide voltage offsets akin to cell potentials. The combined effects enable the creation of self-powered and ultrasoft piezoionic mechanoreceptors that generate a charge density four to six orders of magnitude higher than those of triboelectric and piezoelectric devices.

Real-Time Detection of Body Nutrition in Sports Training Based on Cloud Computing and Somatosensory Network.

With the progress of society and the improvement of living standards, sports training has gradually become an area of increasing concern for society and individuals. To more comprehensively grasp the physical function, body shape, and physical fitness of athletes, many researchers have conducted extensive research on the real-time detection of human body nutrition. This study is mainly supported by cloud computing and somatosensory network technology, and the real-time detection of human body composition in sports training is the main research object. In the experiment, two methods of human body composition detection were tested: the BIA method and the body composition analysis method based on the electrochemical sensor of body sweat. It designed a human nutrient composition detection system based on the BIA method. The error rate of the system is relatively small, which is basically maintained at about 2%. It uses a body surface sweat electrochemical sensor to detect changes in glucose concentration during human exercise. After exercising for a period of time, the test subject's sweat glucose concentration remained around 0.5 mM.

Altered Structural and Functional Connectivity of Salience Network in Patients with Classic Trigeminal Neuralgia

Classic trigeminal neuralgia (CTN) is a neuropathic pain disorder displaying spontaneously stabbing or electric shock-like paroxysms in the face. Previous research suggests structural and functional abnormalities in brain regions related to sensory and cognitive-affective dimensions of pain contribute to the pathophysiology of CTN. However, few studies to date have investigated how changes in whole-brain functional networks and white matter connectivity are related to CTN. We performed an independent component analysis to examine abnormalities in resting state functional connectivity of large-scale networks in 48 patients with CTN compared to 46 matched healthy participants. Then, diffusion tensor tractography was performed to test whether these alterations of functional connectivity in intrinsic networks were associated with impairment of the white matter tracts connecting them. Distinct patterns of functional connectivity were detected within default mode network, somatosensory network, and salience network (SN) in the CTN group when compared with healthy controls. Furthermore, abnormality of SN was negatively correlated with pain severity. In support of aberrant functional connectivity within SN, structural disintegration was observed in the white matter tract from left anterior insula (aIns) to left anterior cingulate cortex (ACC) in CTN. These results suggest that altered structural and functional connectivity between aIns and ACC may underpin the aberrant SN in patients with CTN and provide an alternative target for clinical interventions. PerspectiveThis article presents distinctive abnormalities of functional and structural connectivity from aIns to ACC in the patients with CTN, which is associated with pain ratings. This measure could potentially provide an alternative target for clinicians to alleviate this type of intermittent and refractory pain.

Distinct Functional Cortico-Striato-Thalamo-Cerebellar Networks in Genetic Generalized and Focal Epilepsies with Generalized Tonic-Clonic Seizures.

This study aimed to delineate cortico-striato-thalamo-cerebellar network profiles based on static and dynamic connectivity analysis in genetic generalized and focal epilepsies with generalized tonic-clonic seizures, and to evaluate its potential for distinguishing these two epilepsy syndromes. A total of 342 individuals participated in the study (114 patients with genetic generalized epilepsy with generalized tonic-clonic seizures (GE-GTCS), and 114 age- and sex-matched patients with focal epilepsy with focal to bilateral tonic-clonic seizure (FE-FBTS), 114 healthy controls). Resting-state fMRI data were examined through static and dynamic functional connectivity (dFC) analyses, constructing cortico-striato-thalamo-cerebellar networks. Network patterns were compared between groups, and were correlated to epilepsy duration. A pattern-learning algorithm was applied to network features for classifying both epilepsy syndromes. FE-FBTS and GE-GTCS both presented with altered functional connectivity in subregions of the motor/premotor and somatosensory networks. Among these two groups, the connectivity within the cerebellum increased in the static, while the dFC variability decreased; conversely, the connectivity of the thalamus decreased in FE-FBTS and increased in GE-GTCS in the static state. Connectivity differences between patient groups were mainly located in the thalamus and cerebellum, and correlated with epilepsy duration. Support vector machine (SVM) classification had accuracies of 66.67%, 68.42%, and 77.19% when using static, dynamic, and combined approaches to categorize GE-GTCS and FE-GTCS. Network features with high discriminative ability predominated in the thalamic and cerebellar connectivities. The network embedding of the thalamus and cerebellum likely plays an important differential role in GE-GTCS and FE-FBTS, and could serve as an imaging biomarker for differential diagnosis.

Differential extrinsic brain network connectivity and social cognitive task-specific demands in Autism Spectrum Disorder (ASD)

Few studies have used task-based functional connectivity (FC) magnetic resonance imaging to examine emotion-processing during the critical neurodevelopmental period of adolescence in Autism Spectrum Disorders (ASDs). Moreover, task designs with pervasive confounds (e.g., lack of appropriate controls) persist because they activate neural circuits of interest reliably. As an alternative approach to “subtracting” activity from putative control conditions, we propose examining FC across an entire task run. By pivoting our analysis and interpretation of existing paradigms, we may better understand neural response to non-focal instances of socially-relevant stimuli that approximate real-world experiences more closely. Hence, using two well-established affective tasks (face-viewing, face-matching) with diverging social-cognitive demands, we investigated extrinsic FC from amygdala (AMG) and fusiform gyrus (FG) seeds in typically-developing (TD; N = 17) and ASD (N = 17) male adolescents (10–18 yo) and clinical correlations (Social Communication Questionnaire; SCQ) of group FC differences. Participant data (4TD, 6ASD) with excessive head-motion were excluded from final analysis. Direct between-group comparisons revealed significant differences between groups for neural response but not task performance (accuracy, reaction time). During face-viewing, we found greater FC from AMG and FG seeds for ASD participants (ASD > TD) in regions involved in the Default Mode and Fronto-Parietal Task Control Networks. During face-matching, we found greater FC from AMG and FG seeds for TD participants (TD > ASD), in regions associated with the Salience, Dorsal Attention, and Somatosensory Networks. SCQ scores correlated positively with regions with group differences on the face-viewing task and negatively with regions identified for the face-matching task. Task-dependent group differences in FC despite comparable behavioral performance suggest that high-functioning ASD may wield compensatory strategies; clinically-correlated FC patterns may associate with differential task-demands, ecological validity, and context-dependent processing. Employing this novel approach may further the development of targeted therapeutic interventions informed by individual differences in the highly heterogeneous ASD population.